LncRNA FAM83A-AS1 promotes epithelial-mesenchymal transition of pancreatic cancer cells via Hippo pathway.

BACKGROUND: Long non-coding RNAs (lncRNAs) have been proved to play a vital role in pancreatic cancer (PC). However, the role of lncRNA FAM83A-AS1 in PC remains unclear. In this study, we explored the biological function and underlying mechanism of FAM83A-AS1 in PC cells. METHODS: The FAM83A-AS1 expression was assessed via public databases and validated by qRT-PCR. The biofunction and immune cell infiltration of FAM83A-AS1 were analyzed through GO, KEGG, GESA and ssGSEA. The migration, invasion and proliferation abilities of PC cells were examined by Transwell, wound healing, CCK8 and colony formation. The EMT and Hippo pathway markers were evaluated by western blot. RESULTS: FAM83A-AS1 expression was higher in PC tissues and cells than normal. Additionally, FAM83A-AS1 was associated with poor prognosis of PC and involved in cadherin binding and immune infiltration. Subsequently, we proved FAM83A-AS1 overexpression enhanced the migration, invasion and proliferation abilities of PC cells, whereas FAM83A-AS1 downregulation inhibited those. Moreover, western blot results showed that FAM83A-AS1 knockdown increased the E-cadherin expression and decreased the expression of N-cadherin, ß-catenin, Vimentin, Snail and Slug. On the contrary, FAM83A-AS1 upregulation results in the opposite effects. Besides, FAM83A-AS1 overexpression inhibited the expression of p-YAP, p-MOB1, p-Lats1, SAV1, MST1 and MST2 as well as the results of FAM83A-AS1 knockdown were opposite. CONCLUSION: FAM83A-AS1 promoted EMT of PC cells via Hippo signaling inactivation and may be a potential diagnosis and prognosis target.

CORDIC-Based General Multiple Fading Generator for Wireless Channel Digital Twin.

A wireless channel digital twin is a useful tool to evaluate the performance of a communication system at the physical or link level by generating the physical channel controllably. In this paper, a stochastic general fading channel model is proposed, which considered most of the channel fading types for various communication scenarios. By using the sum-of-frequency-modulation (SoFM) method, the phase discontinuity of the generated channel fading was well addressed. On this basis, a general and flexible generation architecture for channel fading was developed on a field programmable gate array (FPGA) platform. In this architecture, improved CORDIC-based hardware circuits for the trigonometric function, exponential function, and natural logarithm were designed and implemented, which improved the real-time performance of the system and the utilization rate of the hardware resources compared with the traditional LUT and CORDIC method. For a 16-bit fixed-point data bit width single-channel emulation, the hardware resource consumption was significantly reduced from 36.56% to 15.62% for the overall system by utilizing the compact time-division (TD) structure. Moreover, the classical CORDIC method brought an extra latency of 16 system clock cycles, while the latency caused by the improved CORDIC method was decreased by 62.5%. Finally, a generation scheme of a correlated Gaussian sequence was developed to introduce a controllable arbitrary space-time correlation for the channel generator with multiple channels. The output results of the developed generator were consistent with the theoretical results, which verified the correctness of both the generation method and hardware implementation. The proposed channel fading generator can be applied for the emulation of large-scale multiple-input, multiple-output (MIMO) channels under various dynamic communication scenarios.

A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations.

A new non-stationary (NS) geometry-based stochastic model (GBSM) is presented for developing and testing the communication systems of vehicle-to-vehicle (V2V) applications, which considers the three-dimensional (3D) scattering environments and allows 3D velocity as well. In this paper, the proposed GBSM for NS V2V channels allowed 3D velocity variations and was more suitable for actual V2V communications because it provided smoother transitions between the consecutive channel segments. The time-variant channel coefficient and the channel parameters, i.e., Doppler frequencies, path delay and power, angle of arrival (AoA), and angle of departure (AoD), were analyzed and derived. Likewise, the theoretical statistical properties as the probability density function (PDF), the auto-correlation function (ACF), and Doppler power spectral density (DPSD) were also analyzed and derived under the von Mises-Fisher (VMF) distribution. Finally, the theoretical and measured results were well coordinated alongside the implemented results, which confirmed the feasibility of the introduced model along with the theoretical expressions.

A Geometry-Based Beamforming Channel Model for UAV mmWave Communications.

Considering the three-dimensional (3D) trajectory, 3D antenna array, and 3D beamforming of unmanned aerial vehicle (UAV), a novel non-stationary millimeter wave (mmWave) geometry-based stochastic model for UAV to vehicle communication channels is proposed. Based on the analysis results of measured and ray tracing simulation data of UAV mmWave communication links, the proposed parametric channel model is constructed by a line-of-sight path, a ground specular path, and two strongest single-bounce paths. Meanwhile, a new parameter computation method is also developed, which is divided into the deterministic (or geometry-based) part and the random (or empirical) part. The simulated power delay profile and power angle profile demonstrate that the statistical properties of proposed channel model are time-variant with respect to the scattering scenarios, positions and beam direction. Moreover, the simulation results of autocorrelation functions fit well with the theoretical ones as well as the measured ones.

Evolutionary analyses of mitochondrial carrier family of dictyostelids.

The transportation of solutes across the inner membrane of the mitochondria is catalyzed by a nuclear-coded family of transport proteins called mitochondrial carriers (MCs). Sequences from dictyostelid genome projects have facilitated analysis of the evolution of the dictyostelid mitochondrial carrier family (MCF). The average evolutionary distances between various regions in the MCF shows that the transmembrane region (TR) and conical pit region (CPR) are the only two conserved structural regions. A phylogenetic tree built using the concatenated orthologous TR and CPR sequences of 7 MCs showed that dictyostelids are similar to metazoans in this way. A close evolutionary relationship was observed between dictyostelids and metazoans in 4 MCs known to be related to ADP/ATP transport (MAA). This was further evidenced by the fact that dictyostelids have undergone gene expansion similar to that of metazoans during the evolution of MAA. Sequence logo analysis of CPR in MAA showed that dictyostelids have motifs similar to those of Metazoa. Combined with the conserved substrate binding site of 7 MCs in eukaryotes, it is postulated that dictyostelids are closely related to Metazoa with respect to the evolution of MAA.

A newly discovered ubiquitin-conjugating enzyme E2 correlated with the cryogenic autolysis of Volvariella volvacea.

In Volvariella volvacea, a species of edible mushroom, cryogenic autolysis is a typical part of abnormal metabolism. Previous functional annotation cluster analyses of cold-induced gene expression profiles have shown that the ubiquitin-conjugating enzyme E2 (UBE2), rather than the cyclin-like F-box domain alone, forms the functional cluster. In this study, analysis of gene expression profiling showed that only one type of UBE2 in V. volvacea (UBEV2) was significantly up-regulated. Further quantitative real-time PCR analysis confirmed that the expression of UBEV2 was significantly up-regulated (P<0.05) after cold-treatment lasting 4, 6, and 8h. This provided evidence that UBEV2 was closely correlated with cryogenic autolysis. The specific distribution of UBEV2 in recently diverged herb decay fungi indicated that UBEV2 was not evolutionarily correlated with early diverging fungi. Phylogenetic analysis indicated that UBEV2 was generated by horizontal gene transfer (HGT) from the ancestry of Selaginella moellendorffii UBE2. Further relative time estimation and detection of natural selection showed that there has been recent positive selection after HGT in UBEV2. Molecular modeling and logo analysis showed that the cysteine-cysteine motif is the characteristic of the UBEV2 family. These observations indicate that UBEV2 is a new type of UBE2 correlated with the cryogenic autolysis of V. volvacea.

A specific type of cyclin-like F-box domain gene is involved in the cryogenic autolysis of Volvariella volvacea.

Cryogenic autolysis is a typical phenomenon of abnormal metabolism in Volvariella volvacea. Recent studies have identified 20 significantly up-regulated genes via high-throughput sequencing of the mRNAs expressed in the mycelia of V. volvacea after cold exposure. Among these significantly up-regulated genes, 15 annotated genes were used for functional annotation cluster analysis. Our results showed that the cyclin-like F-box domain (FBDC) formed the functional cluster with the lowest P-value. We also observed a significant expansion of FBDC families in V. volvacea. Among these, the FBDC3 family displayed the maximal gene expansion in V. volvacea. Gene expression profiling analysis revealed only one FBDC gene in V. volvacea (FBDV1) that was significantly up-regulated, which is located in the FBDC3 family. Comparative genomics analysis revealed the homologous sequences of FBDV1 with high similarity were clustered on the same scaffold. However, FBDV1 was located far from these clusters, indicating the divergence of duplicated genes. Relative time estimation and rate test provided evidence for the divergence of FBDV1 after recent duplications. Real-time RT-PCR analysis confirmed that the expression of the FBDV1 was significantly up-regulated (P < 0.001) after cold-treatment of V. volvacea for 4 h. These observations suggest that the FBDV1 is involved in the cryogenic autolysis of V. volvacea.

Min-max hyperellipsoidal clustering for anomaly detection in network security.

A novel hyperellipsoidal clustering technique is presented for an intrusion-detection system in network security. Hyperellipsoidal clusters toward maximum intracluster similarity and minimum intercluster similarity are generated from training data sets. The novelty of the technique lies in the fact that the parameters needed to construct higher order data models in general multivariate Gaussian functions are incrementally derived from the data sets using accretive processes. The technique is implemented in a feedforward neural network that uses a Gaussian radial basis function as the model generator. An evaluation based on the inclusiveness and exclusiveness of samples with respect to specific criteria is applied to accretively learn the output clusters of the neural network. One significant advantage of this is its ability to detect individual anomaly types that are hard to detect with other anomaly-detection schemes. Applying this technique, several feature subsets of the tcptrace network-connection records that give above 95% detection at false-positive rates below 5% were identified.

Hierarchical Kohonenen net for anomaly detection in network security.

A novel multilevel hierarchical Kohonen Net (K-Map) for an intrusion detection system is presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map. One significant advantage of this multilevel hierarchical K-Map is its computational efficiency. Unlike other statistical anomaly detection methods such as nearest neighbor approach, K-means clustering or probabilistic analysis that employ distance computation in the feature space to identify the outliers, our approach does not involve costly point-to-point computation in organizing the data into clusters. Another advantage is the reduced network size. We use the classification capability of the K-Map on selected dimensions of data set in detecting anomalies. Randomly selected subsets that contain both attacks and normal records from the KDD Cup 1999 benchmark data are used to train the hierarchical net. We use a confidence measure to label the clusters. Then we use the test set from the same KDD Cup 1999 benchmark to test the hierarchical net. We show that a hierarchical K-Map in which each layer operates on a small subset of the feature space is superior to a single-layer K-Map operating on the whole feature space in detecting a variety of attacks in terms of detection rate as well as false positive rate.

Algorithmic fusion of gene expression profiling for diffuse large B-cell lymphoma outcome prediction.

Many different methods and techniques have been investigated for the processing and analysis of microarray gene expression profiling datasets. It is noted that the accuracy and reliability of the results are often dependent on the measurement approaches applied, and no single measurement so far is guaranteed to generate a satisfactory result. In this paper, an algorithmic fusion approach is presented for extracting genes that are predictive to clinical outcomes (survival-fatal) of diffuse large B-cell lymphoma on a set of microarray data for gene expression profiling. The approach integrates a set of measurements from different aspects in terms of the discrepancy indications and merit expectations of the gene expression patterns with respect to the clinical outcomes. A combination of statistical and non-statistical criteria, continuous and discrete parameterizations, as well as model-based and modeless evaluations is applied in the approach. By integrating these measurements, a set of genes that are indicative to the clinical outcomes are better captured from the gene expression profiling dataset.

A global learning algorithm for a RBF network.

This article presents a new learning algorithm for the construction and training of a RBF neural network. The algorithm is based on a global mechanism of parameter learning using a maximum likelihood classification approach. The resulting neurons in the RBF network partitions a multidimensional pattern space into a set of maximum-size hyper-ellipsoid subspaces in terms of the statistical distributions of the training samples. An important feature of the algorithm is that the learning process includes both the tasks of discovering a suitable network structure and of determining the connection weights. The entire network and its parameters are thought of evolved gradually in the learning process.