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Motivation Dropout events bring challenges in analyzing single-cell RNA sequencing data as they introduce noise and distort the true distributions of gene expression profiles. Recent studies focus on estimating dropout probability and imputing dropout events by leveraging information from similar cells or genes. However, the number of dropout events differs in different cells, due to the complex factors, such as different sequencing protocols, cell types, and batch effects. The dropout event differences are not fully considered in assessing the similarities between cells and genes, which compromises the reliability of downstream analysis. Results This work proposes a hybrid Generative Adversarial Network with dropouts identification to impute single-cell RNA sequencing data, named AGImpute. First, the numbers of dropout events in different cells in scRNA-seq data are differentially estimated by using a dynamic threshold estimation strategy. Next, the identified dropout events are imputed by a hybrid deep learning model, combining Autoencoder with a Generative Adversarial Network. To validate the efficiency of the AGImpute, it is compared with seven state-of-the-art dropout imputation methods on two simulated datasets and seven real single-cell RNA sequencing datasets. The results show that AGImpute imputes the least number of dropout events than other methods. Moreover, AGImpute enhances the performance of downstream analysis, including clustering performance, identifying cell-specific marker genes, and inferring trajectory in the time-course dataset. Availability and implementation The source code can be obtained from https://github.com/xszhu-lab/AGImpute. [ABSTRACT FROM AUTHOR]

Spatial transcriptome (ST) technology provides both the spatial location and transcriptional profile of spots, as well as tissue images. ST data can be utilized to construct gene regulatory networks, which can help identify gene modules that facilitate the understanding of biological processes such as cell communication. Correlation measurement is the core basis for constructing a gene regulatory network. However, due to the high noise and sparsity in ST data, common correlation measurement methods such as the Pearson correlation coefficient (PCC) and Spearman correlation coefficient (SPCC) are not suitable. In this work, a new gene correlation measurement method called STgcor is proposed. STgcor defines vertexes as spots in a two-dimensional coordinate plane consisting of axes X and Y from the gene pair (X and Y). The joint probability density of Gaussian distribution of the gene pair (X and Y) is calculated to identify and eliminate outliers. To overcome sparsity, the degree, trend, and location of the distribution of vertexes are used to measure the correlation between gene pairs (X, Y). To validate the performance of the STgcor method, it is compared with the PCC and SPCC in a weighted coexpression network analysis method using two ST datasets of breast cancer and prostate cancer. The gene modules identified by these methods are then compared and analyzed. The results show that the STgcor method detects some special gene modules and cancer-related pathways that cannot be detected by the other two methods. [ABSTRACT FROM AUTHOR]

Objective: Cancer-related cognitive impairments (CRCI) are frequently reported among cancer survivors, and attention is the most frequently assessed cognitive domain in CRCI. However, there is no consensus as to whether attention is impaired. We suggest that a major reason for this lack of agreement is a lack of construct validity for neuropsychological attention tests. We propose to assess the construct validity of neuropsychological attention tests with respect to experimental paradigms from cognitive psychology.Methods: Self-reported cancer survivors (N = 314) completed an online battery comprising six experimental attention paradigms and eight neuropsychological tests. Confirmatory factor analysis was used to evaluate the fit of five models derived from a general population sample (N = 636) in a previous study (M. Treviño, Cogn Res Princ Implic, in press). We then subjected the best-fitting model to a measurement invariance analysis.Results: The best-fitting model was a six intercorrelated factor structure, comprising Capacity, Search, Digit Span, Arithmetic, Sustained Attention, and Flanker Interference factors. Configural and weak invariance held, indicating that the factor loadings were invariant across groups. Strong invariance, indicating that intercepts were also invariant, held except for the Approximate Number Sense test.Conclusions: According to our factor model, Spatial Span and Digit Symbol Coding measure attentional capacity, while the Trail Making Test (A&B) and Letter Cancellation tests measure visual search ability. However, Digit Span and Arithmetic tests do not measure attention. We hope that these results will lead to better scientific models, better patient education, and, ultimately, improved outcomes for survivors. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) can present cellular heterogeneity at higher resolution when measuring the gene expression in an individual cell. However, there are still some computational problems in scRNA-seq data, including high dimensionality, high sparseness, and high noise. To solve them, dimensionality reduction is essential as it reduces dimensions and also removes most of the zeros and noise. Therefore, we propose a hybrid dimensionality reduction algorithm for scRNA-seq data by integrating binning-based entropy and a denoising autoencoder, named ScEDA. In ScEDA, a novel binning-based entropy estimation method is performed to select efficient genes, while removing noise. For each gene, binning-based entropy is designed to describe the differences in its expression across all cells, that is, the distribution of expression of each gene in all cells. Genes are regarded as inefficient and removed when they achieve low binning-based entropy. Moreover, by combining Kullback–Leibler (KL) divergence with the autoencoder, the objective function is reconstructed to maximize the similarity in distribution between input data and reconstructed data. Furthermore, by adding Poisson-distributed noise to the original input data, the denoising autoencoder is used to improve robustness. Compared with three other clustering methods, ScEDA provides superior average performance on 16 real scRNA-seq datasets, with obvious enhancement in large-scale datasets. [ABSTRACT FROM AUTHOR]

Background: With increasing multidisciplinary care strategies, palliative medicine practitioner perceptions toward acupuncture integration are significant. Objective: To evaluate acupuncture availability and acceptability in Australian palliative care. Outcomes: (1) Domains of the survey included participant characteristics, (2) workplace availability, (3) personal attitudes, and (4) recommendation likelihood. Design: An online REDCap survey of Australian palliative medicine practitioners was administered. Results: Acupuncture was mostly not available/permitted at workplaces (45.2%) due to cost/funding (57.1%) and limited evidence (57.1%). When available by workplace (24.2%) or affiliated service (4.8%), doctors mostly administered acupuncture (66.7%). Respondents were not up to date with current research (71.4%). Referral likelihood increased with confidence in provider (80.0%), workplace availability (77.1%), and patient prior/current use (77.1%). Patient acupuncture discussions were rare (62.9%) with barriers of effectiveness uncertainty (71.4%) and limited knowledge of availability (57.1%). Conclusion: Despite available integrative services and acceptability by Australian palliative medicine practitioners, utilization is low. Further research into acupuncture effectiveness for palliative symptoms, feasibility, and patient acceptability is required. [ABSTRACT FROM AUTHOR]

Huge amounts of genes in single-cell RNA sequencing (scRNA-seq) data may influence the performance of data clustering. To obtain high-quality genes for data clustering, the study proposes a novel gene selection algorithm based on Fisher score and genetic algorithms with dynamic crossover (abbreviated as FDCGA). To reduce time and space complexity, FDCGA first employs Fisher score to gain the preliminary candidate genes and then utilizes genetic algorithms with dynamic crossover to select beneficial genes to data clustering and analysis. The experimental results conducted on several publicly real-world scRNA-seq datasets demonstrate that FDCGA outperforms the other several competitors in terms of both NMI and ARI metrics and possesses significant optimization performances. The experimental convergence shows that the fitness of FDCGA can increase and converge to a fixed state versus the number of iterations. The statistical analysis demonstrates that FDCGA statistically significantly outperforms the other competing methods. [ABSTRACT FROM AUTHOR]

The development of single cell RNA sequencing (scRNA-seq) has provided new perspectives to study biological problems at the single cell level. One of the key issues in scRNA-seq data analysis is to divide cells into several clusters for discovering the heterogeneity and diversity of cells. However, the existing scRNA-seq data are high-dimensional, sparse, and noisy, which challenges the existing single-cell clustering methods. In this study, we propose a joint learning framework (JLONMFSC) for clustering scRNA-seq data. In our method, the dimension of the original data is reduced to minimize the effect of noise. In addition, the graph regularized matrix factorization is used to learn the local features. Further, the Low-Rank Representation (LRR) subspace clustering is utilized to learn the global features. Finally, the joint learning of local features and global features is performed to obtain the results of clustering. We compare the proposed algorithm with eight state-of-the-art algorithms for clustering performance on six datasets, and the experimental results demonstrate that the JLONMFSC achieves better performance in all datasets. The code is avalable at https://github.com/lanbiolab/JLONMFSC. • Single-cell sequencing data clustering is critical for exploring the heterogeneity and diversity of unknown cell groups. • We propose a joint learning framework for single-cell data clustering based on the factorization of graph regularization matrix and low rank representation subspace clustering. • The experimental results show that our method outperformances than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objective. The goal was to investigate the connection between neuroinflammation in the brain and serum inflammatory markers as Alzheimer's disease progressed. We also sought to determine whether electroacupuncture had an effect on inflammatory markers found in blood and other brain regions. Methods. As an animal model for AD, we used senescence-accelerated mouse prone 8 (SAMP8) mice. To examine the effects and probable mechanism of electroacupuncture, we used HE staining, immunofluorescence staining, western blotting, and enzyme-linked immunosorbent assay. Results. Electroacupuncture therapy protected neurons, significantly downregulated the Iba-1 level in the hippocampus (p value was 0.003), frontal lobe cortex (p value was 0.042), and temporal lobe cortex (p value was 0.013) of the AD animal model, all of which had significantly lower levels of IL-6 (p value was 0.001), IL-1β (p value was 0.001), and TNF-α (p value was 0.001) in their serum. Conclusion. The amounts of IL-6, IL-1β, and TNF-α detected in the serum were strongly linked to the levels discovered in the hippocampus and the frontal lobes of the brain, respectively. A better understanding of the electroacupuncture process as well as the course of Alzheimer's disease and the therapeutic benefits of electroacupuncture may be gained by using biomarkers such as serum inflammatory marker biomarkers. [ABSTRACT FROM AUTHOR]

Objective: To determine the acceptability and feasibility of acupuncture for the treatment of endometriosis-related chronic pelvic pain. Design: A prospective, randomized controlled feasibility study. Setting: Outpatient setting in Sydney, Australia. Subjects: Participants who were aged 18–45 years, had a confirmed laparoscopic diagnosis of endometriosis in the past 5 years, and had regular menstrual periods and mean pelvic pain scores ≥4/10. Interventions: Sixteen acupuncture treatments delivered by registered acupuncturists using a standardized point protocol over 8 weeks, twice per week plus usual care compared with usual care alone. Outcome measures: Primary outcome measures were feasibility, safety, and acceptability of the acupuncture intervention. Secondary outcomes were changes in self-reported pelvic pain scores, changes in quality of life as measured by the Endometriosis Health Profile (EHP-30), changes in descending pain modulation, and changes in systemic inflammation (plasma interleukin [IL-6] concentrations). Results: Twenty-nine participants were eligible to participate, with 19 participants completing the trial. There was unequal withdrawals between groups; the acupuncture group had a withdrawal rate of 14% compared with 53% in usual care. Adverse events were uncommon (6.7%) and generally mild. A 1.9 point decrease in median nonmenstrual pain scores and a 2.0 decrease in median menstrual pain scores between baseline and end of trial were observed in the acupuncture group only. Improvements in all domains of the EHP-30 were seen in the acupuncture group, with no changes seen in usual care. There was no difference between baseline and end of treatment in IL-6 concentrations for either group. Conclusions: Acupuncture was an acceptable, well-tolerated treatment and it may reduce pelvic pain and improve quality of life; however, usual care was not an acceptable control group. Trial Registration: anzctr.org.au: ACTRN12617000053325. Prospectively registered January 11, 2017. [ABSTRACT FROM AUTHOR]