Shape asymmetries and lopsidedness-radial-alignment in simulated galaxies

Galaxies are observed to be lopsided, meaning that they are more massive and more extended along one direction than the opposite. However, the galaxies generated in cosmological simulations are much less lopsided, inconsistent with observations. In this work, we provide a statistical analysis of the lopsided morphology of 2148 simulated isolated satellite galaxies generated by TNG50-1 simulation, incorporating the effect of tidal fields from halo centres. We study the radial alignment (RA) between the major axes of satellites and the radial direction of their halo centres within truncation radii of $3R_h$, $5R_h$ and $10R_h$. According to our results, RA is absent for all these truncations. We also calculate the far-to-near-side semi-axial ratios of the major axes, denoted by $a_-/a_+$, which measures the semi-axial ratios of the major axes in the hemispheres between backwards (far-side) and facing (near-side) the halo centres. If the satellites are truncated within radii of $3R_h$ and $5R_h$ with $R_h$ being the stellar half mass radius, the numbers of satellites with longer semi-axes on the far-side are found to be almost equal to those with longer semi-axes on the near-side. Within a larger truncated radius of $10R_h$, the number of satellites with axial ratios $a_-/a_+ <1.0$ is about $10\%$ more than that with $a_-/a_+ > 1.0$. Therefore, the tidal fields from halo centres play a minor role in the generation of lopsided satellites. The lopsidedness radial alignment (LRA), i.e., an alignment of long semi-major-axes along the radial direction of halo centres, is further studied. No clear evidence of LRA is found in our sample within the framework of $\Lambda$CDM Newtonian dynamics. In comparison, the LRA can be naturally induced by the external fields from the central host galaxy in Milgromian dynamics. (See paper for full abstract)Comment: 16 pages, 12 figures, 3 tables, submitted to MNRA

A novel flow-guide device for uniform exhaust in a central air exhaust ventilation system

Exhaust ventilation system with one central fan and multiple terminals has been widely used for the heat and contaminant removal in building environment. Conventional design without pressure balancing leads to uneven distribution of exhaust airflow rate among the multiple outlets. Existed balancing methods usually uses dampers (constant-air-volume valve or regulating valve), tapered duct, or varied inlet area. However, these methods result in higher fan energy consumption, or complicated construction and on-site commissioning. In this paper, a flow-guide device was developed for adjusting the pressure distribution of duct branches. This new device is integrated with the interflow Tee-junction and does not need any commissioning or regulating. The resistance performance of the device responding to the structural parameter was derived using the CFD simulation and experiment. The negative direct resistance featured by the device was found to effectively benefit exhaust at the outlets farther away from the central fan. The ductwork hydraulic model based on the Bernoulli's law of airflow and the fitted resistance correlations were further proposed to fulfill the parametric design. Finally, full-scale test was carried out for a central exhaust system installed with the flow-guide devices referring to a factory workshop with heat and contaminant sources. Compared to the system without the devices, the total rate of the system increased by 25%. Discrepancy of exhaust rate decreased by 78% and uneven degree decreased by 82%, which well meets the engineering balancing requirement. Meanwhile, total resistance of the system reduced 23.8% owing to the negative loss the devices bring

A prognostic signature based on snoRNA predicts the overall survival of lower-grade glioma patients

IntroductionSmall nucleolar RNAs (snoRNAs) are a group of non-coding RNAs enriched in the nucleus which direct post-transcriptional modifications of rRNAs, snRNAs and other molecules. Recent studies have suggested that snoRNAs have a significant role in tumor oncogenesis and can be served as prognostic markers for predicting the overall survival of tumor patients. MethodsWe screened 122 survival-related snoRNAs from public databases and eventually selected 7 snoRNAs that were most relevant to the prognosis of lower-grade glioma (LGG) patients for the establishment of the 7-snoRNA prognostic signature. Further, we combined clinical characteristics related to the prognosis of glioma patients and the 7-snoRNA prognostic signature to construct a nomogram.ResultsThe prognostic model displayed greater predictive power in both validation set and stratification analysis. Results of enrichment analysis revealed that these snoRNAs mainly participated in the post-transcriptional process such as RNA splicing, metabolism and modifications. In addition, 7-snoRNA prognostic signature were positively correlated with immune scores and expression levels of multiple immune checkpoint molecules, which can be used as potential biomarkers for immunotherapy prediction. From the results of bioinformatics analysis, we inferred that SNORD88C has a major role in the development of glioma, and then performed in vitro experiments to validate it. The results revealed that SNORD88C could promote the proliferation, invasion and migration of glioma cells. DiscussionWe established a 7-snoRNA prognostic signature and nomogram that can be applied to evaluate the survival of LGG patients with good sensitivity and specificity. In addition, SNORD88C could promote the proliferation, migration and invasion of glioma cells and is involved in a variety of biological processes related to DNA and RNA

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A New Multiple-Distribution GAN Model to Solve Complexity in End-to-End Chromosome Karyotyping

With significant development of Internet of medical things (IoMT) and cloud-fog-edge computing, medical industry is now involving medical big data to improve quality of service in patient care. Karyotyping refers classifying human chromosomes. However, performing karyotyping task generally requires domain expertise in cytogenetics, long-period experience for high accuracy, and considerable manual efforts. An end-to-end chromosome karyotype analysis system is proposed over medical big data to automatically and accurately perform chromosome related tasks of detection, segmentation, and classification. Facing image data generated and collected by means of edge computing, we firstly utilize visual feature to generate chromosome candidates with Extremal Regions (ER) technology. Due to severe occlusion and cross overlapping, we utilize ring radius transform to cluster pixels with same property to approximate chromosome shapes. To solve the problem of unbalanced and small dataset by covering diverse data patterns, we proposed multidistributed generated advertising network (MD-GAN) to perform data enhancement by generating additional training samples. Afterwards, we fine-tune CNN for chromosome classification task by involving generated and sufficient training images. Through experiments in self-collected datasets, the proposed method achieves high accuracy in tasks of chromosome detection, segmentation, and classification. Moreover, experimental results prove that MD-GAN-based data enhancement contributes to classification results of CNN to a certain extent

Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey

With the development of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have emerged to wireless communication system, especially in cybersecurity. In this paper, we offer a review on attack detection methods involving strength of deep learning techniques. Specifically, we firstly summarize fundamental problems of network security and attack detection and introduce several successful related applications using deep learning structure. On the basis of categorization on deep learning methods, we pay special attention to attack detection methods built on different kinds of architectures, such as autoencoders, generative adversarial network, recurrent neural network, and convolutional neural network. Afterwards, we present some benchmark datasets with descriptions and compare the performance of representing approaches to show the current working state of attack detection methods with deep learning structures. Finally, we summarize this paper and discuss some ways to improve the performance of attack detection under thoughts of utilizing deep learning structures