Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI

Brain network analysis is a useful approach to studying human brain disorders because it can distinguish patients from healthy people by detecting abnormal connections. Due to the complementary information from multiple modal neuroimages, multimodal fusion technology has a lot of potential for improving prediction performance. However, effective fusion of multimodal medical images to achieve complementarity is still a challenging problem. In this paper, a novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices and predict abnormal brain connections based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). Specifically, the prior knowledge is incorporated into the separators for disentangling each modality of information by the graph convolutional networks (GCN). And a disentangled cosine distance loss is devised to ensure the disentanglement's effectiveness. Moreover, the hierarchical representation fusion module is designed to effectively maximize the combination of relevant and effective features between modalities, which makes the generated structural-functional connectivity more robust and discriminative in the cognitive disease analysis. Results from a wide range of tests performed on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that the proposed model performs better than competing approaches in terms of classification evaluation. In general, the proposed HSCF model is a promising model for generating brain structural-functional connectivities and identifying abnormal brain connections as cognitive disease progresses.Comment: 4 figure

Aerosols Monitored by Satellite Remote Sensing

Aerosols, small particles suspended in the atmosphere, affect the air quality and climate change. Their distributions can be monitored by satellite remote sensing. Many images of aerosol properties are available from websites as the by-products of the atmospheric correction of the satellite data. Their qualities depend on the accuracy of the atmospheric correction algorithms. The approaches of the atmospheric correction for land and ocean are different due to the large difference of the ground reflectance between land and ocean. A unified atmospheric correction (UAC) approach is developed to improve the accuracy of aerosol products over land, similar to those over ocean. This approach is developed to estimate the aerosol scattering reflectance from satellite data based on a lookup table (LUT) of in situ measured ground reflectance. The results show that the aerosol scattering reflectance can be completely separated from the satellite measured radiance over turbid waters and lands. The accuracy is validated with the mean relative errors of 22.1%. The vertical structures of the aerosols provide a new insight into the role of aerosols in regulating Earth\u27s weather, climate, and air quality

PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method

Gram-negative bacteria use various secretion systems to deliver their secreted effectors. Among them, type IV secretion system exists widely in a variety of bacterial species, and secretes type IV secreted effectors (T4SEs), which play vital roles in host-pathogen interactions. However, experimental approaches to identify T4SEs are time- and resource-consuming. In the present study, we aim to develop an in silico stacked ensemble method to predict whether a protein is an effector of type IV secretion system or not based on its sequence information. The protein sequences were encoded by the feature of position specific scoring matrix (PSSM)-composition by summing rows that correspond to the same amino acid residues in PSSM profiles. Based on the PSSM-composition features, we develop a stacked ensemble model PredT4SE-Stack to predict T4SEs, which utilized an ensemble of base-classifiers implemented by various machine learning algorithms, such as support vector machine, gradient boosting machine, and extremely randomized trees, to generate outputs for the meta-classifier in the classification system. Our results demonstrated that the framework of PredT4SE-Stack was a feasible and effective way to accurately identify T4SEs based on protein sequence information. The datasets and source code of PredT4SE-Stack are freely available at http://xbioinfo.sjtu.edu.cn/PredT4SE_Stack/index.php

Particle dynamics revealed by 210Po/210Pb disequilibria around Prydz Bay, the Southern Ocean in summer

Seawater samples were collected around Prydz Bay in summer of 2014, dissolved and particulate 210Po and 210Pb were measured to reveal the disequilibrium characteristics and particle dynamics. Our results show that the distribution of 210Po and 210Po/210Pb activity ratio in the upper water is mainly affected by biological absorption or particle adsorption. An abnormal excess of 210Po relative to 210Pb was observed in the surface water at stations P1-2 and P2-2, which is likely to be the horizontal transport of water mass with high DPo/DPb)A.R. and TPo/TPb)A.R.. In this study, the removal of particulate 210Po is mainly controlled by the scavenging of dissolved 210Po and the two have a linear positive correlation with the salinity, a negative linear correlation with the content of dissolved oxygen and a reciprocal relationship with the content of POC. The export flux of POC at 100 m is estimated to be 1.8–4.4 mmol·m−2·d−1 (avg. 2.9 mmol·m−2·d−1) based on 210Po/210Pb disequilibria, with the highest value in the shelf, which is consistent with the distribution of biological productivity

Is hepatic resection always a better choice than radiofrequency ablation for solitary hepatocellular carcinoma regardless of age and tumor size?

In this study, we aimed to compare survival outcomes after receiving radiofrequency ablation (RFA) and hepatic resection (HR) for solitary hepatocellular carcinoma (HCC) with stratification by tumor size and age. A retrospective cohort was obtained from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. Patients were grouped by tumor size (0-2, 2-5, and > 5 cm) and age (>65 and ≤65). Overall survival (OS) and disease-specific survival (DSS) were assessed. For patients >65 with tumors measuring 0-2 and 2-5 cm, the HR group had better OS and DSS compared with the RFA group. For patients >65 with tumors > 5 cm, OS and DSS did not differ significantly between the RFA and HR groups (p = 0.262 and p = 0.129, respectively). For patients ≤65, the HR group had better OS and DSS compared with the RFA group regardless of tumor size. For patients with resectable solitary HCC, regardless of age, HR is the better choice not only for tumors ≤ 2 cm, but also for tumors 2-5 cm. For resectable solitary HCC with tumors ＞5 cm, HR is the better choice for patients ≤65 but for patients >65, the issue of treatment choice needs to be further studied

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences

NRBP1 modulates uric acid transporter ABCG2 expression by activating the Wnt/β-catenin pathway in HK-2 cells

Background: Nuclear receptor binding protein 1 (NRBP1) and ATP-binding cassette subfamily G member 2 (ABCG2) was the gout risk gene and high-capacity urate exporter respectively. However, the relationship between NRBP1 and ABCG2 and the underlying molecular mechanism contributing to these associations are unknown. Methods: Firstly, the efficiency of the overexpression and knockdown of NRBP1 was confirmed by western blot. Next, the effect of NRBP1 overexpression and knockdown on the expression of ABCG2, organic anion transporter 1 (OAT1), glucose transporter 9 (GLUT9) and urate transporter 1 (URAT1) was detected by qRT-PCR and western blot. At the same time, the cellular location of ABCG2 and its expression after NRBP1 overexpression and knockdown was tested by immunofluorescence (IF) staining. Then, the mechanism of NRBP1 modulates ABCG2 expression was evaluated by western blot with or without the β-catenin inhibitor (21H7). Results: The lentivirus system was used to generate stable NRBP1 overexpression, while the plasmids carrying a NRBP1 siRNA was generated to knockdown NRBP1 expression in HK-2 cells. Meanwhile, the overexpression of NRBP1 significantly decreased the mRNAs and proteins expression of GLUT9 and URAT1, while the knockdown of NRBP1 increased the mRNAs and proteins expression of ABCG2 significantly. In addition, the NRBP1 modulates the expression of ABCG2 was by ctivating the Wnt/β-catenin pathway in HK-2 cells according to the IF and western blot results. Conclusion: Taken together, our study demonstrated that NRBP1 inhibition played an essential role in attenuating hyperuricemia and gout by upregulation of ABCG2 via Wnt/β-catenin signaling pathway in HK-2 cells. Resumen: Antecedentes: La proteína de unión al receptor nuclear 1 (NRBP1) y el miembro G de la subclase ATP binding Box 2 (ABCG2) son los genes de riesgo de gota y los genes de salida de urato de alto rendimiento, respectivamente. Sin embargo, se desconoce la relación entre NRBP1 y ABCG2, y los posibles mecanismos moleculares que conducen a estas asociaciones. Métodos: En primer lugar, la sobreexpresión y el knockout de NRBP1 fueron confirmados por Western-blot. Los efectos de la sobreexpresión y knockout de NRBP1 en la expresión de ABCG2, transportador de aniones orgánicos 1 (OAT1), transportador de glucosa 9 (GLUT9) y transportador de ácido úrico 1 (URAT1) fueron detectados por qRT-PCR y Western-blot. Mientras tanto, la localización y expresión de ABCG2 después de la sobreexpresión y knockout de NRBP1 fueron detectadas por inmunofluorescencia (IF). Luego, el efecto regulador de NRBP1 sobre la expresión de ABCG2 fue estudiado por Western-blot y comparado con el inhibidor de la β-catenina (21H7). Resultados: El sistema lentiviral indujo una sobreexpresión estable de NRBP1, mientras que el plásmido portador de SiRNA NRBP1 inhibió la expresión de NRBP1 en las células HK-2. Mientras tanto, la sobreexpresión de NRBP1 redujo significativamente la expresión de ARNm y proteínas de GLUT9 y URAT1, mientras que el knockout de NRBP1 aumentó significativamente la expresión de ARNm y proteínas de ABCG2. Además, de acuerdo con los resultados de IF y Western-blot, NRBP1 regula la expresión de ABCG2 activando la vía Wnt/β-catenina en las células HK-2. Conclusión: La inhibición del NRBP1 aumenta la regulación de ABCG2 a través de la vía de señalización Wnt/β-catenina, que desempeña un papel importante en la reducción de la hiperuricemia y la gota

Global Vibration Comfort Evaluation of Footbridges Based on Computer Vision

The vibration comfort evaluation is a control standard other than strength and deflection, but the general comfort evaluation method only considers the response of the mid-span position and does not consider the difference in the vibration response of different positions at the same time. It is crucial to study how pedestrians actually feel when they walk on footbridges. The computer vision-based vibration comfort evaluation method is a novel method with advantages, such as noncontact and long-distance. In this study, a computer vision-based method was used to evaluate the global vibration comfort of footbridges under human-induced excitation. The improved Lucas–Kanade optical flow method is used for multitarget displacement identification of footbridges. Additionally, the YOLOv5 algorithm for pedestrian detection is used to obtain the position information of pedestrians on the footbridges. Then, according to the pedestrian position information, the structural responses of different pedestrian positions corresponding to time periods are extracted from the displacement responses of each point, and they are combined to obtain the structural global displacement. The global acceleration can be obtained by calculating the global displacement. The rms value can be calculated based on the global acceleration and compared with the standard for comfort evaluation. The global comfort evaluation method is validated by pedestrian walking experiments with different frequencies on a laboratory footbridge. The experimental results show that the computer vision-based global comfort evaluation method for footbridges is feasible and is a more specific and real-time comfort evaluation method

Insect Trapping Method Based on Progressive Star Network

This paper proposes an insect trapping method based on progressive star network. There are two steps: Firstly, construct the network; secondly, trap insects through the network. The network is constructed as following descriptions: 1. Confirm the density of light node. 2. Adjust the height of light node versus growth of crops/plants. This paper contributes to relevant studies in that it proposes a new star network, in which the height of light node changes according to the growth of crops or plants, makes full use of biological resources and avoids bad consequences that traditional chemical pesticides bring about. The star network proposed is simple, reliable and successfully avoids redundant data resulted from frequency communicatio