Amount Of Sample Research Articles

Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance.

Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance. First, to capture the local label correlations, EFSMDER implements orthogonal regression to map the original EEG feature space into a low-dimension space. Then, it employs the global label correlations in the original multi-dimension emotional label space to effectively construct the label information in the low-dimension space. With the aid of local and global relevance information, EFSMDER can conduct representational EEG feature subset selection. Three EEG emotional databases with multi-dimension emotional labels were used for performance comparison between EFSMDER and fourteen state-of-the-art methods, and the EFSMDER method achieves the best multi-dimension classification accuracies of 86.43, 84.80, and 97.86 percent on the DREAMER, DEAP, and HDED datasets, respectively.

Clinical Evaluation of a New Chemiluminescence Assay for the Detection of Treponema Pallidum Antibodies.

A new chemiluminescence assay, the Anti-TP-Ⅱ assay, is going to be commercially available in clinical laboratories in China and other countries. This study examined the performance of the new assay for the detection of TP infection and compared it with that of the Anti-TP assay by using large amounts of clinical samples. The precision, accuracy, anti-interference ability, and the clinical sensitivity and specificity of the Anti-TP-Ⅱ assay were evaluated. In addition, compared with those of the Anti-TP assay, the false positive and false negative rates of the Anti-TP-Ⅱ assay were evaluated for 2,436 clinical routine samples and 711 preselected Anti-TP assay reactive samples. Discrepancy of the samples was investigated with the recomLinec Treponema IgM/IgG kit or the Elecsys syphilis assay. The precision, accuracy, and anti-interference ability of the Anti-TP-Ⅱ assay met the national standard of China, and there was an overall agreement of 96.75% (Kappa = 0.91) between the two assays. The sensitivity and specificity of the Anti-TP-Ⅱ assay were 100% (95% CI: 94.13% to 100%) and 99.92% (95% CI: 99.70% to 99.99%), respectively. Compared with the Anti-TP assay, the Anti-TP-Ⅱ assay significantly reduced the number of borderline samples and the false positive rate. Considering its excellent performance, the Anti-TP-Ⅱ assay is a good screening test for high-throughput laboratories and can replace the previous generation of reagents, the Anti-TP assay, with a superior specificity.

RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation.

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which requires a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss in the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation. The source code is available at https://github.com/hsiangyuzhao/RCPS.

RNA Isolation from Human Stem Cell-Derived Retinal Organoids.

RNA isolation is an essential first step for many types of molecular analyses, including reverse transcription PCR (RT-PCR)/quantitative RT-PCR (qRT-PCR), Northern blotting, microarrays, and RNA-sequencing. While many RNA purification methods have been reported, it can be challenging to extract sufficient quantity, and suitable quality, of RNA from very small amounts of tissue and/or samples containing low numbers of cells. Here we outline a total RNA isolation method that reproducibly yields high-quality RNA from human stem cell-derived retinal organoids for downstream transcriptomic analysis.

SUBJECT: REVIEW METHODS OF DETECTING SUGAR IN A SAMPLE

The sugar is a simple type of carbohydrate and represents the main source of energy to power the biological and physical activities in the body. However, it has been reported that over weight gain, imperfect metabolic processes and heart diseases are mainly related to excess sugar intake, which causes diabetic complains. This has raised the demand of having reliable test methods for quantifying sugar amount in different samples to both monitor sugar levels and to study the biological effects of carbohydrates. This provides valuable information in various fields, including food science, biochemistry, and medicine. Several test methods have been applied to detect the presence of sugar in a biological sample. Conventional tests imply using special chemical reagents. Two well known tests are discussed briefly in this report, namely Molich’s and Fehling's tests. The chemical reagents, test procedures, precaution as well as advantages and limitations were comprehensively discussed. A comparison between the two tests has been thoroughly addressed.

Accuracy of Saliva as a Diagnostic Medium for Hepatitis B Virus Infection: A Quantitative ELISA Analysis

Abstract Context: Diagnosis of hepatitis B virus infection (HBV) has been traditionally serum-based with its attendant risks and invasive procedure. Massive research interests are being directed to the use of saliva for screening, diagnosis, and monitoring of several infectious diseases, including HBV. Various reports suggest that saliva contains biomarkers that are comparable with that of sera. It can also be obtained with minimal risk to the clinician and patient as well. Aim: To determine the diagnostic accuracy of saliva in quantitative detection of hepatitis B surface antigen (HBsAg) using enzyme-linked immunosorbent assay (ELISA). Settings and Design: This cross-sectional analytical study was performed on HBV seropositive patients at the Gastroenterology clinic of the University of Benin Teaching Hospital, Benin City, Edo State. Materials and Methods: Under standardized conditions, equal amounts of blood and saliva samples of 43 HBsAg seropositive patients were analyzed using ELISA to quantitatively detect the concentration of HBsAg. Statistical Analysis Used: The analysis relied on Spearman’s correlation coefficient, linear regression analysis, and Bland–Altman plots to describe the correlational, predictive, and agreements between measurements of HBsAg in sera and saliva. The statistical significance was set at P < 0.05, while a 95% confidence level was used to construct intervals. Results: All participants had detectable levels of HBsAg in both saliva and serum with mean titers of 1.70 ± 0.35 ng/ml and 2.80 ± 0.77 ng/ml, respectively. Correlational and linear regression showed poor fit and predictive relationships of the HBsAg levels. Bland–Altman analysis showed good agreement and no significant bias in their diagnostic agreements. Conclusion: Saliva can be reliably used in screening and diagnosis of HBV infection. There was agreement in their levels independently and within their averages. It may be premature to rely on saliva for quantitative assay of HBsAg in treatment monitoring.

A ratiometric substrate for rapid evaluation of transfer hydrogenation efficiency in solution.

A cyclometalated iridium(III) complex bearing a self-immolative quinolinium moiety was developed as a ratiometric substrate for transfer hydrogenation studies. This photoluminescent probe allowed the rapid screening of a variety of Ir catalysts using a microplate reader, offering a convenient method to assess activity using a minimum amount of catalyst sample.

Image-free recognition of moderate ROP from mild with machine learning algorithm on plasma Raman spectrum

The retinopathy of prematurity (ROP) can cause serious clinical consequences and, fortunately, it is remediable while the time window for treatment is relatively narrow. Therefore, it is urgent to screen all premature infants and diagnose ROP degree timely, which has become a large workload for pediatric ophthalmologists. We developed a retinal image-free procedure using small amount of blood samples based on the plasma Raman spectrum with the machine learning model to automatically classify ROP cases before medical intervention was performed. Statistical differences in infrared Raman spectra of plasma samples were found among the control, mild (ZIIIS1), moderate (ZIIIS2 & ZIIS1), and advanced (ZIIS2) ROP groups. With the different wave points of Raman spectra as the inputs, the outputs of our support vector machine showed that the area under the curves in the receiver operating characteristic (AUC) were 0.763 for the pair comparisons of the control with the mild groups, 0.821 between moderate and advanced groups (ZIIS2), while more than 90% in comparisons of the other four pairs: control vs. moderate (0.981), control vs. advanced (0.963), mild vs. moderate (0.936), and mild vs. advanced (0.953), respectively. Our study could advance principally the ROP diagnosis in two dimensions: the moderate ROPs have been classified remarkably from the mild ones, which leaves more time for the medical treatments, and the procedure of Raman spectrum with a machine learning model based on blood samples can be conveniently promoted to those hospitals lacking of the pediatric ophthalmologists with experience in reading retinal images.

Development of a reliable method for determination of N-nitrosamines in medicines using disposable pipette extraction and HPLC-MS analysis.

This study outlines the development and optimization of an analytical method using Disposable Pipette Extraction (DPX) followed by high performance liquid chromatography-mass spectrometry (HPLC-MS) analysis to determine NAs in medicines. HPLC-MS analysis utilized a reversed-phase and positive mode electrospray ion source. DPX parameters were optimized through univariate and multivariate analyses, including extraction phase, desorption solvent, sample pH, equilibrium time, and extraction/desorption cycles. The optimized conditions included a C18 extraction phase, methanol desorption solvent, pH at 7, an equilibrium time of 30 seconds, 2 extraction cycles, and 5 desorption cycles. Considering this method, it was possible to achieve a sample preparation step for the analysis of NAs in medicines using a minimal amount of extraction phase, sample, and desorption solvent. Furthermore, the total extraction procedure enables the extraction of NAs in around 4 minutes with NA recovery up to 98%. Analytical performance demonstrated precision and accuracy below 15% and a quantification limit of 1 ng mL-1, meeting validation requirements set by regulations worldwide. Thus, the DPX/HPLC-MS technique offers a faster and cost-effective method for analyzing NAs in medicines compared to traditional approaches. Besides, this method reduces solvent consumption and residue generation, enhancing environmental sustainability according to green chemistry principles.

Гербарий лишайников Института морской геологии и геофизики ДВО РАН (SAK): первые результаты инвентаризации

The organization of the lichen herbarium in the Institute of Marine Geology and Geophysics of the FEB RAS was initiated in 2014 after a number of integrated expeditions in the Sakhalin Region. The herbarium is based on the collections of Aleksandr K. Ezhkin and Vladimir V. Kaganov. A significant amount of lichen samples was collected in protected areas of the Sakhalin Region, in wild remote sites, and in areas of volcanic activity. Currently, a part of the collection (1178 samples, 307 species) is mounted and stored in the Laboratory of plant ecology and geoecology, which is about 1/8 of all herbarium material of lichens stored in the Institute of Marine Geology and Geophysics of the FEB RAS.

Chromatographic Separation and Quantitation of Sphingolipids from the Central Nervous System or Any Other Biological Tissue.

Chromatographic separation and purification of an individual lipid to homogeneity have long been introduced. Using this concept, a more precise method has been developed to identify and characterize the sphingolipid composition(s) using a small amount (30mg) of biological sample. Sphingolipids (lipids containing sphingosine or dihydrosphingosine) are well-known regulators of the central nervous system development and play a critical role in neurodegenerative diseases. Introducing a silicic acid column chromatography, sphingolipid components have been separated to individual fractions such as ceramide, glucosyl/galactosylceramide, other neutral and acidic glycosphingolipids, including (dihydro)sphingosine and psychosine; as well as phospholipids from which individual components are quantified employing a single or combination of other advanced chromatography procedures such as thin-layer chromatography, gas chromatography-mass spectrometry, and high-performance liquid chromatography-mass spectrometry.

Protein-Protein Binding Kinetics by Biolayer Interferometry.

The specific kinetics and thermodynamics of protein-protein interactions underlie the molecular mechanisms of cellular functions; hence the characterization of these interaction parameters is central to the quantitative understanding of physiological and pathological processes. Many methods have been developed to study protein-protein interactions, which differ in various features including the interaction detection principle, the sensitivity, whether the method operates in vivo, in vitro, or in silico, the temperature control, the use of labels, immobilization, the amount of sample required, the number of measurements that can be accomplished simultaneously, or the cost. Bio-Layer Interferometry (BLI) is a label-free biophysical method to measure the kinetics of protein-protein interactions. Label-free interaction assays are a broad family of methods that do not require protein modifications (other than immobilization) or labels such as fusions with fluorescent proteins or transactivating domains or chemical modifications like biotinylation or reaction with radionuclides. Besides BLI, other label-free techniques that are widely used for determining protein-protein interactions include surface plasmon resonance (SPR), thermophoresis, and isothermal titration calorimetry (ITC), among others.

Efficient centralized traffic grid signal control based on meta-reinforcement learning

Dear Editor, This letter presents a novel method to tackle the two challenges of the centralized traffic control based on reinforcement learning (RL): the curse of dimensionality as the scale of traffic grid increases and the data-inefficiency problem that requires large amounts of samples to learn. First, we use a sequence-to-sequence (seq2seq) model and the attention mechanism to decompose the state-action space into sub-spaces, thus dealing with the first challenge. Second, we propose a new context-based meta-RL model that disentangles task inference and control, which improves the meta-training efficiency and accelerates the learning process in the new environment. We evaluate our approach on real-world datasets and the results demonstrate that our approach outperforms the state-of-the-art deep reinforcement learning (DRL)-based methods and the traditional control methods.

Sample Preparation for Electron Cryo-Microscopy of Macromolecular Machines.

High-resolution structure determination by electron cryo-microscopy underwent a step change in recent years. This now allows study of challenging samples which previously were inaccessible for structure determination, including membrane proteins. These developments shift the focus in the field to the next bottlenecks which are high-quality sample preparations. While the amounts of sample required for cryo-EM are relatively small, sample quality is the key challenge. Sample quality is influenced by the stability of complexes which depends on buffer composition, inherent flexibility of the sample, and the method of solubilization from the membrane for membrane proteins. It further depends on the choice of sample support, grid pre-treatment and cryo-grid freezing protocol. Here, we discuss various widely applicable approaches to improve sample quality for structural analysis by cryo-EM.

An integrated microfluidic system for automatic detection of cholangiocarcinoma cells from bile.

Cholangiocarcinoma (CCA) is an aggressive cancer that originates from the epithelial cells lining the bile ducts. Due to its location deep within the body and nonspecific symptoms in the early stages, it is often diagnosed at the advanced stage, thus leading to worse prognosis. Circulating tumor cells within liquid biopsies (i.e. blood) have been considered as promising biomarkers for CCA diagnosis, though current methods for profiling them are not satisfactory in terms of sensitivity and specificity. Herein we developed a new cancer cell probing and immuno-tracking assay known as "CAPTURE", which was performed on an integrated microfluidic system (IMS) to automate CCA diagnosis from bile with a sample amount of only 1 mL. The assay utilized magnetic beads surface-coated with two affinity reagents, a nucleic acid aptamer (HN16) and a glycosaminoglycan (SCH 45-mix), for capturing cancer cells in bile; the "gold standard" anti-epithelial cell adhesion molecule was used as a comparison. In a single-blind test of 54 CCA-positive (+) and 102 CCA-negative (-) clinical samples, sensitivities and specificities of 96 and 80%, respectively, were documented with the CAPTURE assay on-bench. An IMS composed of a centrifugal module for sample pretreatment and a CAPTURE module for cell capture and staining was integrated with a new "vertical integration module" for detecting cancer cells from bile without human intervention. Furthermore, a novel micro-tier structure within the centrifugal module was designed to block passage of gallbladder stones with diameters >1 mm, thereby preventing their interference during the subsequent CAPTURE assay. Improved sensitivity and specificity (100 & 83%, respectively) by using three affinity reagents were achieved on the IMS when using 26 clinical bile samples, confirming its clinical bio-applicability for CCA diagnosis. This approach could be therefore used for early-stage CCA diagnostics, ideally enabling effective treatment, as well as reducing potential for relapse.

Comprehensive Micro-SPE-Based Bottom-Up Proteomic Workflow for Sensitive Analysis of Limited Samples.

Clinical and biological samples are often scarce and precious (e.g., rare cell isolates, microneedle tissue biopsies, small-volume liquid biopsies, and even single cells or organelles). Typical large-scale proteomic methods, where significantly higher protein amounts are analyzed, are not directly transferable to the analysis of limited samples due to their incompatibility with pg-, ng-, and low-μg-level protein sample amounts. Here, we report the on-microsolid-phase extraction tip (OmSET)-based sample preparation workflow for sensitive analysis of limited biological samples to address this challenge. The developed platform was successfully tested for the analysis of 100-10,000 typical mammalian cells and is scalable to allow for lower and larger protein amounts and more samples to be analyzed (i.e., higher throughput of analysis).

High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers.

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

Micro blood analysis technology (μBAT): multiplexed analysis of neutrophil phenotype and function from microliter whole blood samples.

There is an ongoing need to do more with less and provide highly multiplexed analysis from limited sample volumes. Improved "sample sparing" assays would have a broad impact across pediatric and other rare sample type studies in addition to enabling sequential sampling. This capability would advance both clinical and basic research applications. Here we report the micro blood analysis technology (μBAT), a microfluidic platform that supports multiplexed analysis of neutrophils from a single drop of blood. We demonstrate the multiplexed orthogonal capabilities of μBAT including functional assays (phagocytosis, neutrophil extracellular traps, optical metabolic imaging) and molecular assays (gene expression, cytokine secretion). Importantly we validate our microscale platform using a macroscale benchmark assay. μBAT is compatible with lancet puncture or microdraw devices, and its design facilitates rapid operations without the need for specialized equipment. μBAT offers a new method for investigating neutrophil function in populations with restricted sample amounts.

Reducing Occupational Risks and Greening a Traditional Sample Treatment of Industrial Fluorine Containing-materials by Application of Design of Experiments

Despite the new technological advances, some traditional methodologies concerning wet chemistry must be used as reference methods when dealing with complex matrices or when no certified reference materials are available. In this context, Willard-Winter distillation is nowadays still employed as a reference technique for fluorine extraction in day-to-day analysis. However, this procedure requires strong acid mixtures, increasing waste treatment procedures/costs and the potential risks associated with their use. The present work reports the application of design of experiments (DoE) to improve the analytical methodology of reference for fluorine extraction through Willard-Winter distillation by substituting perchloric acid. Variables affecting the sample treatment of fluorine-containing compounds, anhydrite, fluorspar, cryolite and aluminium fluoride were studied to ensure complete dissolution and total extraction of fluorine. Volume of sulfuric acid, sample amount, volume of distilled solution including volume of melt and amount of NaOH for fluorspar and the extended fluoroaluminate compounds were the variables studied. Predicted experimental conditions were performed and validated in the target compound, obtaining fluorine concentrations comparable to those obtained by the reference methodology. By this modified approach, not only harmful effect of manipulation of perchloric acid is reduced but also costs of the analytical procedure do so. Besides, a greener performance is achieved by avoiding chlorinated species, reducing waste dangerousness and its treatment.

Effect Light-Intensity on Sleep Quality in Elderly

Background: Bedroom lighting is accused of being one of the factors causing changes in the quality of sleep in the elderly. Modifying the power of room lighting is an alternative to improving the sleep quality of the elderly. The aim of the study is to determine the effect of lighting intensity on the sleep quality of elderly.
 Methods: A quantitative study with a quasi-experimental control group design. Amount of sample is 40 responden and choosen used random sampling. Quality of sleep measure by Pittsburgh Sleep Quality Index (PSQI) and light-intensity measure used a luxmeter. the intervention group received lights with an intensity of 5, 10, 15, 20 watts, where the respondents were treated every day for 4 days with these light intensities. The Shapiro-Wilk test was used to test the normality of the data, while paired T test used to analyze the conditions before and after in the control and treatment groups. Independent sample t-test used to compare the sleep quality of the control and treatment groups.
 Results: There was no difference in sleep quality in the control group (0.15) between before and after the study, but there was a difference in sleep quality in the intervention group (0.00). There were differences in sleep quality between the control and treatment groups after the intervention was given (0.01).
 Conclusion: The intensity of the light has an effect on the sleep quality of the elderly, where the lower the intensity of the light, the better the quality of sleep. Elderly should sleep on level lighting intensity less than 5 watt.