UNASSIGNED: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer\'s disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation.
UNASSIGNED: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation.
UNASSIGNED: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods.
UNASSIGNED: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

Genotype-to-phenotype mapping is an essential problem in the current genomic era. While qualitative case-control predictions have received significant attention, less emphasis has been placed on predicting quantitative phenotypes. This emerging field holds great promise in revealing intricate connections between microbial communities and host health. However, the presence of heterogeneity in microbiome datasets poses a substantial challenge to the accuracy of predictions and undermines the reproducibility of models. To tackle this challenge, we investigated 22 normalization methods that aimed at removing heterogeneity across multiple datasets, conducted a comprehensive review of them, and evaluated their effectiveness in predicting quantitative phenotypes in three simulation scenarios and 31 real datasets. The results indicate that none of these methods demonstrate significant superiority in predicting quantitative phenotypes or attain a noteworthy reduction in Root Mean Squared Error (RMSE) of the predictions. Given the frequent occurrence of batch effects and the satisfactory performance of batch correction methods in predicting datasets affected by these effects, we strongly recommend utilizing batch correction methods as the initial step in predicting quantitative phenotypes. In summary, the performance of normalization methods in predicting metagenomic data remains a dynamic and ongoing research area. Our study contributes to this field by undertaking a comprehensive evaluation of diverse methods and offering valuable insights into their effectiveness in predicting quantitative phenotypes.

BACKGROUND: Kobreisa littledalei, belonging to the Cyperaceae family is the first Kobresia species with a reference genome and the most dominant species in Qinghai-Tibet Plateau alpine meadows. It has several resistance genes which could be used to breed improved crop varieties. Reverse Transcription Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR) is a popular and accurate gene expression analysis method. Its reliability depends on the expression levels of reference genes, which vary by species, tissues and environments. However, K.littledalei lacks a stable and normalized reference gene for RT-qPCR analysis.
RESULTS: The stability of 13 potential reference genes was tested and the stable reference genes were selected for RT-qPCR normalization for the expression analysis in the different tissues of K. littledalei under two abiotic stresses (salt and drought) and two hormonal treatments (abscisic acid (ABA) and gibberellin (GA)). Five algorithms were used to assess the stability of putative reference genes. The results showed a variation amongst the methods, and the same reference genes showed tissue expression differences under the same conditions. The stability of combining two reference genes was better than a single one. The expression levels of ACTIN were stable in leaves and stems under normal conditions, in leaves under drought stress and in roots under ABA treatment. The expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression was stable in the roots under the control conditions and salt stress and in stems exposed to drought stress. Expression levels of superoxide dismutase (SOD) were stable in stems of ABA-treated plants and in the roots under drought stress. Moreover, RPL6 expression was stable in the leaves and stems under salt stress and in the stems of the GA-treated plants. EF1-alpha expression was stable in leaves under ABA and GA treatments. The expression levels of 28 S were stable in the roots under GA treatment. In general, ACTIN and GAPDH could be employed as housekeeping genes for K. littledalei under different treatments.
CONCLUSIONS: This study identified the best RT-qPCR reference genes for different K. littledalei tissues under five experimental conditions. ACTIN and GAPDH genes can be employed as the ideal housekeeping genes for expression analysis under different conditions. This is the first study to investigate the stable reference genes for normalized gene expression analysis of K. littledalei under different conditions. The results could aid molecular biology and gene function research on Kobresia and other related species.

In recent years, the field of biology has witnessed a surge of interest in genomics research due to the advancements in biotechnology. Gene expression pattern analysis plays a crucial role in this research, as it enables us to understand the regulatory mechanism of gene expression and the associated biological processes. Real-time quantitative polymerase chain reaction (q-PCR) is an efficient method to analyze the gene expression patterns, for which accuracy relies on the standardized analysis of reference genes. However, numerous studies have shown that no reference gene is universal in all conditions, so screening a suitable reference gene under certain conditions is of great importance. Cinnamomum burmannii (C. burmannii) is rich in volatile components and has high medicinal and economic value. However, knowledge of the screening of reference genes for the gene expression analysis of C. burmannii is insufficient. Aiming at this problem, we evaluated and screened the reference genes in C. burmannii under different experimental conditions, including different abiotic stresses (Cold-treated, PEG-treated and Nacl-treated), different tissues, leaves at different developmental stages and different chemical types. In this study, different algorithms (∆Ct, geNorm, NormFinder and BestKeeper) were used to evaluate the stability of the candidate reference genes, and RefFinder further merged the output data to screen out the optimum reference gene under various experimental conditions in C. burmannii. The results showed that the optimal reference gene number for gene standardization was 2 under different experimental conditions. RPL27|RPS15 was the most suitable combination under the Nacl-treated and PEG-treated samples. RPL27|APT was the optimum combination under the Cold-treated samples. The optimal combinations of other samples were EF1α|ACT7 for different tissues, eIF-5A|Gllα for different borneol clones in C. burmannii, RPS15|ACT7 for leaves at different developmental stages and RPS15|TATA for all samples. Additionally, two terpenoid synthesis-related genes (CbWRKY4 and CbDXS2) were standardized to verify the feasibility of the selected reference genes under different experimental conditions. This study will be helpful for the subsequent molecular genetic mechanism study of C. burmannii.

Quantitative real-time PCR (qRT-PCR) is a widely applied technique for accurately assessing the expression of target genes. In practice, the evaluation of gene expression requires appropriate reference genes. To screen reliable reference genes for evaluating gene expression via qRT-PCR in Mythimna loreyi, a notorious migratory pest across Asia, Africa, Europe, and Australia, we assessed the expression stability of 13 candidate reference genes in M. loreyi using the ΔCt method, BestKeeper, Normfinder, GeNorm, and the web-based comprehensive platform RefFinder. These reference genes include RPL10, RPL27, RPL32, RPS3, TATA-box, GAPDH, AK, Actin, EF, α-tubulin, SOD, 18S rRNA, and FTZ-F1, which is frequently employed in Lepidoptera insects. Our findings revealed that the performance of the candidate reference gene depended on experimental conditions. Specifically, RPL27 and RPL10 were the most suitable for evaluating expression changes across developmental stages, tissues, and adult ages. The optimal reference genes were recommended in specific experiment conditions, for instance, EF and RPS3 were recommended for mating status, AK and RPL10 were recommended for temperature treatments, RPL27 and FTZ-F1 were recommended for larva diet, and EF and RPL27 were recommended for adult diet treatments. Additionally, expression profiles of pheromone-binding protein 2 (MlorPBP2) and glutathione S-transferase (MlorGST1) were used to validate the reference genes. This study provides reference genes for the accurate normalization of qRT-PCR data, laying the groundwork for studying the expression of target genes in M. loreyi.

Hymenopellis radicata (H. radicata) is an edible fungus rich in protein and mineral elements, with high edible and medical value. And reference genes suitable for normalization of qRT-PCR data from this species have not been investigated. In this study, therefore, we selected 11 housekeeping genes common in biology. The expression levels of these housekeeping genes were measured in three different tissues and six different abiotic stress treatments in mycelium. They were evaluated for expression stability using online tools. The results showed that gene ACT could be stable expressed in all samples. The expressions of genes TUB and UBQ10 are the most stable under heat stress, ACT and EF are the most stable genes under salt stress, ACT and TUB are the most stable genes under oxidation stress, RPL6 and EF are the most stable genes under pH condition, ACT and RPB2 are the most stable genes under cadmium stress, and RPB2 and UBC are the most stable genes under drought condition. ACT and PP2A are the most stable genes at different tissue sites. This study is of great help to explore the gene expression pattern of H. radicata, and also provides reference for internal reference gene screening under other conditions.

In traditional Chinese medicine, Angelica dahurica is a valuable herb with numerous therapeutic applications for a range of ailments. There have not yet been any articles on the methodical assessment and choice of the best reference genes for A. dahurica gene expression studies. Real-time quantitative PCR (RT-qPCR) is widely employed as the predominant method for investigating gene expression. In order to ensure the precise determination of target gene expression outcomes in RT-qPCR analysis, it is imperative to employ stable reference genes. In this study, a total of 11 candidate reference genes including SAND family protein (SAND), polypyrimidine tract-binding protein (PTBP), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), actin (ACT), TIP41-like protein (TIP41), cyclophilin 2 (CYP2), elongation factor 1 α (EF1α), ubiquitin-protein ligase 9 (UBC9), tubulin β-6 (TUB6), thioredoxin-like protein YLS8 (YLS8), and tubulin-α (TUBA) were selected from the transcriptome of A. dahurica. Subsequently, three statistical algorithms (geNorm, NormFinder, and BestKeeper) were employed to assess the stability of their expression patterns across seven distinct stimulus treatments. The outcomes obtained from these analyses were subsequently amalgamated into a comprehensive ranking using RefFinder. Additionally, one target gene, phenylalanine ammonia-lyase (PAL), was used to confirm the effectiveness of the selected reference genes. According to the findings of this study, the two most stable reference genes for normalizing the expression of genes in A. dahurica are TIP41 and UBC9. Overall, our research has determined the appropriate reference genes for RT-qPCR in A. dahurica and provides a crucial foundation for gene screening and identifying genes associated with the biosynthesis of active ingredients in A. dahurica.

MicroRNAs (miRNAs) are a class of small non-coding RNAs that exert their biological functions as negative regulators of gene expression. They are involved in the skin wound healing process with a dynamic expression pattern and can therefore potentially serve as biomarkers for skin wound age estimation. However, no reports have described any miRNAs as suitable reference genes (RGs) for miRNA quantification in wounded skin or samples with post-mortem changes. Here, we aimed to identify specific miRNAs as RGs for miRNA quantification to support further studies of skin wound age estimation. Overall, nine miRNAs stably expressed in mouse skin at certain posttraumatic intervals (PTIs) were preselected by next-generation sequencing as candidate RGs. These nine miRNAs and the commonly used reference genes (comRGs: U6, GAPDH, ACTB, 18S, 5S, LC-Ogdh) were quantitatively examined using quantitative real-time reverse-transcription polymerase chain reaction at different PTIs during skin wound healing in mice. The stabilities of these genes were evaluated using four independent algorithms: GeNorm, NormFinder, BestKeeper, and comparative Delta Ct. Stability was further evaluated in mice with different post-mortem intervals (PMIs). Overall, mmu-miR-26a-5p, mmu-miR-30d-5p, and mmu-miR-152-3p were identified as the most stable genes at both different PTIs and PMIs. These three miRNA RGs were additionally validated and compared with the comRGs in human samples. After assessing using one, two, or three miRNAs in combination for stability at different PTIs, PMIs, or in human samples, the set of miR-26a/30d/152 was approved as the best normalizer. In conclusion, our data suggest that the combination of miR-26a/30d/152 is recommended as the normalization strategy for miRNA qRT-PCR quantification in skin wound age estimation.
UNASSIGNED: The small size of miRNAs makes them less susceptible to post-mortem autolysis or putrefaction, leading to their potential use in wound age estimation.Studying miRNAs as biological indicators of skin wound age estimation requires the selection and validation of stable reference genes because commonly used reference genes, such as U6, ACTB, GAPDH, 5S, 18S, and LC-Ogdh, are not stable.miR-26a/30d/152 are stable and reliable as reference genes and their use in combination is a recommended normalization strategy for miRNA quantitative analysis in wounded skin.

Quantitative real-time PCR (qRT-PCR) is widely accepted as a precise and convenient method for quantitatively analyzing the expression of functional genes. The data normalization strongly depends upon stable reference genes. The bean bug, Riptortus pedestris (Hemiptera: Alydidae), is a significant pest of leguminous crops and broadly distributed across Southeast Asia. In this study, a total of 16 candidate reference genes (RPL32, RPS23, SDHA, UBQ, UCCR, GST, TATA-box, HSP70, GAPDH, RPL7A, SOD, RPS3, Actin, α-tubulin, AK, and EF1) were carefully chosen in R. pedestris, and their expression levels were assessed across various conditions, including different developmental stages, diverse tissues, temperature treatments, adult age, molting time, and mating status. Following this, the stability of these reference genes was evaluated using four algorithms (ΔCt, GeNorm, NormFinder, and BestKeeper). Ultimately, the comprehensive rankings were determined using the online tool RefFinder. Our results demonstrate that the reference gene for qRT-PCR analysis in R. pedestris is contingent upon the specific experimental conditions. RPL7A and EF1 are optimal reference genes for developmental stages. Furthermore, α-tubulin and EF1 exhibit the most stable expression across various adult tissues. RPL32 and RPL7A exhibit the most stable expression for adult age. For nymph age, RPL32 and SOD display the most stable expression. For temperature conditions, RPS23 and RPL7A were identified as the most suitable for monitoring gene expression. Lastly, we verified the practicability of evaluating expression levels of odorant-binding protein 37 (RpedOBP37) and cytochrome P450 6a2 (RpedCYP6) throughout developmental stages, tissues, and temperature conditions. These findings are a significant addition to the qRT-PCR analysis studies on R. pedestris, serving as a fundamental groundwork for future investigations on stable reference genes in R. pedestris as well as other organisms.

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.