Exploring Guolin Qigong (Mind-Body Exercise) for Improving Cancer Related Fatigue in Cancer Survivors: A Mixed Method Randomized Controlled Trial Protocol.

BACKGROUND: Cancer-related fatigue and its associated symptoms of sleep disorder and depression are prevalent in cancer survivors especially among breast, lung, and colorectal cancer survivors. While there is no gold standard for treating cancer-related fatigue currently, studies of mind-body exercises such as Qigong have reported promise in reducing symptoms. This study was designed to evaluate the feasibility and effect of Guolin Qigong on cancer-related fatigue and other symptoms in breast, lung and colorectal cancer survivors while exploring their perceptions and experiences of Guolin Qigong intervention. METHODS: This is an open-label randomized controlled trial with 60 participants divided into 2 study groups in a 1:1 ratio. The intervention group will receive 12 weeks of Guolin Qigong intervention with a 4-week follow-up while control will receive usual care under waitlist. The primary outcome will be feasibility measured based on recruitment and retention rates, class attendance, home practice adherence, nature, and quantum of missing data as well as safety. The secondary subjective outcomes of fatigue, sleep quality and depression will be measured at Week-1 (baseline), Week-6 (mid-intervention), Week-12 (post-intervention), and Week-16 (4 weeks post-intervention) while an objective 24-hour urine cortisol will be measured at Week-1 (baseline) and Week-12 (post-intervention). We will conduct a semi-structured interview individually with participants within 3 months after Week-16 (4 weeks post-intervention) to obtain a more comprehensive view of practice adherence. DISCUSSION: This is the first mixed-method study to investigate the feasibility and effect of Guolin Qigong on breast, lung, and colorectal cancer survivors to provide a comprehensive understanding of Guolin Qigong's intervention impact and participants' perspectives. The interdisciplinary collaboration between Western Medicine and Chinese Medicine expertise of this study ensures robust study design, enhanced participant care, rigorous data analysis, and meaningful interpretation of results. This innovative research contributes to the field of oncology and may guide future evidence-based mind-body interventions to improve cancer survivorship. TRIAL REGISTRATION: This study has been registered with ANZCTR (ACTRN12622000688785p), was approved by Medical Research Ethic Committee of University Malaya Medical Centre (MREC ID NO: 2022323-11092) and recognized by Western Sydney University Human Research Ethics Committee (RH15124).

Human-like adrenal features in Chinese tree shrews revealed by multi-omics analysis of adrenal cell populations and steroid synthesis.

The Chinese tree shrew ( Tupaia belangeri chinensis) has emerged as a promising model for investigating adrenal steroid synthesis, but it is unclear whether the same cells produce steroid hormones and whether their production is regulated in the same way as in humans. Here, we comprehensively mapped the cell types and pathways of steroid metabolism in the adrenal gland of Chinese tree shrews using single-cell RNA sequencing, spatial transcriptome analysis, mass spectrometry, and immunohistochemistry. We compared the transcriptomes of various adrenal cell types across tree shrews, humans, macaques, and mice. Results showed that tree shrew adrenal glands expressed many of the same key enzymes for steroid synthesis as humans, including CYP11B2, CYP11B1, CYB5A, and CHGA. Biochemical analysis confirmed the production of aldosterone, cortisol, and dehydroepiandrosterone but not dehydroepiandrosterone sulfate in the tree shrew adrenal glands. Furthermore, genes in adrenal cell types in tree shrews were correlated with genetic risk factors for polycystic ovary syndrome, primary aldosteronism, hypertension, and related disorders in humans based on genome-wide association studies. Overall, this study suggests that the adrenal glands of Chinese tree shrews may consist of closely related cell populations with functional similarity to those of the human adrenal gland. Our comprehensive results (publicly available at http://gxmujyzmolab.cn:16245/scAGMap/) should facilitate the advancement of this animal model for the investigation of adrenal gland disorders.

Identification of cancer driver genes based on hierarchical weak consensus model.

Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-024-00279-6.

AGImpute: imputation of scRNA-seq data based on a hybrid GAN with dropouts identification.

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.

Baduanjin Mind-Body Exercise for Cancer-Related Fatigue: Protocol for a Remotely Delivered Randomized Wait-List Controlled Feasibility Study.

BACKGROUND: People living with a cancer diagnosis often experience cancer-related fatigue (CRF). Between 9% and 45% of people report CRF as moderate to severe, negatively impacting their quality-of-life (QOL). The evidence-base for managing CRF recommends exercise-related therapies over pharmaceutical interventions. One such exercise-like therapy is Baduanjin mind-body exercise (MBE), which has additional benefits. A remotely delivered program may further benefit people with CRF. The primary objective of this pilot will test study feasibility of a remotely delivered Baduanjin MBE exercise program for people living with CRF. METHODS: This is a randomized wait-list controlled pilot study and will take place in Sydney, Australia. Subject to informed consent, 40 adults with moderate CRF levels and receiving or previously received adjuvant chemotherapy, will undertake a home-based 8-week Baduanjin MBE program supported by online resources and instructors. The primary feasibility outcomes are recruitment, enrollment, retention, and adherence rates; and safety as measured by tolerance and adverse-event frequency. Clinical outcomes (eg, changes in CRF, QOL, and participant perceptions) are assessed at pre-intervention, week 1, week 4, week 8, and post-intervention. Analyses follows the Intent-to-Treat (all participants as per randomization) and per-protocol (participants adhering to the protocol). Missing data will be imputed from previous data entries and regression models may be tested to predict missing outcomes. DISCUSSION: To our knowledge, this is the first study evaluating the feasibility and effects of Baduanjin MBE on CRF using a remote delivery method. These feasibility data will inform a fully powered future trial investigating evidence of effect on CRF and QOL.Trial registration: Australian and New Zealand Clinical Trials Registry (ANZCTR 12623000177651).Ringgold ID: 651498 Chinese Medicine Centre.

Are Published Cancer Care Trial Protocols With Traditional Chinese Medicine Interventions Concordant With SPIRIT-TCM Extension 2018? A Scoping Review on Published Trial Protocols Between 2019 and 2022.

BACKGROUND: The SPIRIT-TCM Extension 2018 was created to guide the design and reporting of Traditional Chinese Medicine (TCM) clinical trial protocols. This study aims to investigate the extent of concordance with this guideline in the relevant field of cancer care research. METHODS: A scoping review of TCM cancer trial protocols published in English and Chinese since January 2019 was conducted. Five major academic databases (MEDLINE, EMBASE, CINAHL, CENTRAL, and China National Knowledge Infrastructure) were searched. Concordance with the SPIRIT-TCM Extension 2018 was assessed by descriptive analysis. RESULTS: Fifty-three TCM cancer care trial protocols were identified, comprising 23 acupuncture, 26 Chinese herbal medicine (CHM), and 4 Tai Chi/Qigong (TCQ) interventions. The majority of the checklist items had a low rate of concordance, especially in the reporting of quality control and safety, dosage, TCM diagnostic patterns, possible interactions between Western Medicine and TCM interventions, and TCM-related outcome assessments. CONCLUSIONS: Although the SPIRIT-TCM Extension 2018 guideline was established through extensive Delphi consultation, there are low rates of concordance between published TCM cancer care clinical trial protocols with the guideline. Further research is necessary to understand the low rate of concordance and how scientific rigors of reporting can be improved in TCM cancer care research.

Community- and Program-Level Predictors of Funding Streams Used by Coordinated Specialty Care Programs.

OBJECTIVE: Although coordinated specialty care (CSC) is an effective service model to address first-episode psychosis, CSC is not widely accessible in the United States, and funding for this service model often remains challenging. The authors examined whether community- or program-level factors predict the use of public and private funding streams in a national sample of 34 CSC programs in 22 U.S. states and territories. METHODS: As part of a larger mixed-methods study, CSC program leaders completed a brief questionnaire regarding funding sources. Statistical modeling was used to examine program- and community-level predictors of the use of funding sources. RESULTS: Most CSC programs (20 of 34, 59%) reported that Mental Health Block Grant (MHBG) set-aside funds accounted for more than half of their total funding, and 11 of these programs reported that these funds contributed to >75% of their funding. Programs ≤5 years old were more likely to rely on MHBG set-aside funds. Programs in Medicaid expansion states were more likely to rely on Medicaid funding than programs in nonexpansion states. Programs in higher-income service catchment areas used more state funds than did those in lower-income areas, and among programs in lower-income service catchment areas, those that were >4 years old were more likely than those ≤4 years old to rely on state funds other than Medicaid. CONCLUSIONS: CSC programs remain largely dependent on MHBG set-aside funding. Some programs have diversified their funding streams, most notably by including more Medicaid and other state funding. A more comprehensive funding approach is needed to reduce reliance on the MHBG set-aside funds.

JLONMFSC: Clustering scRNA-seq data based on joint learning of non-negative matrix factorization and subspace clustering.

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.

Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.

Essential proteins play a vital role in development and reproduction of cells. The identification of essential proteins helps to understand the basic survival of cells. Due to time-consuming, costly and inefficient with biological experimental methods for discovering essential proteins, computational methods have gained increasing attention. In the initial stage, essential proteins are mainly identified by the centralities based on protein-protein interaction (PPI) networks, which limit their identification rate due to many false positives in PPI networks. In this study, a purified PPI network is firstly introduced to reduce the impact of false positives in the PPI network. Secondly, by analyzing the similarity relationship between a protein and its neighbors in the PPI network, a new centrality called neighborhood similarity centrality (NSC) is proposed. Thirdly, based on the subcellular localization and orthologous data, the protein subcellular localization score and ortholog score are calculated, respectively. Fourthly, by analyzing a large number of methods based on multi-feature fusion, it is found that there is a special relationship among features, which is called dominance relationship, then, a novel model based on dominance relationship is proposed. Finally, NSC, subcellular localization score, and ortholog score are fused by the dominance relationship model, and a new method called NSO is proposed. In order to verify the performance of NSO, the seven representative methods (ION, NCCO, E_POC, SON, JDC, PeC, WDC) are compared on yeast datasets. The experimental results show that the NSO method has higher identification rate than other methods.

Characteristics of cancer-related fatigue and its correlation with anxiety, depression, and stress-related hormones among Chinese cancer survivors: a cross-sectional study.

Background: Fatigue is a common source of distress for cancer survivors. The severity of cancer-related fatigue varies significantly, which may be due to individual differences in host factors. Aim: This cross-sectional study aims to explore how demographic, oncological, sociological, psychological, and stress-related hormones levels interact to influence the distinct experiences of fatigue (Cancer-related fatigue [CRF] occurrence and fatigue degree). Methods: A cross-sectional study carried out at the oncology outpatient and ward department of Xiyuan Hospital of China Academy of Chinese Medical Sciences recruited 306 cancer patients between January 2021 to December 2021. General information, fatigue, psychological factors was evaluated by general information questionnaire, the Revised Piper's Fatigue Scale-Chinese Version (RPFS-CV), and the self-report Hospital Anxiety and Depression Scale (HADS). Stress-related hormones were measured with chemiluminescent enzyme immunoassay (Zhengzhou Antobio). Results: 306 patients were included, 229 (74.8%) were diagnosed with CRF, including 94 (41.0%) with mild fatigue, 121 (52.8%) with moderate fatigue, and 14 (6.1%) with severe fatigue. Multivariate regression analysis showed that higher depression scores, aldosterone levels may increase the risk of CRF. Patients who are obese (Body mass index ≥ 28 kg/m2) may help to reduce the risk of CRF. Other contributing factors for increased levels of fatigue (p< 0.05) include being female, having anxiety, depression and high aldosterone levels. Conclusion: The research suggested that CRF was a common symptom in cancer survivors and pay attention to these influencing factors may help to better identify patients susceptible to fatigue and provide long-term, targeted interventions.

ALKBH5-mediated m6A demethylation of HS3ST3B1-IT1 prevents osteoarthritis progression.

HS3ST3B1-IT1 was identified as a downregulated long noncoding RNA in osteoarthritic cartilage. However, its roles and mechanisms in the pathogenesis of osteoarthritis (OA) are unclear. In this study, we demonstrated that the expressions of HS3ST3B1-IT1 and its maternal gene HS3ST3B1 were downregulated and positively correlated in osteoarthritic cartilage. Overexpression of HS3ST3B1-IT1 significantly increased chondrocyte viability, inhibited chondrocyte apoptosis, and upregulated extracellular matrix (ECM) proteins, whereas HS3ST3B1-IT1 knockdown had the opposite effects. In addition, HS3ST3B1-IT1 significantly ameliorated monosodium-iodoacetate-induced OA in vivo. Mechanistically, HS3ST3B1-IT1 upregulated HS3ST3B1 expression by blocking its ubiquitination-mediated degradation. Knockdown of HS3ST3B1 reversed the effects of HS3ST3B1-IT1 on chondrocyte viability, apoptosis, and ECM metabolism. AlkB homolog 5 (ALKBH5)-mediated N6-methyladenosine (m6A) demethylation stabilized HS3ST3B1-IT1 RNA. Together, our data revealed that ALKBH5-mediated upregulation of HS3ST3B1-IT1 suppressed OA progression by elevating HS3ST3B1 expression, suggesting that HS3ST3B1-IT1/HS3ST3B1 may serve as potential therapeutic targets for OA treatment.

A Gene Correlation Measurement Method for Spatial Transcriptome Data Based on Partitioning and Distribution.

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.

Perceptions of Australian Palliative Medicine Specialists Toward Acupuncture for Palliative Care.

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.

The effect of incisional negative pressure wound therapy on the improvement of postoperative cosmetic suture wounds and scar hyperplasia.

This study aimed to explore the effects of early incisional negative pressure wound therapy (INPWT) on cosmetic suture wounds and postoperative scar hyperplasia. We retrospectively evaluated 120 patients who underwent abdominoperineal resection at the Changhai Hospital between February 2018 and October 2021 and divided them into two groups according to their treatment: the INPWT group (n = 60) and the control group (n = 60). The quality of post-surgical wound healing in the two groups was evaluated. The Patient Scar Assessment Scale (PSAS), the Vancouver Scar Scale (VSS), and the visual analogue scale (VAS) were used to evaluate the surgical incision scar at 1-year follow-up. At this follow-up visit, 115 patients underwent reexamination; five patients were lost to follow-up, including two patients in the INPWT group and three patients in the control group. The INPWT group showed better wound healing than the control group (P < .05). The proportion of patients who received INPWT was significantly higher in the non-surgical site infection (SSI) group than in the SSI group (P < .05). The PSAS, VSS and VAS scores were significantly improved in the INPWT group compared with those in the control group (P < .05). Our results show that INPWT improved the quality of cosmetic suture wounds and reduced the degree of postoperative scar hyperplasia.

Metasurfaces enabled polarization-multiplexing heralded single photon imaging.

Quantum imaging has non-negligible advantages in terms of sensitivity, signal-to-noise ratio, and novel imaging schemes. Based on metasurfaces, the information density and stability of the quantum imaging system can be further improved. Here we experimentally demonstrate that two patterns, simultaneously and independently superimposed on a high-efficiency dielectric metasurface, can be remotely switched via polarization-entangled photon pairs. Furthermore, using the time-correlated property of entangled photon pairs, the information carried by quantum light can be remarkably discriminated from background noise. This work confirms that the phase manipulation of quantum light with metasurfaces has a huge potential in the field of quantum imaging, quantum state tomography, and also promises real-world quantum metasurface devices.

State Mental Health Authority Level of Involvement in Coordinated Specialty Care Clinics and Client Outcomes.

OBJECTIVE: State mental health authorities (SMHAs) in all U.S. states and territories administer the Mental Health Block Grant (MHBG) set-aside funding for first-episode psychosis. Funds support implementation of coordinated specialty care (CSC) programs. The authors investigated the relationship between the level of SMHA involvement with CSC programs and clinical outcomes of clients in these programs. METHODS: As part of a mixed-methods study of 34 CSC programs, SMHAs from 21 states and one U.S. territory associated with the 34 CSC programs participated in a 1-hour interview (between November 2018 and May 2019) focused on SMHA involvement in administration of MHBG set-aside funds and the SMHA's ongoing relationship with funded CSC programs. SMHA involvement was rated on a scale of 1 to 5, with 5 indicating the highest involvement. Client outcome data were collected at the 34 study sites over an 18-month period. Multilevel random-effect modeling was used, controlling for response propensity (propensity score), client demographic variables, and program-level covariates (i.e., fidelity score, staff turnover rates, service area urbanicity, and number of clients enrolled). RESULTS: Clients in CSC programs with SMHAs that were the most involved (level 5) had significantly improved symptoms, social functioning, and role functioning, compared with clients in programs with which SMHAs were least involved (level 1). CONCLUSIONS: The findings suggest that increased SMHA involvement in CSC programs is relevant for positive client outcomes. Levels of first-episode psychosis funding doubled in 2021 and 2022, and it is important to identify how SMHAs affect the success of CSC programs and the individuals served.

Effects of Electroacupuncture on the Correlation between Serum and Central Immunity in AD Model Animals.

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-1ß (p value was 0.001), and TNF-α (p value was 0.001) in their serum. Conclusion: The amounts of IL-6, IL-1ß, 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.

Establishing construct validity of neuropsychological tests in cancer survivors.

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.

Dimensionality Reduction of Single-Cell RNA Sequencing Data by Combining Entropy and Denoising AutoEncoder.

ABSTRACT 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.

Client Racial Composition in First-Episode Psychosis Programs Compared With Compositions in Program Service Areas.

OBJECTIVE: The authors examined the extent to which clients served by first-episode psychosis programs reflected the racial composition of the surrounding service area and, to the extent that they did not, explored possible explanatory factors. METHODS: As part of a national study of coordinated specialty care (CSC) sites in the United States, 35 programs documented race for 772 clients. Programs identified a geographic service area for their clients. Using Census data, the authors identified the proportion of clients in this service area who were Black and then examined the extent of disproportionality, calculated as a risk ratio and as a relative difference in racial composition between CSC programs and their service areas. RESULTS: Overall, 71% of CSC programs had a disproportionately greater proportion of Black clients than Black residents within the service area. This disproportionality was still evident after conducting sensitivity analyses that included adjusting for sampling error in the service area population estimates; however, smaller study sites displayed greater fluctuations in disproportionality in the sensitivity analyses. CONCLUSIONS: Using data from diverse CSC programs, the authors illustrate that the odds of Blacks receiving services through a CSC program are much higher than would be expected on the basis of the population living in the area being served by the program. Multiple reasons may explain this finding, but in the absence of clear explanatory factors, this result may be ripe for discussion and further investigation.