Augmented proximity: Integration of physical and virtual proximity to enhance network connectivity

With the continuing increase in online communications, virtual proximity as well as physical proximity has become a common way to connect individuals. Virtual proximity refers to the psychological closeness felt by people based on their participation in a virtual space. Hence, augmenting physical proximity with virtual types is believed to enhance connectivity within social networks, and applications that consider both types have the capability to provide new forms of interaction. However, despite the importance of their coexistence, these proximity concepts have been studied separately or are being integrated using diverse terminologies that often lead to misunderstandings. Furthermore, although some applications reflect the two proximity types (e.g., location-based services), there is no metric of comparison. This paper proposes the concept of augmented proximity (AP), which combines physical and virtual forms into a network analogous to one of mixed reality (MR). The concept provides a clear distinction between physical and virtual proximity using a single quantitative value. Using this concept, a formal taxonomy is established to compare and evaluate AP-based networks. The taxonomy consists of three dimensions that can be analyzed using graph theory, including the extent of connectivity degree, diffusion effect, and extent of perceived closeness. Furthermore, using the services underlying AP-based network, the proposed taxonomy can be applied to evaluate the suitability of the services as an indicator for comparison. The results show that one of the two services has a higher taxonomy-based value, and a reasonable basis for selecting one based on proximity is established. This study suggests that AP will play an important role as a quantitative indicator in developing and comparing applications that consider proximity in both virtual and physical modes.

Integrated Analysis Reveals a lncRNA–miRNA–mRNA Network Associated with Pigeon Skeletal Muscle Development

Growing evidence has demonstrated the emerging role of long non-coding RNA as competitive endogenous RNA (ceRNA) in regulating skeletal muscle development. However, the mechanism of ceRNA regulated by lncRNA in pigeon skeletal muscle development remains unclear. To reveal the function and regulatory mechanisms of lncRNA, we first analyzed the expression profiles of lncRNA, microRNA (miRNA), and mRNA during the development of pigeon skeletal muscle using high-throughput sequencing. We then constructed a lncRNA–miRNA–mRNA ceRNA network based on differentially expressed (DE) lncRNAs, miRNAs, and mRNAs according to the ceRNA hypothesis. Functional enrichment and short time-series expression miner (STEM) analysis were performed to explore the function of the ceRNA network. Hub lncRNA–miRNA–mRNA interactions were identified by connectivity degree and validated using dual-luciferase activity assay. The results showed that a total of 1625 DE lncRNAs, 11,311 DE mRNAs, and 573 DE miRNAs were identified. A ceRNA network containing 9120 lncRNA–miRNA–mRNA interactions was constructed. STEM analysis indicated that the function of the lncRNA-associated ceRNA network might be developmental specific. Functional enrichment analysis identified potential pathways regulating pigeon skeletal muscle development, such as cell cycle and MAPK signaling. Based on the connectivity degree, lncRNAs TCONS_00066712, TCONS_00026594, TCONS_00001557, TCONS_00001553, and TCONS_00003307 were identified as hub genes in the ceRNA network. lncRNA TCONS_00026594 might regulate the FSHD region gene 1 (FRG1)/ SRC proto-oncogene, non-receptor tyrosine kinase (SRC) by sponge adsorption of cli-miR-1a-3p to affect the development of pigeon skeletal muscle. Our findings provide a data basis for in-depth elucidation of the lncRNA-associated ceRNA mechanism underlying pigeon skeletal muscle development.

Interaction of Urban Rivers and Green Space Morphology to Mitigate the Urban Heat Island Effect: Case-Based Comparative Analysis

The spatial morphology of waterfront green spaces helps generate cooling effects to mitigate the urban heat island effect (UHI) in metropolis cities. To explore the contribution and influence of multi-dimensional spatial indices on the mitigation of UHIs, the green space of the riparian buffer along 18 river channels in Shanghai was considered as a case study. The spatial distribution data of the land surface temperature (LST) in the study area were obtained by using remote sensing images. By selecting the related spatial structure morphological factors of the waterfront green space as the quantitative description index, the growth regression tree model (BRT) was adapted to analyze the contribution of various indexes of the waterfront green space on the distribution of the LST and the marginal effect of blue–green synergistic cooling. In addition, mathematical statistical analysis and spatial analysis methods were used to study the influence of the morphological group (MG) types of riparian green spaces with different morphological characteristics on the LST. The results showed that in terms of the spatial structure variables between blue and green spaces, the contribution of river widths larger than 30 m was more notable in decreasing the LST. In the case of a larger river width, the marginal effect of synergistic cooling could be observed in farther regions. The green space that had the highest connectivity degree and was located in the leeward direction of the river exhibited the lowest LST. In terms of the spatial morphology, the fractional cover values of the vegetation (Fv) and area (A) of the green space were the main factors affecting the cooling effect of the green space. For all MG types, a large green patch that had a high green coverage and connectivity degree, as well as was distributed in the leeward direction of the river, corresponded to the lowest LST. The research presented herein can provide methods and development suggestions for optimizing spatial thermal comfort in climate adaptive cities.

Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

Identification and analysis of immune-related subtypes of hepatocellular carcinoma

Hepatocellular carcinoma is a malignance that remains difficult to cure. Immunotherapy has shown its potential application in a variety of refractory malignancies. Due to the complexity of immune microenvironment of hepatocellular carcinoma, the efficacy of immunotherapy for hepatocellular carcinoma is not as effective as expected. Expression data of hepatocellular carcinoma from the TCGA and ICGC databases were used for classification and verification of hepatocellular carcinoma subtypes. The immune-related functions and pathways were identified via gene set enrichment analysis, while the sections denoting the subsets of the immune cells were estimated using the CIBERSORT algorithm. Immunity low (Immunity_L), immunity medium (Immunity_M), and immunity high (Immunity_H) were specified as the three immune-related subtypes of hepatocellular carcinoma. The quantity of stromal and immune cells was the most substantial in Immunity_H, compared to the other subtypes. Interestingly, the proportion of M0 macrophages decreased from Immunity_L to Immunity_H, while the proportion of CD8 T cells increased. Furthermore, the HLA genes expression levels, as well as those of six immune checkpoint genes were substantially lower in Immunity_L than in Immunity_H. Functional analysis was performed for 1512 differentially expressed genes between Immunity_L and Immunity_H. Finally, the PPI network was constructed with 118 nodes. The highest connectivity degree nodes were B2M, HLA-DRA, and HLA-DRB1. The above results were verified in ICGC-JP and ICGC-FR databases with a consistent trend. In this study, we divided hepatocellular carcinoma into three subtypes and explored the immune-related characteristics of these subtypes. These results may provide new insights for immunotherapy of hepatocellular carcinoma.

Optimization of the surface code design for Majorana-based qubits

The surface code is a prominent topological error-correcting code exhibiting high fault-tolerance accuracy thresholds. Conventional schemes for error correction with the surface code place qubits on a planar grid and assume native CNOT gates between the data qubits with nearest-neighbor ancilla qubits.Here, we present surface code error-correction schemes using only Pauli measurements on single qubits and on pairs of nearest-neighbor qubits. In particular, we provide several qubit layouts that offer favorable trade-offs between qubit overhead, circuit depth and connectivity degree. We also develop minimized measurement sequences for syndrome extraction, enabling reduced logical error rates and improved fault-tolerance thresholds.Our work applies to topologically protected qubits realized with Majorana zero modes and to similar systems in which multi-qubit Pauli measurements rather than CNOT gates are the native operations.

A structure hierarchy for silicate minerals: chain, ribbon, and tube silicates

A structure hierarchy is developed for chain-, ribbon- and tube-silicate based on the connectedness of one-dimensional polymerisations of (TO4)n− tetrahedra, where T = Si4+ plus P5+, V5+, As5+, Al3+, Fe3+, B3+, Be2+, Zn2+ and Mg2+. Such polymerisations are described by a geometrical repeat unit (with ng tetrahedra) and a topological repeat unit (or graph) (with nt vertices). The connectivity of the tetrahedra (vertices) in the geometrical (topological) repeat units is denoted by the expression cTr (cVr) where c is the connectivity (degree) of the tetrahedron (vertex) and r is the number of tetrahedra (vertices) of connectivity (degree) c in the repeat unit. Thus cTr = 1Tr12Tr23Tr34Tr4 (cVr = 1Vr12Vr23Vr34Vr4) represents all possible connectivities (degrees) of tetrahedra (vertices) in the geometrical (topological) repeat units of such one-dimensional polymerisations. We may generate all possible cTr (cVr) expressions for chains (graphs) with tetrahedron (vertex) connectivities (degrees) c = 1 to 4 where r = 1 to n by sequentially increasing the values of c and r, and by ranking them accordingly. The silicate (sensu lato) units of chain-, ribbon- and tube-silicate minerals are identified and associated with the relevant cTr (cVr) symbols. Following description and association with the relevant cTr (cVr) symbols of the silicate units in all chain-, ribbon- and tube-silicate minerals, the minerals are arranged into decreasing O:T ratio from 3.0 to 2.5, an arrangement that reflects their increasing structural connectivity. Considering only the silicate component, the compositional range of the chain-, ribbon- and tube-silicate minerals strongly overlaps that of the sheet-silicate minerals. Of the chain-, ribbon- and tube-silicates and sheet silicates with the same O:T ratio, some have the same cVr symbols (vertex connectivities) but the tetrahedra link to each other in different ways and are topologically different. The abundance of chain-, ribbon- and tube-silicate minerals decreases as O:T decreases from 3.0 to 2.5 whereas the abundance of sheet-silicate minerals increases from O:T = 3.0 to 2.5 and decreases again to O:T = 2.0. Some of the chain-, ribbon- and tube-silicate minerals have more than one distinct silicate unit: (1) vinogradovite, revdite, lintisite (punkaruaivite) and charoite have mixed chains, ribbons and/or tubes; (2) veblenite, yuksporite, miserite and okenite have clusters or sheets in addition to chains, ribbons and tubes. It is apparent that some chain-ribbon-tube topologies are favoured over others as of the ~450 inosilicate minerals, ~375 correspond to only four topologically unique graphs, the other ~75 minerals correspond to ~46 topologically unique graphs.

Hypoxia on vhl-deficient cells to obtain hif related genes through bioinformatics analysis

Vhl is responsible for degrading the transcription factor hif-1. Hif-1 transcription factors drive changes in hypoxic gene expression to adapt cells to exposure to hypoxic environments. This study hopes to analyze the effect of hypoxia on the vhl-deficient cells to obtain hif regulated genes through bioinformatics analysis. The top ten genes evaluated by connectivity degree in the PPI network were identified. CDK1, CTNNB1, NHP2, CCNA2, CTNNB1, MRPL16, CCND1 were down-regulated. CDK1, RPL12, RPL17, RPL27, RPS10 were up-regulated.

A Method of Sanitizing Privacy-Sensitive Sequence Pattern Networks Mined From Trajectories Released

Mobility patterns mined from released trajectories can help to allocate resources and provide personalized services, although these also pose a threat to personal location privacy. As the existing sanitization methods cannot deal with the problems of location privacy inference attacks based on privacy-sensitive sequence pattern networks, the authors proposed a method of sanitizing the privacy-sensitive sequence pattern networks mined from trajectories released by identifying and removing influential nodes from the networks. The authors conducted extensive experiments and the results were shown that by adjusting the parameter of the proportional factors, the proposed method can thoroughly sanitize privacy-sensitive sequence pattern networks and achieve the optimal values for security degree and connectivity degree measurements. In addition, the performance of the proposed method was shown to be stable for multiple networks with basically the same privacy-sensitive node ratio and be scalable for batches of networks with different sensitive nodes ratios.