The Emerging Regulatory Role of Circular RNAs in Periodontal Tissues and Cells

Periodontitis is a chronic complex inflammatory disease associated with a destructive host immune response to microbial dysbiosis, leading to irreversible loss of tooth-supporting tissues. Regeneration of functional periodontal soft (periodontal ligament and gingiva) and hard tissue components (cementum and alveolar bone) to replace lost tissues is the ultimate goal of periodontal treatment, but clinically predictable treatments are lacking. Similarly, the identification of biomarkers that can be used to accurately diagnose periodontitis activity is lacking. A relatively novel category of molecules found in oral tissue, circular RNAs (circRNAs) are single-stranded endogenous, long, non-coding RNA molecules, with covalently circular-closed structures without a 5’ cap and a 3’ tail via non-classic backsplicing. Emerging research indicates that circRNAs are tissue and disease-specific expressed and have crucial regulatory functions in various diseases. CircRNAs can function as microRNA or RNA binding sites or can regulate mRNA. In this review, we explore the biogenesis and function of circRNAs in the context of the emerging role of circRNAs in periodontitis pathogenesis and the differentiation of periodontal cells. CircMAP3K11, circCDK8, circCDR1as, circ_0062491, and circ_0095812 are associated with pathological periodontitis tissues. Furthermore, circRNAs are expressed in periodontal cells in a cell-specific manner. They can function as microRNA sponges and can form circRNA–miRNA–mRNA networks during osteogenic differentiation for periodontal-tissue (or dental pulp)-derived progenitor cells.     

Studies on nucleation kinetics of InxGa1−xN/GaN heterostructures

Nucleation kinetics during the growth of In_xGa_1−xN on a GaN substrate have been studied. The behavior of nonequilibrium between the In_xGa_1−xN and the GaN substrate has been analyzed, and hence, the expression derived for the stress-induced supercooling/superheating has been numerically evaluated. The maximum amount of stress-induced supercooling is found to be 1.017 K at x=0.12. These values are incorporated in the classical heterogeneous nucleation theory. Using the regular solution model, the interfacial tension between the nucleus and substrate and, hence, the interfacial tension between nucleus and mother phase and thermodynamical potential of the compounds have been calculated. The amount of driving force available for the nucleation has been determined for different compositions and degrees of supercooling. It has been shown that the value of the interaction parameter of InN-GaN plays a dominant role in nucleation and growth kinetics of In_xGa_1−xN on a GaN substrate. These values have been used to evaluate the nucleation parameters. It is shown that the nucleation barrier for the formation of a In_xGa_1−xN nucleus on a GaN substrate is minimum in the range of x=0.12 to x=0.17, and it has been qualitatively proved that good quality In_xGa_1−xN on GaN can be grown only in the range 0<x≤0.2.     

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters

RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA features. Dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals was obtained from the CommonMind consortium. The average predictive accuracy for SCZ patients was 67% based on coding genes, and 96% based on long non-coding RNAs (lncRNAs). Machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the former regulate every level of gene expression, not limited to mRNA levels.     

Small Noncoding RNAs in Knee Osteoarthritis: The Role of MicroRNAs and tRNA-Derived Fragments

Knee osteoarthritis (OA) is a degenerative knee joint disease that results from the breakdown of joint cartilage and underlying bone, affecting about 3.3% of the world’s population. As OA is a multifactorial disease, the underlying pathological process is closely associated with genetic changes in articular cartilage and bone. Many studies have focused on the role of small noncoding RNAs in OA and identified numbers of microRNAs that play important roles in regulating bone and cartilage homeostasis. The connection between other types of small noncoding RNAs, especially tRNA-derived fragments and knee osteoarthritis is still elusive. The observation that there is limited information about small RNAs different than miRNAs in knee OA was very surprising to us, especially given the fact that tRNA fragments are known to participate in a plethora of human diseases and a portion of them are even more abundant than miRNAs. Inspired by these findings, in this review we have summarized the possible involvement of microRNAs and tRNA-derived fragments in the pathology of knee osteoarthritis.     

The effect of α phase on the deformation mechanisms of β titanium alloys

The effects of α and β phase interactions on the tensile and creep deformation behavior of β titanium alloys was studied using Ti-6.0wt.% Mn and Ti-8.1wt.%V as the model two-phase alloys, and Ti-13.0wt.%Mn and Ti-14.8wt.%V as the single-phase β alloys. The β phase of α-βTi-8.1V deforms by stress-induced hexagonal martensite (α′), while slip and twinning occurs in the single-phase β alloy with the same chemistry as the β phase. No stress-induced martensite was observed in the β or α-βTi-Mn alloys. This behavior is modeled in terms of a number of factors, including elastic interaction stresses between the α and β phases, coherency between the α phase and hexagonal martensite, and β phase stability. This paper was presented at the Beta Titanium Alloys of the 00’s Symposium sponsored by the Titanium Committee of TMS, held during the 2005 TMS Annual Meeting & Exhibition, February 13–16, 2005 in San Francisco, CA.     

Cu-Al-Mn-Zn-Zr记忆合金的显微组织与力学行为

利用金相显微镜、透射电镜、扫描电镜观察和力学性能测试等手段研究了Cu—18．4A1—8．7Mn—3．4Zn—0．1Zr(摩尔分数，％)记亿合金(多晶)在不同温度与组织下变形时的力学行为。实验合金在不同温度下变形时延伸率不同，变形温度(Td)在Ms与Af之间时的延伸率小于变形温度(Td)高于Af时的延伸率。对实验合金施加应力时，最初的变形主要来自应力诱发马氏体，继而发生马氏体变体择优合并，二者对合金的变形都有贡献；变形量更大时，变体界面共格性被破坏，部分马氏体丧失热弹性。少量α相的析出不影响记忆效应，可使延伸率得以提高。     

A Comprehensive Review of Genetically Engineered Mouse Models for Prader-Willi Syndrome Research

Prader-Willi syndrome (PWS) is a neurogenetic multifactorial disorder caused by the deletion or inactivation of paternally imprinted genes on human chromosome 15q11-q13. The affected homologous locus is on mouse chromosome 7C. The positional conservation and organization of genes including the imprinting pattern between mice and men implies similar physiological functions of this locus. Therefore, considerable efforts to recreate the pathogenesis of PWS have been accomplished in mouse models. We provide a summary of different mouse models that were generated for the analysis of PWS and discuss their impact on our current understanding of corresponding genes, their putative functions and the pathogenesis of PWS. Murine models of PWS unveiled the contribution of each affected gene to this multi-facetted disease, and also enabled the establishment of the minimal critical genomic region (PWScr) responsible for core symptoms, highlighting the importance of non-protein coding genes in the PWS locus. Although the underlying disease-causing mechanisms of PWS remain widely unresolved and existing mouse models do not fully capture the entire spectrum of the human PWS disorder, continuous improvements of genetically engineered mouse models have proven to be very powerful and valuable tools in PWS research.