DYNAMIC CLUSTERING OF CELL-CYCLE MICROARRAY DATA

The cell cycle is a crucial series of events that are repeated over time, allowing the cell to grow, duplicate, and split. Cell-cycle systems play an important role in cancer and other biological processes. Using gene expression data gained from microarray technology it is possible to group or cluster genes that are involved in the cell-cycle for the purpose of exploring their functional co-regulation. Typically, the goal of clustering methods as applied to gene expression data is to place genes with similar expression patterns or profiles into the same group or cluster for the purpose of inferring the function of unknown genes that cluster with genes of known function. Since a gene may be involved in more than one biological process at any one time, co-regulated genes may not have visually similar expression patterns. Furthermore, the time duration for genes in a biological process may differ, and the number of co-regulated patterns or biological processes shared by two genes may be unknown. Based on this reasoning, biologically realistic gene clusters gained from gene co-regulation may not be accurately identified using traditional clustering methods. By taking advantage of techniques and theories from signal processing, it possible to cluster cell-cycle gene expression profiles using a dynamic perspective under the assumption that different spectral frequencies characterize different biological processes

TimeNorm: a novel normalization method for time course microbiome data

Metagenomic time-course studies provide valuable insights into the dynamics of microbial systems and have become increasingly popular alongside the reduction in costs of next-generation sequencing technologies. Normalization is a common but critical preprocessing step before proceeding with downstream analysis. To the best of our knowledge, currently there is no reported method to appropriately normalize microbial time-series data. We propose TimeNorm, a novel normalization method that considers the compositional property and time dependency in time-course microbiome data. It is the first method designed for normalizing time-series data within the same time point (intra-time normalization) and across time points (bridge normalization), separately. Intra-time normalization normalizes microbial samples under the same condition based on common dominant features. Bridge normalization detects and utilizes a group of most stable features across two adjacent time points for normalization. Through comprehensive simulation studies and application to a real study, we demonstrate that TimeNorm outperforms existing normalization methods and boosts the power of downstream differential abundance analysis

Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition

Synchronous oscillations in neural populations are considered being controlled by inhibitory neurons. In the granular layer of the cerebellum, two major types of cells are excitatory granular cells (GCs) and inhibitory Golgi cells (GoCs). GC spatiotemporal dynamics, as the output of the granular layer, is highly regulated by GoCs. However, there are various types of inhibition implemented by GoCs. With inputs from mossy fibers, GCs and GoCs are reciprocally connected to exhibit different network motifs of synaptic connections. From the view of GCs, feedforward inhibition is expressed as the direct input from GoCs excited by mossy fibers, whereas feedback inhibition is from GoCs via GCs themselves. In addition, there are abundant gap junctions between GoCs showing another form of inhibition. It remains unclear how these diverse copies of inhibition regulate neural population oscillation changes. Leveraging a computational model of the granular layer network, we addressed this question to examine the emergence and modulation of network oscillation using different types of inhibition. We show that at the network level, feedback inhibition is crucial to generate neural oscillation. When short-term plasticity was equipped on GoC-GC synapses, oscillations were largely diminished. Robust oscillations can only appear with additional gap junctions. Moreover, there was a substantial level of cross-frequency coupling in oscillation dynamics. Such a coupling was adjusted and strengthened by GoCs through feedback inhibition. Taken together, our results suggest that the cooperation of distinct types of GoC inhibition plays an essential role in regulating synchronous oscillations of the GC population. With GCs as the sole output of the granular network, their oscillation dynamics could potentially enhance the computational capability of downstream neurons

CLUSTERING A SERIES OF REPLICATED POLYPLOID GENE EXPRESSION EXPERIMENTS IN MAIZE

Ploidy level is defined as the number of individual sets of chromosomes contained in a single cell. Many important crop plants, such as potato, soybean and wheat are polyploid. Although it is widely known that polyploidy is a frequent evolutionary event, it is not fully understand why polyploids have been so successful. In this work cluster analysis is employed to study gene expression changes in a maize inbred line (B73) across a range of polyploidy levels. The B73 ploidy series includes monoploid, diploid, triploid and tetraploid plants and consists of biological and technical replicates as measured by microarray technology. An improved version of CORE (iCORE; improved Clustering of Repeat Expression) is presented to differentiate highly negatively correlated genes while taking advantage of the additional information that is provided by replication. The error information from the replicate experiments is utilized to cluster gene expression for both simulated and real ploidy-series data. Simulation results indicate that iCORE leads to an improvement in accuracy over both CORE and hierarchical clustering based on average gene expression only. When applied to the maize ploidy series, the iCORE results provide information that may aid in understanding of the effect of gene dose on gene expression in a ploidy series

Tracheal extubation under Narcotrend EEG monitoring at different depths of anesthesia after tonsillectomy in children: a prospective randomized controlled study

ObjectiveThis study aims to investigate whether tracheal extubation at different depths of anesthesia using Narcotrend EEG (NT value) can influence the recovery quality from anesthesia and cognitive function of children who underwent tonsillotomy.MethodsThe study enrolled 152 children who underwent tonsillotomy and were anesthetized with endotracheal intubation in our hospital from September 2019 to March 2022. These patients were divided into Group A (conscious group, NT range of 95–100), Group B (light sedation group, NT range of 80–94), and Group C (conventional sedation group, NT range of 65–79). A neonatal pain assessment tool, namely, face, legs, activity, cry, and consolability (FLACC), was used to compare the pain scores of the three groups as the primary end point. The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scales were used to evaluate the cognitive function of children in the three groups before and after surgery as the secondary end points.ResultsDifferences were observed in the awakening time and FLACC scores after awakening among the three groups (P < 0.05). Among them, Group A exhibited a significantly shorter awakening time and higher FLACC score after awakening than those in Groups B and C (both P < 0.05). The total incidence of adverse reactions in Group B was significantly lower than that in Groups A and C (P < 0.05). No significant difference was observed in MMSE and MoCA scores before the operation and at 7 days after the operation among the three groups (P > 0.05), but a significant difference was found in MMSE and MoCA scores at 1 day and 3 days after the operation among the three groups (P < 0.05). In addition, MMSE and MoCA scores of the three groups decreased significantly at 1 day and 3 days after the operation than those at 1 day before the operation (P < 0.05).ConclusionWhen the NT value of tonsillectomy is between 80 and 94, tracheal catheter removal can effectively improve the recovery quality and postoperative cognitive dysfunction of children

Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 mu m was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.U.S. National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) [DGE-1143953]; U.S. National Institutes of Health - National Institute of Environmental Health Sciences (NIH-NIEHS) [R25ES025494]; Western Alliance to Expand Student Opportunities (WAESO) at Arizona State University; U.S. National Institutes of Health -National Institute of General Medical Sciences (NIH-NIGMS) [T32GM084905]; Korea Institute of Ocean Science and Technology (KIOST)Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Genetic diversity of Ophiocordyceps sinensis, a medicinal fungus endemic to the Tibetan Plateau: Implications for its evolution and conservation

<p>Abstract</p> <p>Background</p> <p><it>Ophiocordyceps sinensis </it>(syn. <it>Cordyceps sinensis</it>), endemic to alpine regions on the Tibetan plateau, is one of the most valuable medicinal fungi in the world. Huge commercial demand has led to excessive harvest and a dramatic decline in its numbers. The diversity of terrains and climates on the Tibetan Plateau and the broad insect host range (more than 50 species in the family Hepialidae) may have resulted in substantial intraspecific genetic diversity for this fungus. The objective of this study was to evaluate the population distribution of <it>O. sinensis </it>from geographically diverse regions of the Tibetan Plateau based on nrDNA ITS and <it>MAT1-2-1 </it>gene sequences. Understanding of the genetic diversity and genesis of <it>O. sinensis </it>will provide important information for the evolution and conservation of this fungus.</p> <p>Results</p> <p>Significant sequence variations in the ITS and <it>MAT1-2-1 </it>genes (27 and 23 informative sites, eight and seven haplotypes, respectively) were observed. Phylogenetic analysis based on ITS sequences, <it>MAT1-2-1 </it>sequences, or their combined data set, clustered isolates from northern regions in one clade (clade I), whereas isolates from southern regions were dispersed in all four clades (clade I-IV). Single-strand conformation polymorphism (SSCP) analyses of 2639 ITS clones from seven samples revealed 91 different SSCP patterns that were subsequently sequenced. ITS heterogeneity was found in XZ-LZ07-H1 (Nyingchi population), and 17 informative sites and five haplotypes were detected from 15 clones. The five haplotypes clustered into three clades (clade I, II, and IV).</p> <p>Conclusions</p> <p>Significant genetic divergence in <it>O. sinensis </it>was observed and the genetic diversification was greater among southern isolates than that among northern isolates. The polymorphism of nrDNA ITS sequences suggested that <it>O. sinensis </it>spread from a center of origin (the Nyingchi District) to southern regions and subsequently to northern areas. These results suggest that southern populations are important reservoirs of genetic diversity and should be taken into account in conservation programs.</p

Association between systemic immune-inflammation index and trimethylamine N-oxide levels in peripheral blood and osteoporosis in overweight and obese patients

BackgroundThe intricate relationship between systemic immune-inflammation index (SII) and trimethylamine N-oxide (TMAO) in the peripheral blood and osteoporosis (OP) remains unclear. This study aims to investigate variations in the levels of SII and TMAO in the peripheral blood of overweight and obese patients, and examine the associations between these markers, bone mineral density (BMD), and the occurrence of osteoporotic fractures.MethodsThe study enrolled 765 patients aged ≥ 50 years with BMI ≥ 24 kg/m², dividing them into two groups based on visceral fat area (VFA): &lt;100 cm² and ≥100 cm². A corrected regression model analyzed the association of SII, TMAO, BMD, and osteoporotic fractures incidence in patients with central obesity. Receiver operator characteristic (ROC) curves assessed the predictive ability of SII and TMAO for OP screening.ResultsBaseline data showed that patients with VFA ≥ 100 cm² had lower whole body (WB) and lumbar spine (LS) BMD, but higher SII and TMAO levels compared to those with VFA &lt; 100 cm² (p &lt; 0.05). Particularly in the group with VFA ≥ 100 cm2, there was an upward trend in SII and TMAO as bone mass decreased. Regression analysis found SII and TMAO negatively correlated with WB, LS, and femoral neck (FN) BMD, and positively correlated with osteoporotic fractures incidence (p &lt; 0.05). Both were independent risk factors for OP, with combined SII and TMAO detection showing high diagnostic efficacy (sensitivity 94.7%, specificity 96.5%).ConclusionIn overweight and obese patients, particularly those with a VFA ≥ 100 cm², peripheral blood SII and TMAO levels may serve as valuable biomarkers for the early diagnosis of OP, offering potential clinical utility in identifying high-risk individuals