A New Species of \u3cem\u3eDiscinites\u3c/em\u3e (Noeggerathiales) Associated with a New Species of \u3cem\u3eYuania\u3c/em\u3e from the Lower Permian of Inner Mongolia, China

A fructification Discinites baculiformis sp. nov. and the foliage Yuania wudensis sp. nov. are described from the lower Permian Shanxi Formation of Wuda, Inner Mongolia, North China. Discinites baculiformis sp. nov. is at least 31 cm long and 1.5–1.8 cm wide. It has more than 78 whorls of sporophylls, each with ca. 85 sporangia on the adaxial side, a total of more than 6630 sporangia. The impression of the sporophyll epidermis is preserved, and cells are visible. In situ trilete spores are detected. The new species represents the longest strobilus with the largest number of whorls of sporophyll disks so far known in the genus. Yuania wudensis sp. nov. has unbranched rachises, with alternate to subopposite elongate ellipsoidal pinnae. Epidermal cells are rectangular, long, and narrow. The two new species might represent the fructification and foliage of the same parent plant, since there is no other noeggerathialean member in the taphonomic plant community. The association is comparable with the association of Discinites and Russellites from the Permian of Texas

Effects of Design Parameters on Performance of Brushless Electrically Excited Synchronous Reluctance Generator

Permanent magnet synchronous generators, doubly fed induction generators, and traditional electrically excited synchronous generators are widely used for wind power applications, especially large offshore installations. In order to eliminate brushes and slip rings for improved reliability and maintenance-free operation, as well as to avoid costly permanent magnets, a novel brushless electrically excited synchronous reluctance generator having many outstanding advantages has been proposed in this paper. The fundamental operating principles, finite element analysis design studies and performance optimization aspects have been thoroughly investigated by simulations and experimentally under different loading conditions. The effects of different pole combinations and rotor dimensions on the magnetic coupling capacity of this machine have been specifically addressed and fully verified by off-line testing of the 6/2 pole and 8/4 pole prototypes with magnetic barrier reluctance rotor and a new hybrid cage rotor offering superior performance

A new fault diagnosis method using deep belief network and compressive sensing

Compressive sensing provides a new idea for machinery monitoring, which greatly reduces the burden on data transmission. After that, the compressed signal will be used for fault diagnosis by feature extraction and fault classification. However, traditional fault diagnosis heavily depends on the prior knowledge and requires a signal reconstruction which will cost great time consumption. For this problem, a deep belief network (DBN) is used here for fault detection directly on compressed signal. This is the first time DBN is combined with the compressive sensing. The PCA analysis shows that DBN has successfully separated different features. The DBN method which is tested on compressed gearbox signal, achieves 92.5 % accuracy for 25 % compressed signal. We compare the DBN on both compressed and reconstructed signal, and find that the DBN using compressed signal not only achieves better accuracies, but also costs less time when compression ratio is less than 0.35. Moreover, the results have been compared with other classification methods

Human-Manipulator Interface Using Hybrid Sensors via CMAC for Dual Robots

This paper presents a novel method that allows a human operator to communicate his motion to the dual robot manipulators by performing his two double hand-arms movements, which would naturally carry out an object manipulation task. The proposed method uses hybrid sensors to obtain the position and orientation of the human hands. Although the position and the orientation of the human can be obtained from the sensors, the measurement errors increase over time due to the noise of the devices and the tracking error. A cerebellar model articulation controller (CMAC) is used to estimate the position and orientation of the human hand. Due to the limitations of the perceptive and the motor, human operator can not accomplish the high precision manipulation without any assistant. An adaptive multi-space transformation (AMT) is employed to assist the operator to improve the accuracy and reliability in determining the pose of the manipulator. With making full use of the human hand-arms motion, the operator would feel kind of immersive. Using this human-robot interface, the object manipulation task done in collaboration by dual robots could be carried out flexibly through preferring the double hand-arms motion by one operator

Novel Serine 176 Phosphorylation of YBX1 Activates NF-κB in Colon Cancer

Y box protein 1 (YBX1) is a well known oncoprotein that has tumor-promoting functions. YBX1 is widely considered to be an attractive therapeutic target in cancer. To develop novel therapeutics to target YBX1, it is of great importance to understand how YBX1 is finely regulated in cancer. Previously, we have shown that YBX1 could function as a tumor promoter through phosphorylation of its Ser-165 residue, leading to the activation of the NF-κB signaling pathway (1). In this study, using mass spectrometry analysis, we discovered a distinct phosphorylation site, Ser-176, on YBX1. Overexpression of the YBX1-S176A (serine-to-alanine) mutant in either HEK293 cells or colon cancer HT29 cells showed dramatically reduced NF-κB-activating ability compared with that of WT-YBX1, confirming that Ser-176 phosphorylation is critical for the activation of NF-κB by YBX1. Importantly, the mutant of Ser-176 and the previously reported Ser-165 sites regulate distinct groups of NF-κB target genes, suggesting the unique and irreplaceable function of each of these two phosphorylated serine residues. Our important findings could provide a novel cancer therapy strategy by blocking either Ser-176 or Ser-165 phosphorylation or both of YBX1 in colon cancer

A review of stimulated reservoir volume characterization for multiple fractured horizontal well in unconventional reservoirs

Unconventional resource exploration has boosted U.S. oil and gas production, which is successfully by horizontal well drilling and hydraulic fracturing. The horizontal well with multiple transverse fractures has proven to be effective stimulation approach could increase reservoir contact significantly. Unlike the single fracture planes in typical low permeability sands, fractures in shales tends to generate more complex, branching networks. The concept of stimulated reservoir volume was developed to quantitative measure of multistage fracture interact with natural fractures in unconventional reservoir. However, the simple fracture modeling of the past do not suitable for the complex scenarios simulation. This paper reviews the mainstream characterization method of stimulated reservoir volume in shale reservoirs, including microseismic interpretation, rate transient analysis method, analytical and semi-analytical method and numerical method. Finally, the systematic evaluation of application conditions with respect to each method and further research directions for characterization method are proposed.Cited as: Wang, W., Zheng, D., Sheng, G., et al. A review of stimulated reservoir volume characterization for multiple fractured horizontal well in unconventional reservoirs. Advances in Geo-Energy Research, 2017, 1(1): 54-63, doi: 10.26804/ager.2017.01.0

Highly robust model of transcription regulator activity predicts breast cancer overall survival

Background: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. Methods: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. Result: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. Conclusion: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression

A Locality-based Neural Solver for Optical Motion Capture

We present a novel locality-based learning method for cleaning and solving optical motion capture data. Given noisy marker data, we propose a new heterogeneous graph neural network which treats markers and joints as different types of nodes, and uses graph convolution operations to extract the local features of markers and joints and transform them to clean motions. To deal with anomaly markers (e.g. occluded or with big tracking errors), the key insight is that a marker's motion shows strong correlations with the motions of its immediate neighboring markers but less so with other markers, a.k.a. locality, which enables us to efficiently fill missing markers (e.g. due to occlusion). Additionally, we also identify marker outliers due to tracking errors by investigating their acceleration profiles. Finally, we propose a training regime based on representation learning and data augmentation, by training the model on data with masking. The masking schemes aim to mimic the occluded and noisy markers often observed in the real data. Finally, we show that our method achieves high accuracy on multiple metrics across various datasets. Extensive comparison shows our method outperforms state-of-the-art methods in terms of prediction accuracy of occluded marker position error by approximately 20%, which leads to a further error reduction on the reconstructed joint rotations and positions by 30%. The code and data for this paper are available at https://github.com/non-void/LocalMoCap.Comment: Siggraph Asia 2023 Conference Pape