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Abstract Background Recently, microarray data analyses using functional pathway information, e.g., gene set enrichment analysis (GSEA) and significance analysis of function and expression (SAFE), have gained recognition as a way to identify biological pathways/processes associated with a phenotypic endpoint. In these analyses, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then these gene-specific local statistics are combined to evaluate association for pre-selected sets of genes. Commonly used local statistics include t-statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Furthermore, for multiple experimental categories, methods that combine multiple GSEA/SAFE analyses are needed. Results For continuous or ordinal phenotypic outcome, we propose to use as the local statistic the coefficient of multiple determination (i.e., the square of multiple correlation coefficient) R2 from fitting natural cubic spline models to the phenotype-expression relationship. Next, we incorporate this association measure into the GSEA/SAFE framework to identify significant gene sets. Unsigned local statistics, signed global statistics and one-sided p-values are used to reflect our inferential interest. Furthermore, we describe a procedure for inference across multiple GSEA/SAFE analyses. We illustrate our approach using gene expression and liver injury data from liver and blood samples from rats treated with eight hepatotoxicants under multiple time and dose combinations. We set out to identify biological pathways/processes associated with liver injury as manifested by increased blood levels of alanine transaminase in common for most of the eight compounds. Potential statistical dependency resulting from the experimental design is addressed in permutation based hypothesis testing. Conclusion The proposed framework captures both linear and non-linear association between gene expression level and a phenotypic endpoint and thus can be viewed as extending the current GSEA/SAFE methodology. The framework for combining results from multiple GSEA/SAFE analyses is flexible to address practical inference interests. Our methods can be applied to microarray data with continuous phenotypes with multi-level design or the meta-analysis of multiple microarray data sets.

Abstract Background Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples. Results We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at 1. Conclusion Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods.

Abstract Background Recent circadian clock studies using gene expression microarray in two different tissues of mouse have revealed not all circadian-related genes are synchronized in phase or peak expression times across tissues in vivo. Instead, some circadian-related genes may be delayed by 4–8 hrs in peak expression in one tissue relative to the other. These interesting biological observations prompt a statistical question regarding how to distinguish the synchronized genes from genes that are systematically lagged in phase/peak expression time across two tissues. Results We propose a set of techniques from circular statistics to analyze phase angles of circadian-related genes in two tissues. We first estimate the phases of a cycling gene separately in each tissue, which are then used to estimate the paired angular difference of the phase angles of the gene in the two tissues. These differences are modeled as a mixture of two von Mises distributions which enables us to cluster genes into two groups; one group having synchronized transcripts with the same phase in the two tissues, the other containing transcripts with a discrepancy in phase between the two tissues. For each cluster of genes we assess the association of phases across the tissue types using circular-circular regression. We also develop a bootstrap methodology based on a circular-circular regression model to evaluate the improvement in fit provided by allowing two components versus a one-component von-Mises model. Conclusion We applied our proposed methodologies to the circadian-related genes common to heart and liver tissues in Storch et al. 2, and found that an estimated 80% of circadian-related transcripts common to heart and liver tissues were synchronized in phase, and the other 20% of transcripts were lagged about 8 hours in liver relative to heart. The bootstrap p-value for being one cluster is 0.063, which suggests the possibility of two clusters. Our methodologies can be extended to analyze peak expression times of circadian-related genes across more than two tissues, for example, kidney, heart, liver, and the suprachiasmatic nuclei (SCN) of the hypothalamus.

Background: Laparoscopic gastrectomy lacks hand-direct tactile sense and has a limited surgical field compared to laparotomy. Apart from textbook classification, there are anatomical variations in the gastric arteries. Laparoscopic gastrectomy presents technical difficulties and necessitates a more comprehensive comprehension of regional anatomy than open surgical procedures. We aimed to compare efficacy and safety of preoperative computed tomography angiography (CTA) associated with surgical decision-making for laparoscopic gastrectomy. Methods: The GISSG 20-01 study was a multicenter, open-label, randomized clinical trial. The enrollment criteria mainly included histologically confirmed gastric cancer patients with BMI ≥ 25 kg/m2. Eligible patients were randomly assigned to the CTA group or the non-CTA group in a 1:1 ratio. The primary endpoint was the volume of intraoperative blood loss. Results: Between November 2020 and December 2021, 382 patients were enrolled and randomly assigned. After exclusion of 25 patients, 357 patients were included in the modified intention-to-treat population (179 in the CTA group and 178 in the non-CTA group). The mean intraoperative blood loss (CTA vs non-CTA; 74.2 vs 95.0 mL, P = 0.005) and operation time (215.4 vs 231.2 min, P = 0.004) was significantly lower in the CTA group. Total number of retrieved lymph nodes was similar in two groups (32.2 vs 30.2, P = 0.070). The CTA group had a significantly lower surgery task load index sore than the non-CTA group (36.6 vs 41.7, P < 0.001). There was no significant difference in postoperative complications rate of 14.5% in the CTA group and 22.5% in the non-CTA group (difference, − 8.0% [95% CI, − 16.0 to 0.1]; P = 0.053). Conclusion: Preoperative CTA associated with surgical decision-making could relieve surgery burden and lead to a better surgical performance compared with non-CTA support, which including decreased blood loss volume, vessel damage and operation time. Trial Registration. NCT04636099. [ABSTRACT FROM AUTHOR]

Background: The site-specific tissue properties of knee cartilage may play an essential role in knee joint mechanical function, and mitigate joint injury and cartilage degeneration. The present study aimed to determine the significance of tissue inhomogeneity in knee joint contact mechanics using a porcine model. Methods: Finite element models were developed for a porcine knee with intact and total meniscectomy conditions to simulate whole-joint compression-relaxation tests under a 1.2-mm ramp-compression at 0.01, 0.1, or 1 mm/s. Two reference benchmarks were introduced for the implementation of poromechanical material properties of fibril-reinforced cartilage: Benchmark II consisted of 17 sets of cartilage properties, each for a region in the knee, representing site-specific inhomogeneity averaged from cartilage indentation maps of 14 porcine knees. Benchmark I was comprised of a single set of properties by taking the average properties of 17 regions in Benchmark II, assuming tissue homogeneity. To validate the modeling method, the reference benchmarks were used to produce results against whole-joint compression test data. Results: Both benchmarks were able to approximate experimental force-compression data obtained from the same knee with intact menisci and total meniscectomy, provided that the average properties from 14 knees were appropriately scaled to account for individual joint differences. Noticeable differences in stress and pressure distributions were observed between the benchmarks. For instance, benchmark I generated higher peak contact and fluid pressures in the medial tibial cartilage, but benchmark II produced the higher ones in the lateral tibial cartilage. The load sharing asymmetry between the lateral and medial compartments was reduced in benchmark II which was more pronounced for higher compression rates. On the other hand, benchmark II produced a more uniform stress distribution or lower maxima. Meniscectomy caused a slight shift of the contact centers in the tibial plateaus as compared to the intact joint. Conclusion: The modeling results demonstrated substantial differences in loading distributions in the joint between the homogeneous and nonhomogeneous models represented by the two benchmarks, indicating the role of tissue inhomogeneity in the joint contact mechanics. Region-dependent tissue properties may need to be implemented in joint mechanical modeling to evaluate the site of cartilage prone to injury or degeneration. [ABSTRACT FROM AUTHOR]

Background: Transfer RNA-derived small RNAs (tsRNAs), including tRNA-derived fragments (tRFs) and tRNA halves (tiRNAs), constitute a novel class of small noncoding RNAs (sncRNAs). tsRNAs have been linked to tumorigenesis and the progression of carcinogenesis; however, the precise molecular mechanism through which tRFs act in gastric cancer (GC) remains unknown. Methods: tRF-Tyr is a potential GC tumor suppressor that was identified through high-throughput sequencing technology. The expression and subcellular localization of tRF-Tyr in GC were detected by via qRT‒PCR and FISH. RNA pull-down, mass spectrometry, RNA immunoprecipitation (RIP), dual-luciferase reporter and rescue assays were performed to explore the regulatory mechanisms through which tRF-Tyr acts in GC. Results: tRF-Tyr was significantly downregulated and the downregulation of its mainly concentrated in the nuclei of GC cells. Functionally, tRF-Tyr inhibited the proliferation, invasiveness and migration of GC cells and promoted GC cells apoptosis in vitro; meanwhile, tRF-Tyr inhibited tumor growth in vivo. Mechanistically, tRF-Tyr bound directly to the hnRNPD protein and competitively inhibited the binding of hnRNPD to the c-Myc 3′UTR, thereby, regulating the c-Myc/Bcl2/Bax pathway and ultimately inhibiting the progression of GC. Conclusions: This study focused on a novel GC suppressor, tRF-Tyr, and revealed a previously undiscovered mechanism that tRF-Tyr inhibits tumor progression by targeting hnRNPD. These findings provide new insight into the involvement of tRFs in GC and suggest a novel target for GC treatment. [ABSTRACT FROM AUTHOR]

The failure of a filament eruption caused by magnetic reconnection between the erupting filament and the overlying magnetic field has been previously proposed in numerical simulations. It is, however, rarely observed. In this study, we report a reconnection between an erupting filament and its overlying coronal loops that results in the failure of the filament eruption. On 2023 September 24, a filament was located in active region 13445. It slowly rose, quickly erupted, rapidly decelerated, then finally stopped, with an untwisting motion. As a failed eruption, the event is associated with an M4.4 flare but no coronal mass ejection. During the eruption, the filament became bright, and the overlying loops appeared first in the high-temperature channels. They have average temperatures of ∼12.8 and ∼9.6 MK, respectively, indicating that both of them were heated. Two sets of new loops, separately connecting the filament endpoints and the overlying loop footpoints, then formed. Subsequently, the heated overlying loops were seen sequentially in the low-temperature channels, showing the cooling process, which is also supported by the light curves. Plasmoids formed and propagated bidirectionally along the filament and the overlying loops, indicating the presence of plasmoid instability. These results suggest that reconnection occurs between the erupting filament and the overlying loops. The erupting filament eventually disappeared, with the appearance of more newly formed loops. We propose that the reconnection between the erupting filament and the overlying loops ruins the filament completely, hence resulting in the failed eruption. [ABSTRACT FROM AUTHOR]

Aging is an inevitable physiological process in organisms, and the development of tumors is closely associated with cellular senescence. This article initially examines the role of cellular senescence in tumorigenesis, emphasizing the correlation between telomere length—a marker of cellular senescence—and tumor risk. Concurrently, the study explores the expression levels of senescence-associated markers, such as p16, p53, and mTOR, in the context of tumor development. Additionally, the study investigates the impact of tumors on cellular and organismal senescence, including the effects on immune system function and metabolic processes. Ultimately, the discussion explores the potential application of anti-aging strategies in tumor therapy and considers the possibility of utilizing senescence mechanisms as a novel therapeutic approach for tumors. This research provides novel insights into the complex interplay between senescence and tumor development, suggesting potential strategies for future preventative measures and therapeutic interventions. [ABSTRACT FROM AUTHOR]

Summary: Electroencephalograms can capture brain oscillatory activities during sleep as a form of electrophysiological signals. We analysed electroencephalogram recordings from full‐night in‐laboratory polysomnography from 100 patients with Down syndrome, and 100 age‐ and sex‐matched controls. The ages of patients with Down syndrome spanned 1 month to 31 years (median 4.4 years); 84 were younger than 12 years, and 54 were male. From each electroencephalogram, we extracted relative power in six frequency bands or rhythms (delta, theta, alpha, slow sigma, fast sigma, and beta) from six channels (frontal F3 and F4, central C3 and C4, and occipital O1 and O2) during five sleep stages (N3, N2, N1, R and W)—180 features in all. We examined differences in relative power between Down syndrome and control electroencephalograms for each feature separately. During wake and N1 sleep stages, alpha rhythms (8.0–10.5 Hz) had significantly lower power in patients with Down syndrome than controls. Moreover, the rate of increase in alpha power with age during rapid eye movement sleep was significantly slower in Down syndrome than control subjects. During wake and N1 sleep, delta rhythms (0.25–4.5 Hz) had higher power in patients with Down syndrome than controls. During N2 sleep, slow sigma rhythms (10.5–12.5 Hz) had lower power in patients with DS than controls. These findings extend previous research from routine electroencephalogram studies demonstrating that patients with Down syndrome had reduced circadian amplitude—the difference between wake alpha power and deep sleep delta power was smaller in Down syndrome than control subjects. We envision that these brain oscillatory activities may be used as surrogate markers for clinical trials for patients with Down syndrome. [ABSTRACT FROM AUTHOR]