Robust Multiple-View Geometry Estimation Based on GMM

Given three partially overlapping views of the scene from which a set of point or line correspondences have been extracted, 3D structure and camera motion parameters can be represented by the trifocal tensor, which is the key to many problems of computer vision on three views. Unlike in conventional typical methods, the residual value is the only rule to eliminate outliers with large value, we build a Gaussian mixture model assuming that the residuals corresponding to the inliers come from Gaussian distributions different from that of the residuals of outliers. Then Bayesian rule of minimal risk is employed to classify all the correspondences using the parameters computed from GMM. Experiments with both synthetic data and real images show that our method is more robust and precise than other typical methods because it can efficiently detect and delete the bad corresponding points, which include both bad locations and false matches

Magnetic-Assisted Initialization for Infrastructure-free Mobile Robot Localization

Most of the existing mobile robot localization solutions are either heavily dependent on pre-installed infrastructures or having difficulty working in highly repetitive environments which do not have sufficient unique features. To address this problem, we propose a magnetic-assisted initialization approach that enhances the performance of infrastructure-free mobile robot localization in repetitive featureless environments. The proposed system adopts a coarse-to-fine structure, which mainly consists of two parts: magnetic field-based matching and laser scan matching. Firstly, the interpolated magnetic field map is built and the initial pose of the mobile robot is partly determined by the k-Nearest Neighbors (k-NN) algorithm. Next, with the fusion of prior initial pose information, the robot is localized by laser scan matching more accurately and efficiently. In our experiment, the mobile robot was successfully localized in a featureless rectangular corridor with a success rate of 88% and an average correct localization time of 6.6 seconds

Vertical Ferroelectricity in Van der Waals Materials: Models and Devices

Ferroelectricity has a wide range of applications in functional electronics and is extremely important for the development of next-generation information storage technology, but it is difficult to achieve due to its special symmetry requirements. In this letter, based on van derWaals stacking, a generic model is proposed for realizing ferroelectric devices, where a freely movable center layer is packaged in two fixed and symmetrically stacked layers. In this model, the ferroelectric phase transition can be realized between the two equivalent and eccentric ground stacking-states with opposite polarizations. By means of first-principles calculations, taking the h-BN/h-BN/h-BN and h-BN/Graphene/h-BN as feasible models, we carefully evaluate the magnitude of ferroelectricity. The corresponding polarizations are estimated as 1.83 and 1.35 pC/m, respectively, which are comparable to the sliding ferroelectricity. Such a new tri-layer model of vertical ferroelectricity can be constructed by arbitrary van derWaals semiconducting materials, and usually holds low switching barrier. Optimized material combinations with remarkable polarization are highly expectable to be discovered from the huge candidate set for future information storage.Comment: 5 pages;4 figure

Analysis of risk factors for deep vein thrombosis after spinal infection surgery and construction of a nomogram preoperative prediction model

ObjectiveTo investigate the differences in postoperative deep venous thrombosis (DVT) between patients with spinal infection and those with non-infected spinal disease; to construct a clinical prediction model using patients’ preoperative clinical information and routine laboratory indicators to predict the likelihood of DVT after surgery.MethodAccording to the inclusion criteria, 314 cases of spinal infection (SINF) and 314 cases of non-infected spinal disease (NSINF) were collected from January 1, 2016 to December 31, 2021 at Xiangya Hospital, Central South University, and the differences between the two groups in terms of postoperative DVT were analyzed by chi-square test. The spinal infection cases were divided into a thrombotic group (DVT) and a non-thrombotic group (NDVT) according to whether they developed DVT after surgery. Pre-operative clinical information and routine laboratory indicators of patients in the DVT and NDVT groups were used to compare the differences between groups for each variable, and variables with predictive significance were screened out by least absolute shrinkage and operator selection (LASSO) regression analysis, and a predictive model and nomogram of postoperative DVT was established using multi-factor logistic regression, with a Hosmer- Lemeshow goodness-of-fit test was used to plot the calibration curve of the model, and the predictive effect of the model was evaluated by the area under the ROC curve (AUC).ResultThe incidence of postoperative DVT in patients with spinal infection was 28%, significantly higher than 16% in the NSINF group, and statistically different from the NSINF group (P < 0.000). Five predictor variables for postoperative DVT in patients with spinal infection were screened by LASSO regression, and plotted as a nomogram. Calibration curves showed that the model was a good fit. The AUC of the predicted model was 0.8457 in the training cohort and 0.7917 in the validation cohort.ConclusionIn this study, a nomogram prediction model was developed for predicting postoperative DVT in patients with spinal infection. The nomogram included five preoperative predictor variables, which would effectively predict the likelihood of DVT after spinal infection and may have greater clinical value for the treatment and prevention of postoperative DVT

Integrative analysis of the role of BOLA2B in human pan-cancer

Objective:BOLA2B is a recently discovered protein-coding gene. Here, pan-cancer analysis was conducted to determine the expression patterns of BOLA2B and its impact on immune response, gene mutation, and possible molecular biological mechanisms in different tumors, together with investigating its potential usefulness for cancer prognosis.Methods: Data on BOLA2B expression and mutations were downloaded from TCGA and GTEx databases. Clinical survival data from TCGA were used to analyze the prognostic value of BOLA2B. TIMER and ESTIMATE algorithms were used to assess correlations between BOLA2B and tumor-infiltrating immune cells, immune cytokines, and immune scores.Results: BOLA2B was found to be highly expressed at both mRNA and protein levels in multiple tumors, where it was associated with worse overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in all cancers apart from ovarian cancer. BOLA2B was also found to be positively correlated with copy number variation (CNV), and mutations in TP53, TTN, and MUC16 were found to influence BOLA2B expression. Post-transcriptional modifications, including m5C, m1A, and m6A, were observed to regulate BOLA2B expression in all cancers. Functional analysis showed that BOLA2B was enriched in pathways associated with iron–sulfur cluster formation, mTOR-mediated autophagy, and cell cycle inhibition. Decreased BOLA2B expression induced the proliferation of breast cancer cells and G2/M cell cycle arrest.Conclusion:BOLA2B was found to be highly expressed in malignant tumors and could be used as a biomarker of poor prognosis in multiple cancers. Further investigation into BOLA2B’s role and molecular functions in cancer would provide new insights for cancer diagnosis and treatment

Identification and characterization of mRNAs and lncRNAs in the uterus of polytocous and monotocous Small Tail Han sheep (Ovis aries)

Background Long non-coding RNAs (lncRNAs) regulate endometrial secretion and uterine volume. However, there is little research on the role of lncRNAs in the uterus of Small Tail Han sheep (FecB++). Herein, RNA-seq was used to comparatively analyze gene expression profiles of uterine tissue between polytocous and monotocous sheep (FecB++) in follicular and luteal phases. Methods To identify lncRNA and mRNA expressed in the uterus, the expression of lncRNA and mRNA in the uterus of Small Tail Han sheep (FecB++) from the polytocous group (n = 6) and the monotocous group (n = 6) using RNA-sequencing and real-time polymerase chain reaction (RT-PCR). Identification of differentially expressed lncRNAs and mRNAs were performed between the two groups and two phases . Gene ontology (GO) and pathway enrichment analyses were performed to analyze the biological functions and pathways for the differentially expressed mRNAs. LncRNA-mRNA co-expression network was constructed to further analyses the function of related genes. Results In the follicular phase, 473 lncRNAs and 166 mRNAs were differentially expressed in polytocous and monotocous sheep; in the luteal phase, 967 lncRNAs and 505 mRNAs were differentially expressed in polytocous and monotocous sheep. GO and KEGG enrichment analysis showed that the differentially expressed lncRNAs and their target genes are mainly involved in ovarian steroidogenesis, retinol metabolism, the oxytocin signaling pathway, steroid hormone biosynthesis, and the Foxo signaling pathway. Key lncRNAs may regulate reproduction by regulating genes involved in these signaling pathways and biological processes. Specifically, UGT1A1, LHB, TGFB1, TAB1, and RHOA, which are targeted by MSTRG.134747, MSTRG.82376, MSTRG.134749, MSTRG.134751, and MSTRG.134746, may play key regulatory roles. These results offer insight into molecular mechanisms underlying sheep prolificacy

High-Throughput Sequencing Analysis of Endophytic Bacteria Diversity in Fruits of White and Red Pitayas from Three Different Origins

Pitaya contains various types of polyphenols, flavonoid and vitamins which are beneficial for health and it is among the most important commercial tropical fruits worldwide. Endophytic bacteria might be beneficial for plant growth and yield. However, bacterial diversity in pitaya is poorly characterized. In this study, fruits of white and red pitayas from three different origins (Thailand, Vietnam and China) were chosen for endophytic bacteria diversity investigation by using Illumina HiSeq second-generation high-throughput sequencing technology. Large number of endophytic bacteria were detected and 22 phyla, 56 classes, 81 orders, 122 families and 159 genera were identified. Endophytic bacteria diversity was uneven among pitaya fruits from different origins and bacteria structure was different between white pitaya group and red pitaya group. Phylum Bacteroidetes, classes Bacteroidia and Coriobacteriia, orders Bacteroidales and Coriobacteriales, families Prevotellaceae, Bacteroidaceae, Ruminococcaceae, Paraprevotellaceae, Rikenellaceae, Alcaligenaceae and Coriobacteriaceae, genera Prevotella, Bacteroides, Roseburia, Faecalibacterium and Sutterella were statistically significant different species (P < 0.05) between white and red pitayas. These findings might be useful for growth improvement, fruit preservation and processing of different pitaya species from different origins