Single Extraction Research Articles

Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources.

. http://flame.pavlopouloslab.info. https://github.com/PavlopoulosLab/Flame. https://hub.docker.com/r/pavlopouloslab/flame. Supplementary data are available at Bioinformatics online.

Combined Antidiabetic and Antidyslipidemic Activity of Ageratum conyzoides and Gynura procumbens in Alloxan-induced Diabetic Rats

Unhealthy lifestyle habits greatly increase the risks of various degenerative illnesses, for example, diabetes mellitus as well as dyslipidemia. Recent studies have shown that the pharmaceutical drugs used for the treatment of these conditions have undesirable side effects. This indicates that it is necessary to find more effective and safe alternative treatment options, particularly in medicinal plants, such as Ageratum conyzoides and Gynura procumbens. Therefore, this research examined the combined antidiabetic and antidyslipidemic activity of Ageratum conyzoides and Gynura procumbens extracts in alloxan-induced diabetic rats. The insulin sensitivity of the test animals was assessed at the beginning of the experiment using the oral glucose tolerance test (OGTT) through the administration of 3 g/kgBW glucose. Pancreatic destruction was induced with the intraperitoneal injection of a single dose of 150 mg/kgBW alloxan, and the rats were treated with ethanol extract for 14 days. High-density lipoprotein cholesterol (HDL-C), Triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) were measured using the enzymatic method. The results showed that the single extracts and their combination exhibited high antidiabetic and antidyslipidemic activity. This was indicated by a substantial reduction in fasting blood sugar, TG, TC, and LDL-C (p<0.05), and an insignificant increase in HDL-C. The activity of the combined extract was similar to the single extracts, but it was not better in decreasing TG levels. Based on these findings, 95% ethanol extract of Ageratum conyzoides, Gynura procumbens, and their combination exhibited high antidiabetic and antidyslipidemic activity in alloxan-induced rats.

Highly Efficient and Simultaneous Analysis of Three Common Fluorotelomer Alcohols in Vegetables and Soils.

Fluorotelomer alcohols (FTOHs), as precursors of perfluoroalkyl carboxylic acids, are difficult to analyze due to their high volatility and matrix interference. A method based on single-factor experiments and response surface methodology design was developed for simultaneous analysis of three common FTOHs in vegetables and soils, using single extraction, dispersive solid phase extraction cleanup, and gas chromatography-mass spectrometry in negative chemical ionization. The method improved the extraction efficiency up to ∼40 folds and showed a commendable linearity range (1-100 ng/mL, R2 > 0.991), low limit of detection (0.025-0.897 ng/g, dry weight (dw)), and high accuracy and precision (83 ± 7.2-117 ± 6.0% recoveries at 2-20 ng/g fortification levels). It was successfully applied to determine the FTOHs in real vegetables and soils, demonstrating its feasibility for routine analysis. Concentrations of the FTOHs ranged from 3.5 to 37.9 ng/g (dw) and from 6.5 to 141.0 ng/g (dw), respectively, in the vegetables and soils collected nearby fluorochemical factories, which warrants further investigations on FTOH pollution and food safety concerns for which the developed method will be useful.

Exosome Analysis in Prostate Cancer: How They Can Improve Biomarkers' Performance.

Exosomes are extracellular nanovesicles (EV), that is, carriers of different biomolecules such as lipids, proteins, nucleic acids. Their composition and the fact that their release dramatically increases in cases of tumorigenesis open up different scenarios on their possible application to research into new biomarkers. The first purpose of the present review was to specifically analyze and compare different methodologies available for the use of exosomes in prostate cancer (PC). The most widely applied methodologies include ultracentrifugation techniques, size-based techniques, immunoaffinity capture-based techniques (mainly ELISA), and precipitation. To optimize the acquisition of exosomes from the reference sample, more techniques can be applied in sequence for a single extraction, thereby determining an increase in labor time and costs. The second purpose was to describe clinical results obtained with the analysis of PSA-expressing exosomes in PC; this provides an incredibly accurate method of discriminating between healthy patients and those with prostate disease. Specifically, the IC-ELISA alone method achieved 98.57% sensitivity and 80.28% specificity in discriminating prostate cancer (PC) from benign prostatic hyperplasia (BPH). An immunocapture-based ELISA assay was performed to quantify and characterize carbonic anhydrase (CA) IX expression in exosomes. The results revealed that CA IX positive exosomes were 25-fold higher in plasma samples from PC patients than in those from healthy controls. The analysis of PC-linked exosomes represents a promising diagnostic model that can effectively distinguish patients with PC from those with non-malignant prostatic disease. However, the use of exosome analysis in clinical practice is currently limited by several issues, including a lack of standardization in the analytical process and high costs, which are still too high for large-scale use.

Green approach for polyphenol extraction from waste tea biomass: Single and hybrid application of conventional and ultrasound-assisted extraction

Based on a green approach, the potential use of waste tea biomass (fiber and second sieving) with rich polyphenol content was investigated as an alternative source of polyphenol to achieve an economic added value. In addition, this study demonstrated a comparative approach to explore the most sustainable green extraction method by the assessment of single ultrasound-assisted extraction (UAE) at various frequencies (20, 35, and 200 kHz) and the hybrid operations of ultrasound (US) and thermal extraction (50 °C and 80 °C). As a result, it has been determined that waste tea biomass, with a polyphenol extraction rate of more than 80%, provides a higher recovery capacity than tea leaf (the highest polyphenol recovery rate of 72.5%) in almost all single operations. Among the single UAE, 20 kHz was expressed as the method succeeding with high recovery rates (84%) within 30 min for fiber waste. In contrast, the hybrid operation consisting of 20 kHz US (20 min) with heating at 80 °C (10 min) yielded the highest extraction efficiency with 92% in the same time interval more economically for second sieving waste tea biomass. Therefore, this study has shown that it is possible to utilize UAE alone or in combination with heat extraction from tea waste for environmentally friendly, rapid, and effective polyphenol extraction.

Methodological Aspects of Green Extraction of Usnic Acid Using Natural Deep Eutectic Solvents.

Usnic acid (UA) is a compound with multiple biological activities that make it useful in various industries, e.g., pharmaceutical, cosmetic, dentistry, and agricultural sectors. Lichens are the primary source of UA, which is primarily extracted using acetone. This study aimed to investigate the solubility of UA in numerous natural deep eutectic solvents (NADESs) and use a mixture of thymol and camphor as a NADES in the optimization of the UA extraction process with the design of experiments method. For numerical optimization, the following parameters were employed in the experiment to confirm the model: a camphor-to-thymol ratio of 0.3, a liquid-to-solid ratio of 60, and a time of 30 min. The obtained experimental results aligned well with the predicted values, with the mean experimental value falling within the confidence interval, exhibiting deviations between 11.93 and 14.96. By employing this model, we were able to optimize the extraction procedure, facilitating the isolation of approximately 91% of the total UA content through a single extraction, whereas a single acetone extraction yielded only 78.4% of UA.

Motor intent recognition of multi-feature fusion EEG signals by UMAP algorithm.

The key to the analysis of electroencephalogram (EEG) signals lies in the extraction of effective features from the raw EEG signals, which can then be utilized to augment the classification accuracy of motor imagery (MI) applications in brain-computer interface (BCI). It can be argued that the utilization of features from multiple domains can be a more effective approach to feature extraction for MI pattern classification, as it can provide a more comprehensive set of information that the traditional single feature extraction method may not be able to capture. In this paper, a multi-feature fusion algorithm based on uniform manifold approximate and projection (UMAP) is proposed for motor imagery EEG signals. The brain functional network and common spatial pattern (CSP) are initially extracted as features. Subsequently, UMAP is utilized to fuse the extracted multi-domain features to generate low-dimensional features with improved discriminative capability. Finally, the k-nearest neighbor (KNN) classifier is applied in a lower dimensional space. The proposed method is evaluated using left-right hand EEG signals, and achieved the average accuracy of over 92%. The results indicate that, compared with single-domain-based feature extraction methods, multi-feature fusion EEG signal classification based on the UMAP algorithm yields superior classification and visualization performance. Feature extraction and fusion based on UMAP algorithm of left-right hand motor imagery.

Lithium extraction from aqueous medium using hydrophobic deep eutectic solvents

Because lithium resources are becoming increasingly scarce, their recovery is an urgent problem that needs to be solved. In this paper, an environmentally friendly process for the recovery of lithium ions from water resources by liquid-liquid extraction (LLE) is proposed. Menthol (DLM) was used as a hydrogen bond acceptor (HBA), and octanoic acid, decanoic acid, and lauric acid (LA) were used as hydrogen bond donors (HBDs) to form nine hydrophobic deep eutectic solvents (HDESs). The HDESs synthesized using DLM and LA had the best effect and thus were selected for the lithium extraction experiment. The single extraction efficiency of lithium ions was 80.69 %, with optimized rotational speed, equilibrium time, pH, molar ratio of organic phase to inorganic phase (O/L), and initial concentration of different lithium complexes of 660 rpm, 30 min, 12, 3:1, and 0.5 g/L, respectively. In addition, the regeneration cycle experiment of the HDES was further investigated. The possible extraction mechanism was demonstrated by Fourier-transform infrared spectroscopy and slope method. The separation factor (β) of Li/Na and Li/K obtained from the experimental results was 18.72 and 84.25, showing that 2DLM/LA can selectively extract lithium ions from systems containing Li, Na and K. This will be beneficial to design potential green HDESs for recovering lithium resources from aqueous solutions.

An improved intelligent water drops feature selection for finger vein recognition

In recent years, finger vein recognition has gained a lot of attention and been considered as a possible biometric feature. Various feature selection techniques were investigated for intrinsic finger vein recognition on single feature extraction, but their computational cost remains undesirable. However, the retrieved features from the finger vein pattern are massive and include a lot of redundancy. By using fusion methods on feature extraction approaches involving weighted averages, the error rate is minimized to produce an ideal weight. In this research, a novel combinational model of intelligent water droplets is proposed along with hybrid PCA LDA feature extraction for improved finger vein pattern recognition. Initially, finger vein images are pre-processed to remove noise and improve image quality. For feature extraction, Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) are employed to identify the most relevant characteristics. The PCA and LDA algorithms combine features to accomplish feature fusion. A global best selection method using intelligent water drops (GBS-IWD) is employed to find the ideal characteristics for vein recognition. The K Nearest Neighbour Classifier was used to recognize finger veins based on the selected optimum features. Based on empirical data, the proposed method decreases the equal error rate by 0.13% in comparison to existing CNN, 3DFM, and JAFVNet techniques. The overall accuracy of the proposed GBSPSO-KNN is 3.89% and 0.85% better than FFF and GWO, whereas, the proposed GBSIWD-KNN is 4.37% and 1.35% better than FFF and GWO respectively.

Delayed and Immediately Loaded Implants Placed in Fresh Extraction Socket - A Clinical and Radiological Study.

To present the outcomes of immediately loaded single implants placed in the anterior region compared to conventional protocol. 10 patients requiring single anterior tooth extraction was randomised either into immediate or delayed loaded group. Implants were positioned immediately after extraction and prosthesis was providing for the immediately loaded group. The cases were followed up for a period of 9 months at regular 3-month interval after the placement of definitive crowns. The parameters taken were probing depth, pain score and crestal marginal bone loss. A total of 10 AD1N-Touareg Spiral tapered Implant with spiral tap, 5 immediately loaded and 5 delayed loaded were evaluated. For immediate loaded cases, there was 0.93 ± 0.04 mm bone lost after 3 months, 1.26 ± 0.21 mm after 6 months and 1.72 ± 0.13 mm after 9 months. For delayed loading, there was 0.90 ± 0.02 mm bone lost after 3 months, 1.26 ± 0.15 mm after 6 months and 1.80 ± 0.07 mm after 9 months. Pain and probing depth showed gradual reduction in both the groups during the course of treatment. In the present study, the success rate and radiographic results of immediate loading of implants in freshly extracted sockets in the anterior region where comparable to those obtained from delayed groups.

One-Dimensional W-NETR for Non-Invasive Single Channel Fetal ECG Extraction.

Fetal cardiac monitoring is very helpful in the early detection of the potential risk of fetal cardiac abnormalities, which enables prompt preventative care and ensures safe births. As a result, it is crucial to regularly check on the embryonic heart. Methods of non-invasively fetal ECG extraction from maternal abdominal ECG signal are thoroughly discussed. Although fetal signals are generally obscured by maternal ECG signals and noise, extracting a clean fetal ECG is a significant difficulty. The majority of techniques for fetal ECG extraction include many extraction steps. We describe a unique method for splitting a single-channel maternal abdominal ECG into maternal and fetus ECG employing two parallel U-nets with transformer encoding, which we refer to as W-NEt TRansformers (W-NETR). Due to its enhanced capacity to simulate remote interactions and capture global context, the suggested pipeline utilizes the self-attention mechanism of the transformer. We tested the proposed pipeline on synthetic and real datasets and outperformed the current state-of-the-art deep learning models. The proposed model achieved the best results on both datasets for QRS detection precision, recall, and F1 scores. More specifically, it achieved F1 score of 99.88% and 98.9% on the real ADFECGDB and PCDB datasets, respectively. These encouraging results highlight the suggested W-NETR's effectiveness in precisely extracting the fetal ECG, which was achieved with high SSIM and PSNR values in the results. This provides the bed set for long-term maternal and fetal monitoring via portable devices as the proposed system performs real-time execution.

ETucker: a constrained tensor decomposition for single trial ERP extraction

Objective. In this paper, we propose a new tensor decomposition to extract event-related potentials (ERP) by adding a physiologically meaningful constraint to the Tucker decomposition. Approach. We analyze the performance of the proposed model and compare it with Tucker decomposition by synthesizing a dataset. The simulated dataset is generated using a 12th-order autoregressive model in combination with independent component analysis (ICA) on real no-task electroencephalogram (EEG) recordings. The dataset is manipulated to contain the P300 ERP component and to cover different SNR conditions, ranging from 0 to −30 dB, to simulate the presence of the P300 component in extremely noisy recordings. Furthermore, in order to assess the practicality of the proposed methodology in real-world scenarios, we utilized the brain-computer interface (BCI) competition III-dataset II. Main results. Our primary results demonstrate the superior performance of our approach compared to conventional methods commonly employed for single-trial estimation. Additionally, our method outperformed both Tucker decomposition and non-negative Tucker decomposition in the synthesized dataset. Furthermore, the results obtained from real-world data exhibited meaningful performance and provided insightful interpretations for the extracted P300 component. Significance. The findings suggest that the proposed decomposition is eminently capable of extracting the target P300 component’s waveform, including latency and amplitude as well as its spatial location, using single-trial EEG recordings.

First Report of Powdery Mildew Caused by Podosphaera macularis on Humulus scandens in China.

Humulus scandens, (Lour.) Merr., is a climbing herb which are used as traditional Chinese medicine, a raw material for papermaking, making soap, and replacing hops H. lupulus. This herb is distributed in many provinces of China, including Sichuan province. During March and June 2022, powdery mildew was found on leaves of H. scandens in the modern agricultural high-tech demonstration garden of Sichuan Academy of Agricultural Science, Chengdu, Sichuan Province, China. Abundant white or grayish powdery colonies could be seen on the surface of leaves, and 30%-100% of leaf areas were affected. Some of the infected leaves were either chlorotic or senescent. About 90% of the observed plants showed powdery mildew symptoms. Conidiophores (n = 25) were 74.0 to 160.1 μm × 8.7 to 12.7 μm (on average 120.9 × 10.4 μm) and composed of cylindrical foot cells (length 31.9-72.9 μm, average 50.1 µm) and conidia (mostly 10 conidia) in chains. Barrel-shaped conidia with fibrosin bodies (n = 30) were 12.8 to 21.0 μm × 7.9 to 15 μm, on average 16.7 × 11.3 μm, with a length/width ratio of 1.5. Chasmothecia were not found. Based on these morphologic characteristics, the pathogen was initially identified as Podosphaera macularis (Braun and Cook 2012; Mahaffee et al. 2009). To confirm the identification, two isolates (PDLC0315 and PDLC0412) of P. macularis mycelia and conidia were collected, and mycelia and conidia were combined for a single DNA extraction from each isolate. With the total genomic DNA, the sequences of the internal transcribed spacer (ITS), 5.8S rRNA, the 18S and 28S large subunit ribosomal DNA (LSU) (Bradshaw and Tobin 2020; White et al. 1990), were bi-directionally sequenced and deposited in GenBank (ON862625.1 and ON862630.1). The ON862625.1 and ON862630.1 showed 100% similarity with sequences of P. macularis isolate CT1 (MH687414.1). Phylogenetic analyses based on the combined ITS and 28S rDNA sequences indicated that the two specimens, PDLC0315 and PDLC0412 formed a monophyletic clade together with sequences retrieved from Podosphaera macularis CT1 and Head quarter 31 (KX842348.1). The pathogenicity test with the fungus was confirmed by gently pressing the infected leaves onto three healthy wild plants from the same geographical location. Three uninoculated wild plants served as controls. Six inoculated and non-inoculated plants were placed in different growth chambers with a 16-h photoperiod at 22±2°C and 70% of relative humidity. After 10 to 14 days, powdery mildew colonies developed on inoculated plants. Non-inoculated control plants did not show any symptoms. The fungus on inoculated leaves was morphologically identical to that first observed in the garden. As far as we know, this study is the first report of powdery mildew disease in Humulus scandens caused by Podosphaera macularis in China. Rapid expansion and wild distribution of H. scandens could lead to increased powdery mildew risk in outdoor cultivation. Due to the invasive potential of the powdery mildew fungi, this record is important in the context of the range extension of Podosphaera macularis.

Combination of Ketepeng Cina (Cassia alata Linn) And Lidah Buaya Buaya (Aloe vera) As Antibacterial Agent Against Staphylococcus aureus And Pseudomonas aeruginosa

Infection is one of the biggest health problems in Indonesia. Infection can be caused by pathogenic microorganisms such as Staphylococcus aureus and Pseudomonas aeruginosa. Treatment with antibiotics is the primary choice to manage infections caused by bacteria. Previous research showed that many plants have been tested for their antibacterial activity such as ketepeng cina (Cassia alata) and lidah buaya (Aloe vera). Cassia alata and Aloe vera alkaloids, flavonoids, anthraquinones, saponins, tannins contain similar active compounds as antimicrobials. Based on this, the purpose of this study was to determine the antibacterial activity of the combination of methanol extracts of C. alata and A. vera against S. aureus and P. aeruginosa bacteria. The antibacterial activity test was carried out using the Kirby-Bauer method. The single extract of C. alata and A. vera had the greatest antibacterial activity against S. aureus at a concentration of 10 mg/disk with zones of inhibition of 29.5±0.7 mm and 21.5±1.4 mm, respectively. The single antibacterial activity test of both extracts against P. aeruginosa bacteria showed no inhibition zone. The test results showed that the combination of 10 mg/disk C. alata methanol extract and 10 mg/disk Aloe vera could inhibit the growth of S. aureus with an average inhibition zone of 30.75±1.7 mm. However, the lower ratio of both extracts resulted in a likely antagonistic effect against S. aureus. Phytochemical screening tests showed that these two extracts had the same compounds, namely phenolics, flavonoids, anthraquinones. Contact bioautography showed that mainly phenolics, flavonoids, and anthraquinones were responsible in the antibacterial properties.

Rapid detection of pyrethroid resistance‐associated mutation L1014F in the field based on loop‐mediated isothermal amplification technique

AbstractMyzus persicae (Sulzer) is a globally distributed crop pest with a wide host range. The L1014F mutation in the voltage‐gated sodium channel has been implicated in mediating resistance to pyrethroid insecticides in M. persicae. Control failures in crop production are frequently attributed to inaccurate or delayed detection of M. persicae resistance. Thus, simple and effective tools for detecting resistant M. persicae are required to develop rapid and flexible pest control strategies. The resistance assay and L1014F mutation frequency assay results showed that the L1014F mutation frequency could infer M. persicae's resistance level to pyrethroid insecticides. Therefore, in our study, we established a Loop‐Mediated Isothermal Amplification (LAMP)‐based method to detect the L1014F mutation in the voltage‐gated sodium channel of M. persicae. This method uses single aphid crude extract DNA as a template, and the full reaction can be completed in 1 h in a relaxed temperature range. The results could also be obtained by colour change without any molecular detection. We have also optimized the reaction system and procedures to improve the accuracy of the assay to over 95%. Based on this, we can detect the mutation frequency of voltage‐gated sodium channel L014F in M. persicae field populations in a non‐laboratory setting, such as in the field, in a stable, accurate, and rapid manner. This method facilitates quick determination of the resistance of M. persicae populations, followed by guide field control.

SOH estimation and RUL prediction of lithium batteries based on multidomain feature fusion and CatBoost model

AbstractIn this paper, a lithium‐ion battery State of Health (SOH) estimation algorithm is proposed based on the fusion of multidomain features and the application of a CatBoost model. The aim is to address the issue of low prediction accuracy in SOH caused by the utilization of single‐feature extraction techniques. The algorithm encompasses the extraction of various features from the original charge–discharge data, including time‐domain, frequency‐domain, entropy, and time‐series features. Following the evaluation of feature importance, a feature selection process is conducted to eliminate redundant features that provide a limited contribution to the predictive results. Subsequently, a multiple‐set discriminative correlation analysis is employed to integrate high‐dimensional features. To attain accurate predictions, the CatBoost model is further optimized through the utilization of a sparrow search algorithm. Experimental results demonstrate that the proposed algorithm achieves accurate SOH estimations within individual batteries, as evidenced by mean square error values consistently below 4e−4 and goodness‐of‐fit values exceeding or equal to 0.98. Additionally, the algorithm exhibits reliable prediction capabilities across different batteries operating under the same charge/discharge strategy. Comparative analysis indicates that the adoption of the multidomain feature fusion approach yields improved prediction accuracy in contrast to the utilization of a single feature extraction method.

Computational model for breast cancer diagnosis using HFSE framework

Mammography is one of the imaging modalities used in diagnosing breast cancer at an earlier stage. Misdiagnosis leads to risks for the patients. Better feature extraction and selection techniques can reduce misdiagnoses as they are essential in better-performing classifiers. The proposed approach in this paper introduces a novel Hybrid Feature Extraction and Hybrid Feature Selection (HFSE) framework. It uses mammograms from Digital Database for Screening Mammography (DDSM) datasets to classify mammograms into benign and malignant images. This paper presents a novel hybrid feature extraction approach from the Gray level co-occurrence matrix (GLCM), Linear local binary pattern (LBP), Gabor, and Tamura. Combinedly, it extracts 104 features to train the advanced classifiers such as Logistic Regression, Linear Perceptron, Support Vector Machine, Decision Tree, and Artificial Neural Network. The proposed Hybrid feature extraction method vigorously compares combinations of existing single feature extraction methods. The paper also presents a novel hybrid feature selection approach to choose a subset of the most relevant feature. It compares the Intrinsic feature with the proposed hybrid feature selection method. Hyperparameter tuning and Pipeline optimization techniques applied to the classifiers improve their performance metrics. The experimental results show that the proposed framework performs better using Hybrid feature extraction and feature selection on Artificial Neural Networks. This paper makes a comparative analysis of the related works. It outperforms by achieving a classifier accuracy of 94.57%, specificity of 91.80%, the sensitivity of 95.59 %, and an F1-score of 94.89% on Artificial Neural Networks.

Simultaneous determination of femtogram levels of 237Np, 239Pu, and 240Pu in environmental solid samples using extraction chromatography and ICP-MS/MS

An analytical method based on triple quadrupole inductively coupled plasma mass spectrometry (ICP-MS/MS) was developed for simultaneous determination of femtogram levels of 237Np, 239Pu, and 240Pu in environmental samples. By carefully controlling the valence states of Np and Pu in the entire separation procedure using a simple single extraction chromatographic column (TK200), the consistent behavior of Np and Pu was achieved, allowing for the reliable application of 242Pu as the chemical yield tracer for 237Np, 239Pu, and 240Pu. A high decontamination factor of 3.2 × 107 for the most critical interfering element, uranium, was achieved in the chemical separation step. The interferences of 238U1H+ and peak tailing of 238U+ during the measurement of plutonium isotopes were effectively eliminated by utilizing 7.5 mL/min He–1.1 mL/min CO2 as reaction gases in the octupole collision/reaction cell and employing sequential quadrupole mode for mass separation in ICP-MS/MS. Specifically, the interference of 238U1H+ was reduced to 10−6 and the peak tailing of 238U+ to 10−10, surpassing the performance of measurement method without reaction gases by 3 orders of magnitude. The developed method enables the accurate determination of femtogram levels of 237Np, 239Pu, and 240Pu in the samples with U/Np and U/Pu atom ratios of up to 1017 and 1012, respectively. The developed method was validated by analyzing standard reference materials and spiked soil samples.

Potential Extract of Lerak Fruit (Sapindus Rarak), Pegagan (Centella Asiatica,) Mimba Seed (Azadirachta Indica A.Juss) and Mixing of Third Extract to Motility and Viability of Spermatozoa Guinea Pigs (Cavia Porcellus)

Spermicides are substances that can kill spermatozoa. Spermicide circulating in the community contains Nonoxynol9 which, if used for a long time, can irritate the vaginal mucosa and penis. Indonesia has a variety of plants that have the potential to act as anti-fertility, including Sapindus rarak, Centella asiatica, and Azadirachta indica. Objective: This study aim to evaluate extract Sapindus rarak, Centella asiatica, and Azadirachta indica. and mixing of third etract to motility and viability of spermatozoa Guinea pigs. Methods: The sample of this research was 15 guinea pig spermatozoa, 3-4 months old male guinea pigs taken from the cauda epididymis and vas deferens. Guinea pigs were divided into 5 group treatments with each group treatment incubated for 1, 10, and 30 minutes and repeated each for 5 times. The treatments in this study is control, 200 μl of spermatozoa suspension + 50 μl of Sapindus rarak fruit pulp extract 0.9%; 200 μl of spermatozoa suspension + 50 μl of gotu kola extract 0.9%; 200 μl of spermatozoa suspension + 50 μl of 0.9% neem seed extract; 200 μl of spermatozoa suspension + 50 μl of the third mixture extract 0.9%. This study used a completely randomized design (CRD). The observations in this study were the motility of guinea pig spermatozoa categories a + b and the viability of guinea pig spermatozoa using a microscope. The data obtained were analyzed statistically using One Way ANOVA. The results of this study indicated that the administration of extract Sapindus rarak, Centella asiatica, and Azadirachta indica and a mixture of the three extracts had a significant effect (P < 0.05) on the motility of guinea pig spermatozoa categories a + b and viability of guinea pig spermatozoa. Conclution: This study concludes that the mixture of the three extracts has more potential in reducing the motility of spermatozoa categories a + b and the viability of guinea pig spermatozoa compared to single extracts. The mixture of the three extracts may contribute for reduce quality of sperm.

Rare earth elements in tea garden soils and their bioavailability to tea buds in Taiwan

Tea (Camellia sinensis) is a widespread beverage plant that prefers aluminum-enriched acidic soils. However, rare earth elements (REEs) might be highly phyto-available in these soils. With the increasing demands for REEs in high-technology industries, understanding the dynamics of REEs in the environment is essential. Thus, this study identified the total concentration of REEs in the root-zone soils and corresponding tea buds (n = 35) collected from tea gardens in Taiwan. Additionally, the labile REEs in the soils were extracted with 1 M KCl, 0.1 M HCl, and 0.05 M ethylenediaminetetraacetic acid (EDTA) to elucidate the fractionation tendency of REEs in the soil-plant system and the relationships between REEs and Al in the tea buds. The concentration of light REEs (LREEs) was higher than those of medium REEs (MREEs) and heavy REEs (HREEs) in all soil and tea bud samples. According to the upper continental crust (UCC) normalization, MREEs and HREEs were more abundant than LREEs in the tea buds. Furthermore, REEs remarkably increased with increasing Al in the tea buds, whereas the linear correlations between Al and MREEs and HREEs were stronger than between LREEs. Compared with LREEs, the extractabilities of MREEs and HREEs by all single extractants in the soils were higher, coinciding with their higher UCC-normalization-based enrichments in the tea buds. Moreover, the 0.1 M HCl- and 0.05 M EDTA-extractable REEs were affected by soil properties and significantly correlated with the total REEs in the tea buds. The concentration of REEs in the tea buds was successfully predicted by empirical equations of extractable REEs with 0.1 M HCl and 0.05 M EDTA, as well as general soil properties including pH, organic carbon, dithionite-citrate-bicarbonate-extractable iron, aluminum, and phosphorus. However, this prediction should be further verified using many soil and tea types in the future.