A SLAM Algorithm Based on Adaptive Cubature Kalman Filter

We need to predict mathematical model of the system and a priori knowledge of the noise statistics when traditional simultaneous localization and mapping (SLAM) solutions are used. However, in many practical applications, prior statistics of the noise are unknown or time-varying, which will lead to large estimation errors or even cause divergence. In order to solve the above problem, an innovative cubature Kalman filter-based SLAM (CKF-SLAM) algorithm based on an adaptive cubature Kalman filter (ACKF) was established in this paper. The novel algorithm estimates the statistical parameters of the unknown system noise by introducing the Sage-Husa noise statistic estimator. Combining the advantages of the CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM system is significantly improved. It verifies that the proposed ACKF-SLAM algorithm is valid and feasible

Prospero and Pax2 combinatorially control neural cell fate decisions by modulating Ras- and Notch-dependent signaling

<p>Abstract</p> <p>Background</p> <p>The concept of an equivalence group, a cluster of cells with equal potential to adopt the same specific fate, has served as a useful paradigm to understand neural cell type specification. In the <it>Drosophila </it>eye, a set of five cells, called the 'R7 equivalence group', generates a single photoreceptor neuron and four lens-secreting epithelial cells. This choice between neuronal versus non-neuronal cell fates rests on differential requirements for, and cross-talk between, Notch/Delta- and Ras/mitogen-activated protein kinase (MAPK)-dependent signaling pathways. However, many questions remain unanswered related to how downstream events of these two signaling pathways mediate distinct cell fate decisions.</p> <p>Results</p> <p>Here, we demonstrate that two direct downstream targets of Ras and Notch signaling, the transcription factors Prospero and dPax2, are essential regulators of neuronal versus non-neuronal cell fate decisions in the R7 equivalence group. Prospero controls high activated MAPK levels required for neuronal fate, whereas dPax2 represses Delta expression to prevent neuronal fate. Importantly, activity from both factors is required for proper cell fate decisions to occur.</p> <p>Conclusions</p> <p>These data demonstrate that Ras and Notch signaling are integrated during cell fate decisions within the R7 equivalence group through the combinatorial and opposing activities of Pros and dPax2. Our study provides one of the first examples of how the differential expression and synergistic roles of two independent transcription factors determine cell fate within an equivalence group. Since the integration of Ras and Notch signaling is associated with many developmental and cancer models, these findings should provide new insights into how cell specificity is achieved by ubiquitously used signaling pathways in diverse biological contexts.</p

Efficient Genetic Engineering with Rolling Circle Amplification

Mentor: Limin Li, Peking Union Medical College From the Washington University Undergraduate Research Digest: WUURD, Volume 6, Issue 2, Spring 2011. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Kristin Sobotka, Editor

Heterogeneous Attributed Network Embedding with Graph Convolutional Networks

Network embedding which assigns nodes in networks to lowdimensional representations has received increasing attention in recent years. However, most existing approaches, especially the spectral-based methods, only consider the attributes in homogeneous networks. They are weak for heterogeneous attributed networks that involve different node types as well as rich node attributes and are common in real-world scenarios. In this paper, we propose HANE, a novel network embedding method based on Graph Convolutional Networks, that leverages both the heterogeneity and the node attributes to generate high-quality embeddings. The experiments on the real-world dataset show the effectiveness of our method

Characterization of N-Terminal Asparagine Deamidation and Clipping of a Monoclonal Antibody

This study presents a novel degradation pathway of a human immunoglobulin G (IgG) molecule featuring a light chain N-terminal asparagine. We thoroughly characterize this pathway and investigate its charge profiles using cation exchange chromatography (CEX) and capillary isoelectric focusing (cIEF). Beyond the well-documented asparagine deamidation into isoaspartic acid, aspartic acid, and succinimide intermediate, a previously unreported clipping degradation pathway is uncovered. This newly identified clipped N-terminal IgG variant exhibits a delayed elution in CEX, categorized as a “basic variant”, while retaining the same main peak isoelectric point (pI) in cIEF. The influence of temperature and pH on N-terminal asparagine stability is assessed across various stressed conditions. A notable correlation between deamidation percentage and clipped products is established, suggesting a potential hydrolytic chemical reaction underlying the clipping process. Furthermore, the impact of N-terminal asparagine modifications on potency is evaluated through ELISA binding assays, revealing minimal effects on binding affinity. Sequence alignment reveals homology to a human IgG with the germline gene from Immunoglobulin Lambda Variable 6-57 (IGLV6-57), which has implications for amyloid light-chain (AL) amyloidosis. This discovery of the N-terminal clipping degradation pathway contributes to our understanding of immunoglobulin light chain misfolding and amyloid fibril deposition under physiological conditions

Association between Bullying Victimization and Symptoms of Depression among Adolescents: A Moderated Mediation Analysis

Background: Bullying victimization and its effect on symptoms of depression have received attention from researchers, but few studies have considered the potential mechanism. The aim of this study was to examine a moderated mediation model for the association between bullying victimization and depressive symptoms in terms of it being mediated by social anxiety, and investigated whether sleep duration would show moderating effects in this relationship. Methods: In this study, there were 2956 students, who completed three questionnaires, including a bullying victimization scale, as well as a social anxiety and epidemiologic studies depression scale. Results: Bullying victimization’s effects on depressive symptoms were mediated by social anxiety. Furthermore, sleep duration moderated the relationship between bullying victimization and depressive symptoms. Conclusions: The research contributes by clarifying the mechanisms underlying the relationship between bullying victimization and depressive symptoms

Table_4_Prioritizing key synergistic circulating microRNAs for the early diagnosis of biliary tract cancer.xls

Biliary tract cancer (BTC) is a highly aggressive malignant tumor. Serum microRNAs (ser-miRNAs) serve as noninvasive biomarkers to identify high risk individuals, thereby facilitating the design of precision therapies. The study is to prioritize key synergistic ser-miRNAs for the diagnosis of early BTC. Sampling technology, significant analysis of microarrays, Pearson Correlation Coefficients, t-test, decision tree, and entropy weight were integrated to develop a global optimization algorithm of decision forest. The source code is available at https://github.com/SuFei-lab/GOADF.git. Four key synergistic ser-miRNAs were prioritized and the synergistic classification performance was better than the single miRNA’ s. In the internal feature evaluation dataset, the area under the receiver operating characteristic curve (AUC) for each single miRNA was 0.8413 (hsa-let-7c-5p), 0.7143 (hsa-miR-16-5p), 0.8571 (hsa-miR-17-5p), and 0.9365 (hsa-miR-26a-5p), respectively, whereas the synergistic AUC value increased to 1.0000. In the internal test dataset, the single AUC was 0.6500, 0.5125, 0.6750, and 0.7500, whereas the synergistic AUC increased to 0.8375. In the independent test dataset, the single AUC was 0.7280, 0.8313, 0.8957, and 0.8303, and the synergistic AUC was 0.9110 for discriminating between BTC patients and healthy controls. The AUC for discriminating BTC from pancreatic cancer was 0.9000. Hsa-miR-26a-5p was a predictor of prognosis, patients with high expression had shorter survival than those with low expression. In conclusion, hsa-let-7c-5p, hsa-miR-16-5p, hsa-miR-17-5p, and hsa-miR-26a-5p may act as key synergistic biomarkers and provide important molecular mechanisms that contribute to pathogenesis of BTC.</p