Inferring gene expression dynamics via functional regression analysis

<p>Abstract</p> <p>Background</p> <p>Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other.</p> <p>Results</p> <p>We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for <it>Drosophila</it>, using temporal gene expression profiles.</p> <p>Conclusion</p> <p>Our findings point to specific reactivation patterns of gene expression during the <it>Drosophila </it>life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.</p

Facilitation of the inhibitory transmission by gastrin-releasing peptide in the anterior cingulate cortex

Gastrin-releasing peptide (GRP) has been proposed as a peptidergic molecule for behavioral fear and itching. Immunohistochemistry and in situ hybridization studies have shown that GRP and GRP receptor are widely distributed in forebrain areas. Less information is available for the functional action for GRP in the prefrontal cortex including the anterior cingulate cortex (ACC). Here we used whole-cell patch-clamp recording technique to study the modulation of synaptic transmission by GRP in the ACC. We found that GRP increased the frequency of sIPSCs recorded while had no significant effect on sEPSCs in ACC pyramidal neurons. The facilitatory effect of GRP on sIPSCs was blocked by the GRP receptor antagonist, RC3095. In the presence of TTX, however, GRP had no effect on the mIPSCs. Therefore, activation of GRP receptor may facilitate the excitation of the interneurons and enhanced spontaneous GABAergic, but not glutamatergic neurotransmission. Similar results on GRP modulation of GABAergic transmission were observed in the insular cortex and amygdala, suggesting a general possible effect of GRP on cortical inhibitory transmission. Our results suggest that GRP receptor is an important regulator of inhibitory circuits in forebrain areas

Pharmacological isolation of postsynaptic currents mediated by NR2A- and NR2B-containing NMDA receptors in the anterior cingulate cortex

NMDA receptors (NMDARs) are involved in excitatory synaptic transmission and plasticity associated with a variety of brain functions, from memory formation to chronic pain. Subunit-selective antagonists for NMDARs provide powerful tools to dissect NMDAR functions in neuronal activities. Recently developed antagonist for NR2A-containing receptors, NVP-AAM007, triggered debates on its selectivity and involvement of the NMDAR subunits in bi-directional synaptic plasticity. Here, we re-examined the pharmacological properties of NMDARs in the anterior cingulate cortex (ACC) using NVP-AAM007 as well as ifenprodil, a selective antagonist for NR2B-containing NMDARs. By alternating sequence of drug application and examining different concentrations of NVP-AAM007, we found that the presence of NVP-AAM007 did not significantly affect the effect of ifenprodil on NMDAR-mediated EPSCs. These results suggest that NVP-AAM007 shows great preference for NR2A subunit and could be used as a selective antagonist for NR2A-containing NMDARs in the ACC

Risk assessment and source identification of coastal groundwater nitrate in northern China using dual nitrate isotopes combined with Bayesian mixing model

Due to the intensive and complicated human activities, the identification of nitrate pollution source of coastal aquifer is usually a challenge. This study firstly adopted stable isotope technique and stable isotope analysis in R (SIAR) model to identify the nitrate sources and contribution proportions of different sources in typical coastal groundwater of northern China. The results showed that about 91.5% of the groundwater samples illustrated significantly high nitrate concentrations exceeding the maximum WHO drinking water standard (50mg/l), reflecting the high risk of groundwater nitrate pollution in the coastal area. A total of 57 sampling sites were classified into three groups according to hierarchical cluster analysis (HCA). The N-15-NO3- and O-18-NO3- values of groundwater samples from Group C (including nine samples) were much higher than those from Group A (including 40 samples) and Group B (including 8 samples). SIAR results showed that NH4+ fertilizer was the dominant nitrate source for groundwater samples of Groups A and B while manure and sewage (M&amp;S) served as dominant source for Group C. This study provided essential information on the high risk and pollution sources of coastal groundwater nitrate of northern China.</p

Adaptive Stabilization of Stochastic Nonlinear Systems Disturbed by Unknown Time Delay and Covariance Noise

This paper considers a more general stochastic nonlinear time-delay system driven by unknown covariance noise and investigates its adaptive state-feedback control problem. As a remarkable feature, the growth assumptions imposed on delay-dependent nonlinear terms are removed. Then, with the help of Lyapunov-Krasovskii functionals and adaptive backstepping technique, an adaptive state-feedback controller is constructed by overcoming the negative effects brought by unknown time delay and covariance noise. Based on the designed controller, the closed-loop system can be guaranteed to be globally asymptotically stable (GAS) in probability. Finally, a simulation example demonstrates the effectiveness of the proposed scheme

Neurabin Contributes to Hippocampal Long-Term Potentiation and Contextual Fear Memory

Neurabin is a scaffolding protein that interacts with actin and protein phosphatase-1. Highly enriched in the dendritic spine, neurabin is important for spine morphogenesis and synaptic formation. However, less is known about the role of neurabin in hippocampal plasticity and its possible effect on behavioral functions. Using neurabin knockout (KO) mice, here we studied the function of neurabin in hippocampal synaptic transmission, plasticity and behavioral memory. We demonstrated that neurabin KO mice showed a deficit in contextual fear memory but not auditory fear memory. Whole-cell patch clamp recordings in the hippocampal CA1 neurons showed that long-term potentiation (LTP) was significantly reduced, whereas long-term depression (LTD) was unaltered in neurabin KO mice. Moreover, increased AMPA receptor but not NMDA receptor-mediated synaptic transmission was found in neurabin KO mice, and is accompanied by decreased phosphorylation of GluR1 at the PKA site (Ser845) but no change at the CaMKII/PKC site (Ser831). Pre-conditioning with LTD induction rescued the following LTP in neurabin KO mice, suggesting the loss of LTP may be due to the saturated synaptic transmission. Our results indicate that neurabin regulates contextual fear memory and LTP in hippocampal CA1 pyramidal neurons

AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models

The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-based methods have been proposed for this task. However, to our best knowledge, existing methods all require manually crafted seed alignments, which are expensive to obtain. In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments. Specifically, for predicate embeddings, AutoAlign constructs a predicate-proximity-graph with the help of large language models to automatically capture the similarity between predicates across two KGs. For entity embeddings, AutoAlign first computes the entity embeddings of each KG independently using TransE, and then shifts the two KGs' entity embeddings into the same vector space by computing the similarity between entities based on their attributes. Thus, both predicate alignment and entity alignment can be done without manually crafted seed alignments. AutoAlign is not only fully automatic, but also highly effective. Experiments using real-world KGs show that AutoAlign improves the performance of entity alignment significantly compared to state-of-the-art methods.Comment: 14 pages, 5 figures, 4 tables. arXiv admin note: substantial text overlap with arXiv:2210.0854

A unified formula for calculating bending capacity of solid and hollow concrete-filled steel tubes under normal and elevated temperature

Bending is one of the most common forms of deformation that may cause failure of a structural member, such as a column, especially when the member is exposed to fire. Fire resistance design is therefore an important factor that must be considered in the design process of modern building structures. Based on the authors' previous work on the unified formulation of axially loaded CFST hollow and solid columns with circular and polygonal sections, a unified formula for calculating the ultimate bending moment of solid and hollow CFST columns at room temperature is proposed first in this paper. The formula is then extended to include elevated temperature using the average temperature method. Finally, a unified formula for both room and elevated temperature are presented. Validations are carried out through comparisons with the results from experimental tests and finite element simulations