Multiple testing with the structure‐adaptive Benjamini–Hochberg algorithm

Molecular networking: An efficient tool for discovering and identifying natural products

Natural products (NPs), play a crucial role in drug development. However, the discovery of NPs is accidental, and conventional identification methods lack accuracy. To overcome these challenges, an increasing number of researchers are directing their attention to Molecular networking (MN). MN based on secondary mass spectrometry has become an important tool for the separation, purification and structural identification of NPs. However, most new tools are not well known. This review started with the most basic MN tool and explains it from the principle, workflow, and application. Then introduce the principles and workflows of the remaining eight new types of MN tools. The reliability of various MNs is mainly verified based on the application of phytochemistry and metabolomics. Subsequently, the principles and applications of 12 structural annotation tools are introduced. For the first time, the scope of 9 kinds of MN tools is compared horizontally, and 12 kinds of structured annotation tools are classified from the type of compound structure suitable for identification. The advantages and disadvantages of various tools are summarized, and make suggestions for future application directions and the development of computing tools in this review. MN tools are expected to enhance the efficiency of the discovery and identification in NPs.

Newborn apical resection preserves the proliferative capacity of cardiomyocytes located throughout the left ventricle

BACKGROUND

When pigs underwent apical resection (AR) on postnatal day (P) 1 (ARP1) followed by myocardial infarction (MI) on P28, the hearts had little evidence of scarring; meanwhile, hearts underwent MI on P28 without ARP1 showed large infarcts on P56; and the improvement of ARP1 hearts was driven primarily by cardiomyocyte proliferation. AR and MI were performed ~5 mm (AR) and ~20 mm (MI) above the heart apex; thus, we hypothesize that ARP1 preserved the cardiomyocytes cell-cycle throughout the left ventricle, rather than only near the resection site.

METHODS

Sections of cardiac tissue were collected from the left ventricle of uninjured pigs and from both the border zone (BZ) of AR and uninjured regions (remote zone, [RZ]) in ARP1 hearts. Cardiomyocyte proliferation was evaluated via immunofluorescence analysis of phosphorylated histone 3 [PH3] and symmetric Aurora B (sAuB). Single nucleus RNA sequencing (snRNAseq) data collected from the hearts of fetal pigs, uninjured pigs, and the BZ and RZ of ARP1 pigs was evaluated via our cell-cycle-specific autoencoder to identify proliferating cardiomyocytes.

RESULTS

Cardiomyocyte PH3 and sAuB expression, and percentage of proliferating cardiomyocytes in snRNA data was significantly more common in both BZ and RZ of ARP1 than uninjured hearts but did not differ significantly between the ARP1-BZ and ARP1-RZ at any time point. Heat shock proteins HSPA5 and HSP90B1 were overexpressed at both ARP1-BZ and ARP1-RZ. In AC16 cell, overexpression (and knockdown) of HSPA5-HSP90B1 increased (and decrease) cell-cycle activity.

CONCLUSION

ARP1 preserved proliferative capacity of cardiomyocytes located throughout the left ventricle.

Pan-cancer analysis of co-inhibitory molecules revealing their potential prognostic and clinical values in immunotherapy

Background

The widespread use of immune checkpoint inhibitors (anti-CTLA4 or PD-1) has opened a new chapter in tumor immunotherapy by providing long-term remission for patients. Unfortunately, however, these agents are not universally available and only a minority of patients respond to them. Therefore, there is an urgent need to develop novel therapeutic strategies targeting other co-inhibitory molecules. However, comprehensive information on the expression and prognostic value of co-inhibitory molecules, including co-inhibitory receptors and their ligands, in different cancers is not yet available.

Methods

We investigated the expression, correlation, and prognostic value of co-inhibitory molecules in different cancer types based on TCGA, UCSC Xena, TIMER, CellMiner datasets. We also examined the associations between the expression of these molecules and the extent of immune cell infiltration. Besides, we conducted a more in-depth study of VISTA.

Result

The results of differential expression analysis, correlation analysis, and drug sensitivity analysis suggest that CTLA4, PD-1, TIGIT, LAG3, TIM3, NRP1, VISTA, CD80, CD86, PD-L1, PD-L2, PVR, PVRL2, FGL1, LGALS9, HMGB1, SEMA4A, and VEGFA are associated with tumor prognosis and immune cell infiltration. Therefore, we believe that they are hopefully to serve as prognostic biomarkers for certain cancers. In addition, our analysis indicates that VISTA plays a complex role and its expression is related to TMB, MSI, cancer cell stemness, DNA/RNA methylation, and drug sensitivity.

Conclusions

These co-inhibitory molecules have the potential to serve as prognostic biomarkers and therapeutic targets for a broad spectrum of cancers, given their strong associations with key clinical metrics. Furthermore, the analysis results indicate that VISTA may represent a promising target for cancer therapy.

Buckwheat responds to co-exposure to PLA microplastics and Pb by regulating the synthesis of unsaturated fatty acids and jasmonates

Polylactic acid (PLA) microplastics (MPs) and lead (Pb) co-contamination, an emerging co-contamination, may profoundly impact plant growth. We aimed to evaluate the effects of PLA-MPs and Pb on buckwheat growth and physiology and to elucidate the underlying molecular mechanisms through an integrated transcriptomic and metabolomic approach. PLA-MPs alone reduced buckwheat biomass by 26.0 %, while combined exposure to Pb and PLA-MPs (PLA-Pb) further exacerbated morphological impairments, decreasing biomass by 43.1 % and 50.4 % compared to the control. Antioxidant enzyme activities increased under Pb and PLA-Pb treatments. The analysis revealed 536 differentially expressed metabolites (DEMs) and 3229 differentially expressed genes (DEGs) in PLA-Pb vs. control, 168 DEMs and 1555 DEGs in PLA-Pb vs. PLA, and 196 DEMs and 4057 DEGs in PLA-Pb vs. Pb. Key DEGs involved in lignin biosynthesis, including caffeoyl-CoA-O-methyltransferase, cinnamoyl-CoA reductase, and catechol-O-methyltransferase, were upregulated, suggesting that buckwheat mitigates toxicity by enhancing cell wall modification. Similarly, DEGs and DEMs linked to jasmonate biosynthesis were enriched in the alpha-linolenic acid metabolic pathway, including allene oxide synthase, allene oxide cyclase, and 12-oxophytodienoate reductase, as well as methyl jasmonate. These results suggest that buckwheat counters PLA-MPs-Pb toxicity by enhancing oxidative stress responses and upregulating the synthesis of lignin and unsaturated fatty acids. In conclusion, this study provides novel insights into the molecular mechanisms of plant detoxification under PLA-MPs-Pb co-exposure, highlighting the need for ecological risk assessment of combined microplastic and heavy metal pollution.

RNA-Binding Protein Signature in Proliferative Cardiomyocytes: A Cross-Species Meta-Analysis from Mouse, Pig, and Human Transcriptomic Profiling Data

In mammals, because cardiomyocytes withdraw from cell-cycle activities shortly after birth, the heart cannot repair the damage caused by a myocardial injury; thus, understanding how cardiomyocytes proliferate is among the most important topics in cardiovascular sciences. In newborn neonatal mammals, when a left ventricular injury is applied in hearts earlier than postnatal day 7, the cardiomyocytes actively proliferate and regenerate lost myocardium in the following weeks. The regulators promoting cardiomyocyte proliferation were discovered by analyzing transcriptomic data generated from models. Most of these regulators support the mRNA production of cell-cycle machinery, yet the mRNA requires translation into functional proteins under the regulation of RNA-binding proteins (RBPs). In this work, we performed a meta-analysis to study the relationship between RBP expression and cardiomyocyte proliferation. To identify RBPs associated with mouse and pig cardiomyocyte proliferation, the single-nuclei RNA sequencing (snRNA-seq) data from regenerating mouse and pig hearts were reanalyzed via an Autoencoder focusing on RBP expression. We also generated and analyzed new bulk RNA-seq from two human-induced pluripotent stem cell-derived (hiPSC) cardiomyocyte (hiPSC-CM) cell lines; the first cell line was harvested sixteen days after differentiation, when the cells still actively proliferated, and the second cell line was harvested one hundred and forty days after differentiation, when the cells ceased cell cycle activity. Then, the RBP associated with mouse, pig, and hiPSC-CM were compared across species. Twenty-one RBPs were found to be consistently upregulated, and six RBPs were downregulated in proliferating mouse, pig, and hiPSC-derived cardiomyocytes. Among upregulated RBPs across species, an immunofluorescence-based imaging analysis validated the significant increase in the proteins of DHX9, PTBP3, HNRNPUL1, and DDX6 in pig hearts with proliferating CMs. This meta-analysis in all species demonstrated a strong relationship between RBP expression and cardiomyocyte proliferation.

Pan-Cancer Upregulation of the FOXM1 Transcription Factor

BACKGROUND

The human FOXM1 transcription factor controls cell cycle progression and genome stability, and it has been correlated to the onset and progression of many tumor types.

METHODS

In our study, we collected all recent sequence and quantitative transcriptomics data about FOXM1, testing its presence across vertebrate evolution and its upregulation in cancer, both in bulk tissue contexts (by comparing the TCGA tumor dataset and the GTEx normal tissue dataset) and in single-cell contexts.

RESULTS

FOXM1 is significantly and consistently upregulated in all tested tumor types, as well as in tumor cells within a cancer microenvironment. Its upregulation reverberates in the upregulation of its target genes and can be used as a biomarker for poor cancer outcome in at least four tumor types.

CONCLUSIONS

Despite its lack of cancer-related mutations and amplifications, the recurring upregulation of FOXM1 in all tumors puts a focusing lens on this gene as a candidate pan-cancer master regulator.

Genetic Variation in Wheat Root Transcriptome Responses to Salinity: A Comparative Study of Tolerant and Sensitive Genotypes

Salt tolerance is a critical trait for plant survival and productivity in saline environments. Development of salt tolerant crops is a practical strategy for addressing soil salinity issues. In this study, RNA-Seq analysis was performed using two wheat cultivars with contrasting salt tolerance (Neixiang188, tolerant and Barra, sensitive) at 6 h and 24 h after salinity treatment to determine the genetic variations reflected in the RNA expression patterns and identify key genes associated with salt tolerance. Our results revealed that there were 2983 upregulated and 1091 downregulated differentially expressed genes (DEGs), which were found in common in the two accessions. Meanwhile, 529 salt tolerant associated DEGs were subjected to GO function annotation, KEGG enrichment, and protein-protein interaction (PPI) network prediction. Finally, a theoretical framework outlining the salt tolerance mechanisms of Neixiang188 was proposed. It can be inferred that Neixiang188 possesses superior ion homeostasis, ROS detoxification, and osmotic adjustment abilities compared to Barra when subjected to saline stress. The present research sheds light on the genetic foundation of salt tolerance in wheat and offers candidate genes for genetic manipulation. Our research insights enhance the comprehension of the molecular mechanisms underlying salt stress responses and could guide future breeding efforts for improving salt tolerance in crops.

Identification of crucial drought-tolerant genes of barley through comparative transcriptomic analysis and yeast-based stress assay

Drought is a persistent and serious threat to crop yield and quality. The identification and functional characterization of drought tolerance-related genes is thus vital for efforts to support the genetic improvement of drought-tolerant crops. Barley is highly adaptable and renowned for its robust stress resistance, making it an ideal subject for efforts to explore genes related to drought tolerance. In this study, two barley materials with different drought tolerance were subjected to soil drought treatment, including a variety with strong drought tolerance (Hindmarsh) and a genotype with weaker drought tolerance (XZ5). Transcriptomic sequencing data from the aboveground parts of these plants led to the identification of 1,206 differentially expressed genes associated with drought tolerance. These genes were upregulated in Hindmarsh following drought stress exposure but downregulated or unchanged in XZ5 under these same conditions, or were unchanged in Hindmarsh but downregulated in XZ5. Pathway enrichment analyses suggested that these genes are most closely associated with defense responses, signal recognition, photosynthesis, and the biosynthesis of various secondary metabolites. Using protein-protein interaction networks, the ankyrin repeat domain-containing protein 17-like isoform X2 was predicted to impact other drought tolerance-related protein targets in Hindmarsh. In MapMan metabolic pathway analyses, genes found to be associated with the maintenance of drought tolerance in Hindmarsh under adverse conditions were predicted to include genes involved in the abscisic acid, cytokinin, and gibberellin phytohormone signaling pathways, genes associated with redox homeostasis related to ascorbate and glutathione S-transferase, transporters including ABC and AAAP, transcription factors such as AP2/ERF and bHLH, the heat shock proteins HSP60 and HSP70, and the sucrose non-fermenting-1-related protein kinase. Heterologous HvSnRK2 (one of the identified genes, which encodes the sucrose non-fermenting-1-related protein kinase) gene expression in yeast conferred significant drought tolerance, highlighting the functional importance of this gene as one linked with drought tolerance. This study revealed the drought tolerance mechanism of Hindmarsh by comparing transcriptomes while also providing a set of candidate genes for genetic efforts to improve drought tolerance in this and other crop species.

Identification of the central tolerance checkpoint for autoreactive proteinase 3+ B cells in human bone marrow

Major target antigens of ANCA-associated vasculitis (AAV) are myeloperoxidase (MPO) and proteinase 3 (PR3). High-affinity MPO- and PR3-ANCA immunoglobulins are produced by antigen-experienced, class-switched autoreactive B cells. To prevent autoreactivity, B cells are subjected to several self-tolerance checkpoints, from the early immature stages in the bone marrow (BM), collectively called "central tolerance", to late mature stages, collectively called "peripheral tolerance"; the latter was recently elucidated for autoreactive PR3+ B cells. Here we investigated central tolerance controlling immature PR3+B cells in the BM before their migration into the periphery as transitional B cells. We applied an established flow cytometry-based method using labeled recombinant PR3 (rPR3) to identify the PR3+B cells to compare the phenotype of PR3+B cells in paired samples of BM mononuclear cells (BMMC) and peripheral blood mononuclear cells (PBMC) of non-vasculitis controls (No-AAV), and PBMC of patients with PR3-ANCA-associated vasculitis (PR3-AAV). We observed that the proportion of PR3+B cells within BMMC was higher (median [IQR]; 1.98 % [1.77-2.75]) than within PBMC of No-AAV (0.9 % [0.63-1.44], p < 0.01 by paired comparison) and similar to their proportion within PBMC of patients with PR3-AAV (1.82 % [1.66-3.21]; p > 0.05). Within CD24++CD38++ B cells, the subset of B cell migrating from BM to the periphery, BMMC contained a greater proportion of PR3+B cells as compared to PBMC in No-AAV (3.35 % [1.99-4.92] versus 1.23 % [0.62-1.55], p < 0.01), showing different surface markers of maturation (i.e. higher proportion of CD27-CD10+ and lower expression of CD21, IgD, IgM). Importantly, we observed a significant decline of the PR3+ fraction from the immature subset (IgD-IgM+; 2.80 % [1.23-4.02]) to the early transitional subset (IgD+IgM+; 1.76 % [0.96-2.68], p < 0.01) in BMMC, while no significant reduction was observed between the early transitional of BMMC and the transitional compartment of PBMC in No-AAV (1.26 % [0.62-1.56], p > 0.05). In conclusion, to prevent PR3-related autoimmunity, autoreactive PR3+B cells pass a stringent selection in the BM, and their removal by central tolerance checkpoint activity occurs mainly between T1-like/immature to T2-like/early transitional B cells of BMMC.

In silico exploration of phytochemicals as inhibitors for acute myeloid leukemia by targeting LIN28A gene: A cheminformatics study

BACKGROUND

Recent discoveries have illustrated that Lin28A is an oncogene in various cancers, particularly acute myeloid leukemia (AML). The upregulation of Lin28A can actively contribute to tumorigenesis and migration processes in multiple organs. Hence, the inhibition of Lin28A can be achieved by applying phytochemical herbals and targeting Lin28A protein using a computer-aided drug design (CAAD) approach.

METHODS

In this study, we comprehensively applied several bioinformatics tools, including gene ontologies, gene enrichment analysis, and protein-protein interactions (PPI), to determine the biological pathways, functional gene ontology, and biological pathway. Furthermore, we investigated a list of phytochemical herbs as a candidate drug by applying a computation technique involving molecular docking, density functional theory (DFT), molecular dynamics simulation (MDs), and pharmacokinetic and physiochemical properties by applying the SwissADME, pkCSM, and Molsoft LLC web-servers.

RESULTS

The Lin28A gene is related to two significant enrichment pathways, including proteoglycans in cancer and the pluripotency of stem cells through interactions with different genes such as MAPK12, MYC, MTOR, and PIK3CA. Interestingly, limonin, 18β Glycyrrhetic Acid, and baicalein have the highest binding energy scores of -8.4, -8.2, and -7.3 kcal/mol, respectively. The DFT study revealed that baicalein has a higher reactivity than limonin and 18β-Glycyrrhetic due to a small energy gap between LUMO and HUMO. Molecular dynamics simulation exhibited that baicalein complex with Lin28A protein is more stable than other complexes during simulation time due to low fluctuation with simulation periods as compared with other complexes, which indicated that baicalein was more fitting to docking and combining in the protein cave because of the largest number of H-bonds available for the docking simulation process. Furthermore, the drug-likeness and ADMET profiles revealed the activity of limonin, baicalein, and 18β-glycyrrhizic Acid, which possess significant inhibiting Lin28A proteins.

CONCLUSION

This study elucidated that baicalein, 18β-glycyrrhizic, and limonin may be applied as potential candidates for targeting Lin28A as an active oncogene for acute myeloid leukemia.

In Acute Severe Ulcerative Colitis Patients Who Receive Rescue Therapy, Prior Maintenance Therapy and Day 3 C-Reactive Protein After Rescue Therapy Are Associated With 12-Month Colectomy Risk

In steroid-refractory patients with acute severe ulcerative colitis, the number of advanced therapies prior to admission and day 3 C-reactive protein post–rescue therapy is associated with a higher risk of colectomy within 12 months.

Energy drink usage by students attending an online university

Objective: This study determined the frequency, reasons for, and side effects of energy drinks (ED) consumption among online students. Participants: Students attending an online university. Methods: Participants were recruited by email and completed a 59-question survey about their prior months ED practices using a combination of validated surveys previously published examining similar constructs in on-campus students. Results: 307 students (age = 32.4 ± 6.5yrs) completed the survey, and 88% reported consuming EDs. Students' reasons for consuming ED included school (p < .001), work (p < .001), an event/competition (p < .001), pick me up (p < .001), lack of rest (p < .001), more energy (p < .001), and staying awake while driving (p < .001). Only individuals who consumed >10 ED/month reported side effects of headaches (p = .01) and speeding (p = .01). Conclusions: In our sample, a majority of the participants reported consuming ED for various daily activities. Only frequent consumers reported side effects suggesting they had become habituated to caffeine since they drank EDs despite experiencing side effects.

False Discovery Rate Control for Lesion-Symptom Mapping With Heterogeneous Data via Weighted p-Values

Lesion-symptom mapping studies provide insight into what areas of the brain are involved in different aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is nonuniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Recent advancements in multiple testing methodologies allow researchers to weigh hypotheses according to side information (e.g., information on power heterogeneity). In this paper, we propose the use of p-value weighting for voxel-based lesion-symptom mapping studies. The weights are created using the distribution of lesion status and spatial information to estimate different non-null prior probabilities for each hypothesis test through some common approaches. We provide a monotone minimum weight criterion, which requires minimum a priori power information. Our methods are demonstrated on dependent simulated data and an aphasia study investigating which regions of the brain are associated with the severity of language impairment among stroke survivors. The results demonstrate that the proposed methods have robust error control and can increase power. Further, we showcase how weights can be used to identify regions that are inconclusive due to lack of power.

Maternal Undernutrition Affects Fetal Thymus DNA Methylation, Gene Expression, and, Thereby, Metabolism and Immunopoiesis in Wagyu (Japanese Black) Cattle

We aimed to determine the effects of maternal nutrient restriction (MNR) on the DNA methylation and gene expression patterns associated with metabolism and immunopoiesis in the thymuses of fetal Wagyu cattle. Pregnant cows were allocated to two groups: a low-nutrition (LN; 60% nutritional requirement; n = 5) and a high-nutrition (HN; 120% nutritional requirement, n = 6) group, until 8.5 months of gestation. Whole-genome bisulfite sequencing (WGBS) and RNA sequencing were used to analyze DNA methylation and gene expression, while capillary electrophoresis-Fourier transform mass spectrometry assessed the metabolome. WGBS identified 4566 hypomethylated and 4303 hypermethylated genes in the LN group, with the intergenic regions most frequently being methylated. Pathway analysis linked hypoDMGs to Ras signaling, while hyperDMGs were associated with Hippo signaling. RNA sequencing found 94 differentially expressed genes (66 upregulated, 28 downregulated) in the LN group. The upregulated genes were tied to metabolic pathways and oxidative phosphorylation; the downregulated genes were linked to natural killer cell cytotoxicity. Key overlapping genes (GRIA1, CACNA1D, SCL25A4) were involved in cAMP signaling. The metabolomic analysis indicated an altered amino acid metabolism in the MNR fetuses. These findings suggest that MNR affects DNA methylation, gene expression, and the amino acid metabolism, impacting immune system regulation during fetal thymus development in Wagyu cattle.

Proteomics approach to discovering non-invasive diagnostic biomarkers and understanding the pathogenesis of endometriosis: a systematic review and meta-analysis

BACKGROUND

Endometriosis is one of the most common gynaecological diseases, yet it lacks efficient biomarkers for early detection and unravels disease mechanisms. Proteomic profiling has revealed diverse patterns of protein changes in various clinical samples. Integrating and systematically analysing proteomics data can facilitate the development of biomarkers, expediting diagnosis and providing insights for potential clinical and therapeutic applications. Hence, this systematic review and meta-analysis aimed to explore potential non-invasive diagnostic biomarkers in various biological samples and therapeutic targets for endometriosis.

METHODS

Online databases, including Scopus, PubMed, Web of Science, MEDLINE, Embase via Ovid, and Google Scholar, were searched using MeSH terms. Two independent authors screened the articles, extracted the data, and assessed the methodological quality of the included studies. GO and KEGG analyses were performed to identify the pathways that were significantly enriched. Protein‑protein interaction and hub gene selection analyses were also conducted to identify biomarker networks for endometriosis.

RESULTS

Twenty-six observational studies with a total of 2,486 participants were included. A total of 644 differentially expressed proteins (180 upregulated and 464 downregulated) were identified from 9 studies. Proteins in peripheral blood exhibited a sensitivity and specificity of 38-100% and 59-99%, respectively, for detecting endometriosis, while proteins in urine had a sensitivity of 58-91% and specificity of 76-93%. Alpha-1-antitrypsin, albumin, and vitamin D binding proteins were significantly DEPs in both serum and urine. Complement C3 is commonly expressed in serum, menstrual blood, and cervical mucus. Additionally, S100-A8 is commonly expressed in both menstrual blood and cervical mucus. Haptoglobin is commonly detected in both serum and plasma, whereas cathepsin G is found in urine, serum, and plasma. GO and KEGG enrichment analyses revealed that proteoglycans in cancer pathways, which regulate cell-to-cell interactions, modulate the extracellular matrix, and promote the proliferation and invasion of endometrial cells, are commonly enriched in serum and urine.

CONCLUSION

This comprehensive study revealed potential proteomes that were significantly differentially expressed in women with endometriosis utilizing various non-invasive clinical samples. Exploring common differentially expressed proteins in various biological samples provides insights into the diagnosis and pathophysiology of endometriosis, as well as potential clinical and therapeutic applications.

Metagenomic sequencing of the skin microbiota of the scalp predicting the risk of surgical site infections following surgery of traumatic brain injury in sub-Saharan Africa

BACKGROUND

Surgical site infections (SSI) are a significant concern following traumatic brain injury (TBI) surgery and often stem from the skin's microbiota near the surgical site, allowing bacteria to penetrate deeper layers and potentially causing severe infections in the cranial cavity. This study investigated the relationship between scalp skin microbiota composition and the risk of SSI after TBI surgery in sub-Saharan Africa (SSA).

METHODS

This was a prospective cohort study, enrolling patients scheduled for TBI surgery. Sterile skin swabs were taken from the surrounding normal skin of the head and stored for analysis at -80°Celcius. Patients were monitored postoperatively for up to three months to detect any occurrences of SSI. 16S rRNA sequencing was used to analyze the skin microbiota composition, identifying different taxonomic microorganisms at the genus level. The analysis compared two groups: those who developed SSI and those who did not.

RESULTS

A total of 57 patients were included, mostly male (89.5%) with a mean age of 26.5 years, predominantly from urban areas in Uganda and victims of assault. Graphical visualization and metagenomic metrics analysis revealed differences in composition, richness, and evenness of skin microbiota within samples (α) or within the community (β), and showed specific taxa (phylum and genera) associated with either the group of SSI or the No SSI.

CONCLUSIONS

Metagenomic sequencing analysis uncovered several baseline findings and trends regarding the skin microbiome's relationship with SSI risk. There is an association between scalp microbiota composition (abundancy and diversity) and SSI occurrence following TBI surgery in SSA. We hypothesize under reserve that the scalp microbiota dysbiosis could potentially be an independent predictor of the occurrence of SSI; we advocate for further studies with larger cohorts.

Exploring Burnout among Nursing Students in Bangalore: A t-Distributed Stochastic Neighbor Embedding Analysis and Hierarchical Clustering in Cross-Sectional Data

This study explores burnout among nursing students in Bangalore, India, focusing on Exhaustion and Disengagement scores. A cross-sectional design was applied using the Oldenburg Burnout Inventory modified for nursing students, collecting data using a survey that was conducted between October and December 2023. The sample consisted of 237 female nursing students from the Bachelor of Science in Nursing program at Bangalore College of Nursing, South India. The study integrated the t-distributed Stochastic Neighbor Embedding (t-SNE) procedure for data simplification into three t-SNE components, used in a hierarchical clustering analysis, which identified distinct student profiles: "High-Intensity Study Group" and "Altruistic Aspirants". While burnout scores were generally high, students with high study hours ("High-Intensity Study Group") reported greater Exhaustion, with a mean score of 26.78 (SD = 5.26), compared to those in the "Altruistic Aspirants" group, who reported a mean score of 25.00 (SD = 4.48), demonstrating significant differences (p-value = 0.005). Conversely, those motivated by altruism ("Altruistic Aspirants") showed higher Disengagement, with a mean score of 19.78 (SD = 5.08), in contrast to "High-Intensity Study Group", which reported a lower mean of 17.84 (SD = 4.74) (p-value = 0.002). This segmentation suggests that burnout manifests differently depending on the students' academic load and intrinsic motivations. This study underscores the need for targeted interventions that address specific factors characterizing the clusters and provide information for designing future research and interventions. This study was not registered.

Lipidomics reveals ceramide biomarkers for detecting central precocious puberty in girls

BACKGROUND

Pubertal timing is modulated by complex interactions between the pituitary and gonadal sex steroid hormones. Evidence indicates that sphingolipids are involved in the biosynthesis of steroid hormones at multiple levels.

METHOD

This study recruited adolescent female patients from pubertal and pediatric endocrine clinics in Northern and Southern Taiwan from the Taiwan Puberty Longitudinal Study. A total of 112 plasma samples (22 healthy control, 29 peripheral precocious puberty (PPP), and 61 CPP samples) were collected. We extracted lipids from the plasma samples using the modified Folch method. The un-targeted ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) was employed for the lipid analysis.

RESULTS

We identified sphingolipid-linked metabolites, including Cer(18:0/15:0), Cer(18:1/16:0), and Cer(18:1/26:0) as candidate biomarkers for distinguishing girls with CPP from the control group by using an excellent discrimination model (AUC = 0.964). Moreover, Cer(18:0/22:0) and Cer(d18:0/18:1) were identified as potential biomarkers of PPP, with an AUC value of 0.938. Furthermore, CerP(18:1/18:0) was identified as the sole candidate biomarker capable of differentiating CPP from PPP.

CONCLUSIONS

The biomarkers identified in this study can facilitate the accurate detection of CPP in girls, provide insights into lipid-linked pathophysiology, and present a novel method of monitoring the progression of this disorder.

Cell-Cycle-Specific Autoencoding Improves Cluster Analysis of Cycling Cardiomyocytes

BACKGROUND

Our previous analyses of cardiomyocyte single-nucleus RNA sequencing (snRNAseq) data from the hearts of fetal pigs and pigs that underwent apical resection surgery on postnatal day (P) 1 (ARP1), myocardial infarction (MI) surgery on P28 (MIP28), both ARP1 and MIP28 (ARP1MIP28), or controls (no surgical procedure or CTL) identified 10 cardiomyocyte subpopulations (clusters), one of which appeared to be primed to proliferate in response to MI. However, the clusters composed of primarily proliferating cardiomyocytes still contained noncycling cells, and we were unable to distinguish between cardiomyocytes in different phases of the cell cycle. Here, we improved the precision of our assessments by conducting similar analyses with snRNAseq data for only the 1646 genes included under the Gene Ontology term "cell cycle."

METHODS

Two cardiac snRNAseq datasets, one from mice (GEO dataset number GSE130699) and one from pigs (GEO dataset number GSE185289), were evaluated via our cell-cycle-specific analytical pipeline. Cycling cells were identified via the co-expression of 5 proliferation markers (AURKB, MKI67, INCENP, CDCA8, and BIRC5).

RESULTS

The cell-cycle-specific autoencoder (CSA) algorithm identified 7 cardiomyocyte clusters in mouse hearts (mCM1 and mCM3-mCM8), including one prominent cluster of cycling cardiomyocytes in animals that underwent MI or Sham surgery on P1. Five cardiomyocyte clusters (pCM1, pCM3-pCM6) were identified in pig hearts, 2 of which (pCM1 and pCM4) displayed evidence of cell cycle activity; pCM4 was found primarily in hearts from fetal pigs, while pCM1 comprised a small proportion of cardiomyocytes in both fetal hearts and hearts from ARP1MIP28 pigs during the 2 weeks after MI induction, but was nearly undetectable in all other experimental groups and at all other time points. Furthermore, pseudotime trajectory analysis of snRNAseq data from fetal pig cardiomyocytes identified a pathway that began at pCM3, passed through pCM2, and ended at pCM1, whereas pCM3 was enriched for the expression of a cell cycle activator that regulates the G1/S phase transition (cyclin D2), pCM2 was enriched for an S-phase regulator (CCNE2), and pCM1 was enriched for the expression of a gene that regulates the G2M phase transition and mitosis (cyclin B2). We also identified 4 transcription factors (E2F8, FOXM1, GLI3, and RAD51) that were more abundantly expressed in cardiomyocytes from regenerative mouse hearts than from nonregenerative mouse hearts, from the hearts of fetal pigs than from CTL pig hearts, and from ARP1MIP28 pig hearts than from MIP28 pig hearts during the 2 weeks after MI induction.

CONCLUSIONS

The CSA algorithm improved the precision of our assessments of cell cycle activity in cardiomyocyte subpopulations and enabled us to identify a trajectory across 3 clusters that appeared to track the onset and progression of cell cycle activity in cardiomyocytes from fetal pigs.

Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy

The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype-phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10-3), and higher white blood cell count (p-value for heterogeneity = 8.5 × 10-131). These data suggest a more salutary effect of IL6R blockade for T2D among individuals of AFR vs EUR ancestry and provide data to inform ongoing clinical trials targeting IL6 for an expanding number of conditions. Moreover, the method to test for heterogeneity of associations can be applied broadly to other large-scale genotype-phenotype screens in diverse populations.

Comparative Transcriptome Analysis Reveals the Genes and Pathways Related to Wheat Root Hair Length

Tube-like outgrowths from root epidermal cells, known as root hairs, enhance water and nutrient absorption, facilitate microbial interactions, and contribute to plant anchorage by expanding the root surface area. Genetically regulated and strongly influenced by environmental conditions, longer root hairs generally enhance water and nutrient absorption, correlating with increased stress resistance. Wheat, a globally predominant crop pivotal for human nutrition, necessitates the identification of long root hair genotypes and their regulatory genes to enhance nutrient capture and yield potential. This study focused on 261 wheat samples of diverse genotypes during germination, revealing noticeable disparities in the length of the root hair among the genotypes. Notably, two long root hair genotypes (W106 and W136) and two short root hair genotypes (W90 and W100) were identified. Transcriptome sequencing resulted in the development of 12 root cDNA libraries, unveiling 1180 shared differentially expressed genes (DEGs). Further analyses, including GO function annotation, KEGG enrichment, MapMan metabolic pathway analysis, and protein-protein interaction (PPI) network prediction, underscored the upregulation of root hair length regulatory genes in the long root hair genotypes. These included genes are associated with GA and BA hormone signaling pathways, FRS/FRF and bHLH transcription factors, phenylpropanoid, lignin, lignan secondary metabolic pathways, the peroxidase gene for maintaining ROS steady state, and the ankyrin gene with diverse biological functions. This study contributes valuable insights into modulating the length of wheat root hair and identifies candidate genes for the genetic improvement of wheat root traits.

14-Week exercise training modifies the DNA methylation levels at gene sites in non-Alzheimer's disease women aged 50 to 70 years

Exercise training emerges as a key strategy in lifestyle modification, capable of reducing the risk of developing Alzheimer's disease (AD) due to risk factors such as age, family history, genetics and low level of education associated with AD. We aim to analyze the effect of a 14-week combined exercise training (CT) on the methylation of genes associated with AD in non-alzheimer's disease women. CT sessions lasted 60 min, occurring three times a week for 14 weeks. Forty non-Alzheimer's disease women aged 50 to 70 years (60.7 ± 4.1 years) with a mean height of 1.6 ± 0.1 m, mean weight of 73.12 ± 9.0 kg and a mean body mass index of 29.69 ± 3.5 kg/m2, underwent two physical assessments: pre and post the 14 weeks. DNA methylation assays utilized the EPIC Infinium Methylation BeadChip from Illumina. We observed that 14 weeks of CT led to reductions in systolic (p = 0.001) and diastolic (p = 0.017) blood pressure and improved motor skills post-intervention. Among 25 genes linked to AD, CT induced differentially methylated sites in 12 genes, predominantly showing hypomethylated sites (negative β values). Interestingly, despite hypomethylated sites, some genes exhibited hypermethylated sites (positive β values), such as ABCA7, BDNF, and WWOX. A 14-week CT regimen was adequate to induce differential methylation in 12 CE-related genes in healthy older women, alongside improvements in motor skills and blood pressure. In conclusion, this study suggest that combined training can be a strategy to improve physical fitness in older individuals, especially able to induce methylation alterations in genes sites related to development of AD. It is important to highlight that training should act as protective factor in older adults.

Single-cell RNA sequencing analysis identifies one subpopulation of endothelial cells that proliferates and another that undergoes the endothelial-mesenchymal transition in regenerating pig hearts

Background: In our previous work, we demonstrated that when newborn pigs undergo apical resection (AR) on postnatal day 1 (P1), the animals' hearts were completely recover from a myocardial infarction (MI) that occurs on postnatal day 28 (P28); single-nucleus RNA sequencing (snRNAseq) data suggested that this recovery was achieved by regeneration of pig cardiomyocyte subpopulations in response to MI. However, coronary vasculature also has a key role in promoting cardiac repair. Method: Thus, in this report, we used autoencoder algorithms to analyze snRNAseq data from endothelial cells (ECs) in the hearts of the same animals. Main results: Our results identified five EC clusters, three composed of vascular ECs (VEC1-3) and two containing lymphatic ECs (LEC1-2). Cells from VEC1 expressed elevated levels of each of five cell-cyclespecific markers (Aurora Kinase B [AURKB], Marker of Proliferation Ki-67 [MKI67], Inner Centromere Protein [INCENP], Survivin [BIRC5], and Borealin [CDCA8]), as well as a number of transcription factors that promote EC proliferation, while (VEC3 was enriched for genes that regulate intercellular junctions, participate in transforming growth factor β (TGFβ), bone morphogenic protein (BMP) signaling, and promote the endothelial mesenchymal transition (EndMT). The remaining VEC2 did not appear to participate directly in the angiogenic response to MI, but trajectory analyses indicated that it may serve as a reservoir for the generation of VEC1 and VEC3 ECs in response to MI. Notably, only the VEC3 cluster was more populous in regenerating (i.e., ARP1MIP28) than non-regenerating (i.e., MIP28) hearts during the 1-week period after MI induction, which suggests that further investigation of the VEC3 cluster could identify new targets for improving myocardial recovery after MI. Histological analysis of KI67 and EndMT marker PDGFRA demonstrated that while the expression of proliferation of endothelial cells was not significantly different, expression of EndMT markers was significantly higher among endothelial cells of ARP1MIP28 hearts compared to MIP28 hearts, which were consistent with snRNAseq analysis of clusters VEC1 and VEC3. Furthermore, upregulated secrete genes by VEC3 may promote cardiomyocyte proliferation via the Pi3k-Akt and ERBB signaling pathways, which directly contribute to cardiac muscle regeneration. Conclusion: In regenerative heart, endothelial cells may express EndMT markers, and this process could contribute to regeneration via a endothelial-cardiomyocyte crosstalk that supports cardiomyocyte proliferation.

Adjusting for gene-specific covariates to improve RNA-seq analysis

SUMMARY

This article suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by using two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey's q-value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning parameter through cross-validation that maximizes the expected number of hypotheses declared significant. A simulation study demonstrates that our method is comparable to or better than existing methods across realistic scenarios. In data analysis, we find support for our method's premise that the null probability varies with a gene-specific covariate variable.

AVAILABILITY AND IMPLEMENTATION

The source code repository is publicly available at https://github.com/hsjeon1217/conditional_method.

Weighted multiple testing procedures in genome-wide association studies

Multiple testing procedures controlling the false discovery rate (FDR) are increasingly used in the context of genome wide association studies (GWAS), and weighted multiple testing procedures that incorporate covariate information are efficient to improve the power to detect associations. In this work, we evaluate some recent weighted multiple testing procedures in the specific context of GWAS through a simulation study. We also present a new efficient procedure called wBHa that prioritizes the detection of genetic variants with low minor allele frequencies while maximizing the overall detection power. The results indicate good performance of our procedure compared to other weighted multiple testing procedures. In particular, in all simulated settings, wBHa tends to outperform other procedures in detecting rare variants while maintaining good overall power. The use of the different procedures is illustrated with a real dataset.

The cumulative effect of socioeconomic status and dyslexia on linguistic, cognitive and reading skills among Arabic-speaking children

Research has shown that children with dyslexia and children with a low socioeconomic status (SES) fall behind in terms of literacy acquisition, but a question remains regarding the cumulative effect of dyslexia and SES on linguistic, cognitive and reading skills. To examine the impact of cognition and environment on literacy development, we returned to the data set of 1,441 elementary school children (223 dyslexic readers and 1,241 typical readers) from low and medium-high SES backgrounds within Palestinian society in Israel who had participated in the development study of a comprehensive battery of tests in oral and written Arabic. The findings of this retrospective study reveal that, across grade levels, dyslexic readers from a low SES background showed similar performance to those from a medium-high SES background on most linguistic, cognitive and reading measures. As for typical readers, SES contributed to individual differences in all linguistic, cognitive and reading indices, with the exception of RAN. Finally, a cumulative effect of dyslexia and SES was found in relation to morphology, vocabulary, listening comprehension and text-reading accuracy.

PhyloMed: a phylogeny-based test of mediation effect in microbiome

Microbiome data from sequencing experiments contain the relative abundance of a large number of microbial taxa with their evolutionary relationships represented by a phylogenetic tree. The compositional and high-dimensional nature of the microbiome mediator challenges the validity of standard mediation analyses. We propose a phylogeny-based mediation analysis method called PhyloMed to address this challenge. Unlike existing methods that directly identify individual mediating taxa, PhyloMed discovers mediation signals by analyzing subcompositions defined on the phylogenic tree. PhyloMed produces well-calibrated mediation test p-values and yields substantially higher discovery power than existing methods.

Child-therapist interaction features impact Autism treatment response trajectories

BACKGROUND

Identifying mechanisms of change in Autism treatment may help explain response variability and maximize efficacy. For this, the child-therapist interaction could have a key role as stressed by developmental models of intervention, but still remains under-investigated.

AIMS

The longitudinal study of treatment response trajectories considering both baseline and child-therapist interaction features by means of predictive modeling.

METHODS AND PROCEDURES

N = 25 preschool children were monitored for one year during Naturalistic Developmental Behavioral Intervention. N = 100 video-recorded sessions were annotated with an observational coding system at four time points, to extract quantitative interaction features.

OUTCOMES AND RESULTS

Baseline and interaction variables were combined to predict response trajectories at one year, and achieved the best predictive performance. The baseline developmental gap, therapist's efficacy in child engagement, respecting children's timing after fast behavioral synchronization, and modulating the interplay to prevent child withdrawal emerged as key factors. Further, changes in interaction patterns in the early phase of the intervention were predictive of the overall response to treatment.

CONCLUSIONS AND IMPLICATIONS

Clinical implications are discussed, stressing the importance of promoting emotional self-regulation during intervention and the possible relevance of the first period of intervention for later response.

Blood pressure stratification using photoplethysmography and light gradient boosting machine

Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices. Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT. Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal. Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.

Brain structural abnormalities of the associative striatum in adolescents and young adults at genetic high-risk of schizophrenia: Implications for illness endophenotypes

OBJECTIVE

Dysfunction in cortico-striatal circuitry represents a core component of the pathophysiology in schizophrenia (SZ) but its potential as a candidate endophenotype of the illness is often confounded by neuroleptic medication.

METHODS

Accordingly, 26 adolescent and young adult participants at genetic high-risk for schizophrenia, but who were asymptomatic and neuroleptic naïve, and 28 age-matched controls underwent 1.5T structural magnetic resonance imaging of the striatum, manually parcellated into limbic (LST), associative (AST), and sensorimotor (SMST) functional subregions.

RESULTS

In relation to their age peers, participants at genetic high-risk for schizophrenia showed overall lower striatal gray matter volume with their most pronounced loss, bilaterally in the AST, but not the LST or SMST. Neuropsychological testing revealed reduced executive functioning for genetically at-risk participants, although the groups did not differ significantly in overall intelligence or oral reading. For controls but not for at-genetic high-risk participants, stronger executive functioning correlated with increased bilateral AST volume.

CONCLUSIONS

Reduced bilateral AST volume in genetic high-risk adolescents and young adults, accompanied by heritable loss of higher cognitive brain-behavior relationships, might serve as a useful endophenotype of SZ.

PNPase and RhlB Interact and Reduce the Cellular Availability of Oxidized RNA in Deinococcus radiodurans

8-Oxo-7,8-dihydroguanine (8-oxoG) is a major RNA modification caused by oxidative stresses and has been implicated in carcinogenesis, neurodegeneration, and aging. Several RNA-binding proteins have been shown to have a binding preference for 8-oxoG-modified RNA in eukaryotes and protect cells from oxidative stress. To date, polynucleotide phosphorylase (PNPase) is one of the most well-characterized proteins in bacteria that recognize 8-oxoG-modified RNA, but how PNPase cooperates with other proteins to process oxidized RNA is still unclear. Here, we use RNA affinity chromatography and mass spectrometry to search for proteins that preferably bind 8-oxoG-modified RNA in Deinococcus radiodurans, an extremophilic bacterium with extraordinary resistance to oxidative stresses. We identified four proteins that preferably bind to oxidized RNA: PNPase (DR_2063), DEAD box RNA helicase (DR_0335/RhlB), ribosomal protein S1 (DR_1983/RpsA), and transcriptional termination factor (DR_1338/Rho). Among these proteins, PNPase and RhlB exhibit high-affinity binding to 8-oxoG-modified RNA in a dose-independent manner. Deletions of PNPase and RhlB caused increased sensitivity of D. radiodurans to oxidative stress. We further showed that PNPase and RhlB specifically reduce the cellular availability of 8-oxoG-modified RNA but have no effect on oxidized DNA. Importantly, PNPase directly interacts with RhlB in D. radiodurans; however, no additional phenotypic effect was observed for the double deletion of pnp and rhlB compared to the single deletions. Overall, our findings suggest the roles of PNPase and RhlB in targeting 8-oxoG-modified RNAs and thereby constitute an important component of D. radiodurans resistance to oxidative stress. IMPORTANCE Oxidative RNA damage can be caused by oxidative stress, such as hydrogen peroxide, ionizing radiation, and antibiotic treatment. 8-oxo-7,8-dihydroguanine (8-oxoG), a major type of oxidized RNA, is highly mutagenic and participates in a variety of disease occurrences and development. Although several proteins have been identified to recognize 8-oxoG-modified RNA, the knowledge of how RNA oxidative damage is controlled largely remains unclear, especially in nonmodel organisms. In this study, we identified four RNA binding proteins that show higher binding affinity to 8-oxoG-modified RNA compared to unmodified RNA in the extremophilic bacterium Deinococcus radiodurans, which can endure high levels of oxidative stress. Two of the proteins, polynucleotide phosphorylase (PNPase) and DEAD-box RNA helicase (RhlB), interact with each other and reduce the cellular availability of 8-oxoG-modified RNA under oxidative stress. As such, this work contributes to our understanding of how RNA oxidation is influenced by RNA binding proteins in bacteria.

DETECTING MULTIPLE REPLICATING SIGNALS USING ADAPTIVE FILTERING PROCEDURES

Replicability is a fundamental quality of scientific discoveries: we are interested in those signals that are detectable in different laboratories, different populations, across time etc. Unlike meta-analysis which accounts for experimental variability but does not guarantee replicability, testing a partial conjunction (PC) null aims specifically to identify the signals that are discovered in multiple studies. In many contemporary applications, for example, comparing multiple high-throughput genetic experiments, a large number M of PC nulls need to be tested simultaneously, calling for a multiple comparisons correction. However, standard multiple testing adjustments on the M PC p -values can be severely conservative, especially when M is large and the signals are sparse. We introduce AdaFilter, a new multiple testing procedure that increases power by adaptively filtering out unlikely candidates of PC nulls. We prove that AdaFilter can control FWER and FDR as long as data across studies are independent, and has much higher power than other existing methods. We illustrate the application of AdaFilter with three examples: microarray studies of Duchenne muscular dystrophy, single-cell RNA sequencing of T cells in lung cancer tumors and GWAS for metabolomics.

Statistical analysis of spatially resolved transcriptomic data by incorporating multiomics auxiliary information

Effective control of false discovery rate is key for multiplicity problems. Here, we consider incorporating informative covariates from external datasets in the multiple testing procedure to boost statistical power while maintaining false discovery rate control. In particular, we focus on the statistical analysis of innovative high-dimensional spatial transcriptomic data while incorporating external multiomics data that provide distinct but complementary information to the detection of spatial expression patterns. We extend OrderShapeEM, an efficient covariate-assisted multiple testing procedure that incorporates one auxiliary study, to make it permissible to incorporate multiple external omics studies, to boost statistical power of spatial expression pattern detection. Specifically, we first use a recently proposed computationally efficient statistical analysis method, spatial pattern recognition via kernels, to produce the primary test statistics for spatial transcriptomic data. Afterwards, we construct the auxiliary covariate by combining information from multiple external omics studies, such as bulk and single-cell RNA-seq data using the Cauchy combination rule. Finally, we extend and implement the integrative analysis method OrderShapeEM on the primary P-values along with auxiliary data incorporating multiomics information for efficient covariate-assisted spatial expression analysis. We conduct a series of realistic simulations to evaluate the performance of our method with known ground truth. Four case studies in mouse olfactory bulb, mouse cerebellum, human breast cancer, and human heart tissues further demonstrate the substantial power gain of our method in detecting genes with spatial expression patterns compared to existing classic approaches that do not utilize any external information.

Cardiomyocyte Cell-Cycle Regulation in Neonatal Large Mammals: Single Nucleus RNA-Sequencing Data Analysis via an Artificial-Intelligence–Based Pipeline

Adult mammalian cardiomyocytes have very limited capacity to proliferate and repair the myocardial infarction. However, when apical resection (AR) was performed in pig hearts on postnatal day (P) 1 (AR_P1) and acute myocardial infarction (MI) was induced on P28 (MI_P28), the animals recovered with no evidence of myocardial scarring or decline in contractile performance. Furthermore, the repair process appeared to be driven by cardiomyocyte proliferation, but the regulatory molecules that govern the AR_P1-induced enhancement of myocardial recovery remain unclear. Single-nucleus RNA sequencing (snRNA-seq) data collected from fetal pig hearts and the hearts of pigs that underwent AR_P1, MI_P28, both AR_P1 and MI, or neither myocardial injury were evaluated via autoencoder, cluster analysis, sparse learning, and semisupervised learning. Ten clusters of cardiomyocytes (CM1–CM10) were identified across all experimental groups and time points. CM1 was only observed in AR_P1 hearts on P28 and was enriched for the expression of T-box transcription factors 5 and 20 (TBX5 and TBX20, respectively), Erb-B2 receptor tyrosine kinase 4 (ERBB4), and G Protein-Coupled Receptor Kinase 5 (GRK5), as well as genes associated with the proliferation and growth of cardiac muscle. CM1 cardiomyocytes also highly expressed genes for glycolysis while lowly expressed genes for adrenergic signaling, which suggested that CM1 were immature cardiomyocytes. Thus, we have identified a cluster of cardiomyocytes, CM1, in neonatal pig hearts that appeared to be generated in response to AR injury on P1 and may have been primed for activation of CM cell-cycle activation and proliferation by the upregulation of TBX5, TBX20, ERBB4, and GRK5.

Smoothed Nested Testing on Directed Acyclic Graphs

We consider the problem of multiple hypothesis testing when there is a logical nested structure to the hypotheses. When one hypothesis is nested inside another, the outer hypothesis must be false if the inner hypothesis is false. We model the nested structure as a directed acyclic graph, including chain and tree graphs as special cases. Each node in the graph is a hypothesis and rejecting a node requires also rejecting all of its ancestors. We propose a general framework for adjusting node-level test statistics using the known logical constraints. Within this framework, we study a smoothing procedure that combines each node with all of its descendants to form a more powerful statistic. We prove a broad class of smoothing strategies can be used with existing selection procedures to control the familywise error rate, false discovery exceedance rate, or false discovery rate, so long as the original test statistics are independent under the null. When the null statistics are not independent but are derived from positively-correlated normal observations, we prove control for all three error rates when the smoothing method is arithmetic averaging of the observations. Simulations and an application to a real biology dataset demonstrate that smoothing leads to substantial power gains.

A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills

The clinical success of the percutaneous coronary intervention (PCI) is highly dependent on endovascular manipulation skills and dexterous manipulation strategies of interventionalists. However, the analysis of endovascular manipulations and related discussion for technical skill assessment are limited. In this study, a multilayer and multimodal-fusion architecture is proposed to recognize six typical endovascular manipulations. The synchronously acquired multimodal motion signals from ten subjects are used as the inputs of the architecture independently. Six classification-based and two rule-based fusion algorithms are evaluated for performance comparisons. The recognition metrics under the determined architecture are further used to assess technical skills. The experimental results indicate that the proposed architecture can achieve the overall accuracy of 96.41%, much higher than that of a single-layer recognition architecture (92.85%). In addition, the multimodal fusion brings significant performance improvement in comparison with single-modal schemes. Furthermore, the K -means-based skill assessment can obtain an accuracy of 95% to cluster the attempts made by different skill-level groups. These hopeful results indicate the great possibility of the architecture to facilitate clinical skill assessment and skill learning.

Optimal false discovery rate control for large scale multiple testing with auxiliary information

Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the hypotheses, are available. Exploiting such auxiliary information can boost statistical power. To this end, we propose a framework based on a two-group mixture model with varying probabilities of being null for different hypotheses a priori, where a shape-constrained relationship is imposed between the auxiliary information and the prior probabilities of being null. An optimal rejection rule is designed to maximize the expected number of true positives when average false discovery rate is controlled. Focusing on the ordered structure, we develop a robust EM algorithm to estimate the prior probabilities of being null and the distribution of p-values under the alternative hypothesis simultaneously. We show that the proposed method has better power than state-of-the-art competitors while controlling the false discovery rate, both empirically and theoretically. Extensive simulations demonstrate the advantage of the proposed method. Datasets from genome-wide association studies are used to illustrate the new methodology.

Characterization of fin whale song off the Western Antarctic Peninsula

Song is produced by a variety of terrestrial and marine animals and is particularly common among baleen whales. Fin whale (Balaenoptera physalus) song is comprised of relatively simple 20 Hz pulses produced at regular intervals. The timing of these intervals, in addition to the presence and frequency of overtones, appears to be unique to each population. The purpose of this study was to characterize Western Antarctic Peninsula fin whale song and describe temporal pattern variations in song type and occurrence. Recordings were collected in the area from 2001-2004 and again 2014-2016. One song type was identified with a primary inter-pulse interval (IPI) of approximately 14 s and secondary IPI of 12.5 s. This song occurred in three pattern variants: singlet, doublet, and long triplet. The interval between pulses increased by 1.5 s between recording periods while the frequency of the overtones decreased from 89 Hz to 86 Hz. Song was never recorded in August and while it was recorded at other times in some years, it was consistently present in recordings from April through June across all years. While multiple pattern variants were present each year, singlets were generally the most prevalent variant. Doublets and triplets occurred from February through June, with highest levels of variants in February. In later years the triplet variant presence increased and in 2016 it comprised 53% of recorded song bouts. Further research is needed to understand the reasons why song changes over time and to examine the feasibility of using song to delineate and identify populations.

Facial and Vocal Markers of Schizophrenia Measured Using Remote Smartphone Assessments: Observational Study

Background

Machine learning–based facial and vocal measurements have demonstrated relationships with schizophrenia diagnosis and severity. Demonstrating utility and validity of remote and automated assessments conducted outside of controlled experimental or clinical settings can facilitate scaling such measurement tools to aid in risk assessment and tracking of treatment response in populations that are difficult to engage.

Objective

This study aimed to determine the accuracy of machine learning–based facial and vocal measurements acquired through automated assessments conducted remotely through smartphones.

Methods

Measurements of facial and vocal characteristics including facial expressivity, vocal acoustics, and speech prevalence were assessed in 20 patients with schizophrenia over the course of 2 weeks in response to two classes of prompts previously utilized in experimental laboratory assessments: evoked prompts, where subjects are guided to produce specific facial expressions and speech; and spontaneous prompts, where subjects are presented stimuli in the form of emotionally evocative imagery and asked to freely respond. Facial and vocal measurements were assessed in relation to schizophrenia symptom severity using the Positive and Negative Syndrome Scale.

Results

Vocal markers including speech prevalence, vocal jitter, fundamental frequency, and vocal intensity demonstrated specificity as markers of negative symptom severity, while measurement of facial expressivity demonstrated itself as a robust marker of overall schizophrenia symptom severity.

Conclusions

Established facial and vocal measurements, collected remotely in schizophrenia patients via smartphones in response to automated task prompts, demonstrated accuracy as markers of schizophrenia symptom severity. Clinical implications are discussed.

Covariate adaptive familywise error rate control for genome-wide association studies

The familywise error rate has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focused on false discovery rate control, while covariate-adaptive procedures controlling the familywise error rate remain underdeveloped. Here, we propose a novel covariate-adaptive procedure to control the familywise error rate that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyse 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.

Circulating autoreactive proteinase 3+ B cells and tolerance checkpoints in ANCA-associated vasculitis

BACKGROUND

Little is known about the autoreactive B cells in antineutrophil cytoplasmic antibody–associated (ANCA-associated) vasculitis (AAV). We aimed to investigate tolerance checkpoints of circulating antigen-specific proteinase 3–reactive (PR3⁺) B cells.

METHODS

Multicolor flow cytometry in combination with bioinformatics and functional in vitro studies were performed on baseline samples of PBMCs from 154 well-characterized participants of the RAVE trial (NCT00104299) with severely active PR3-AAV and myeloperoxidase-AAV (MPO-AAV) and 27 healthy controls (HCs). Clinical data and outcomes from the trial were correlated with PR3⁺ B cells (total and subsets).

RESULTS

The frequency of PR3⁺ B cells among circulating B cells was higher in participants with PR3-AAV (4.77% median [IQR, 3.98%–6.01%]) than in participants with MPO-AAV (3.16% median [IQR, 2.51%–5.22%]) and participants with AAV compared with HCs (1.67% median [IQR, 1.27%–2.16%], P < 0.001 for all comparisons), implying a defective central tolerance checkpoint in patients with AAV. Only PBMCs from participants with PR3-AAV contained PR3⁺ B cells capable of secreting PR3-ANCA IgG in vitro, proving they were functionally distinct from those of participants with MPO-AAV and HCs. Unsupervised clustering identified subtle subsets of atypical autoreactive PR3⁺ memory B cells accumulating through the maturation process in patients with PR3-AAV. PR3⁺ B cells were enriched in the memory B cell compartment of participants with PR3-AAV and were associated with higher serum CXCL13 levels, suggesting an increased germinal center activity. PR3⁺ B cells correlated with systemic inflammation (C-reactive protein and erythrocyte sedimentation rate, P < 0.05) and complete remission (P < 0.001).

CONCLUSION

This study suggests the presence of defective central antigen-independent and peripheral antigen-dependent checkpoints in patients with PR3-AAV, elucidating the selection process of autoreactive B cells.

Trial registration

ClinicalTrials.gov NCT00104299.

Funding

The Vasculitis Foundation, the National Institute of Allergy and Infectious Diseases of the NIH, and the Mayo Foundation for Education and Research.

Supporting Risk-Aware Use of Online Translation Tools in Delivering Mental Healthcare Services among Spanish-Speaking Populations

Neural machine translation technologies are having increasing applications in clinical and healthcare settings. In multicultural countries, automatic translation tools provide critical support to medical and health professionals in their interaction and exchange of health messages with migrant patients with limited or non-English proficiency. While research has mainly explored the usability and limitations of state-of-the-art machine translation tools in the detection and diagnosis of physical diseases and conditions, there is a persistent lack of evidence-based studies on the applicability of machine translation tools in the delivery of mental healthcare services for vulnerable populations. Our study developed Bayesian machine learning algorithms using relevance vector machine to support frontline health workers and medical professionals to make better informed decisions between risks and convenience of using online translation tools when delivering mental healthcare services to Spanish-speaking minority populations living in English-speaking countries. Major strengths of the machine learning classifier that we developed include scalability, interpretability, and adaptability of the classifier for diverse mental healthcare settings. In this paper, we report on the process of the Bayesian machine learning classifier development through automatic feature optimisation and the interpretation of the classifier-enabled assessment of the suitability of original English mental health information for automatic online translation. We elaborate on the interpretation of the assessment results in clinical settings using statistical tools such as positive likelihood ratios and negative likelihood ratios.