A Community Detection Algorithm Based on Topology Potential and Spectral Clustering

Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes’ topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods

Accurate Road Marking Detection from Noisy Point Clouds Acquired by Low-Cost Mobile LiDAR Systems

Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields of view for multi-beam laser scanners; however, the intensity information scanned by multi-beam systems is noisy and current methods designed for road marking detection from mono-beam point clouds are of low accuracy. This paper presents an accurate algorithm for detecting road markings from noisy point clouds, where most nonroad points are removed and the remaining points are organized into a set of consecutive pseudo-scan lines for parallel and/or online processing. The road surface is precisely extracted by a moving fitting window filter from each pseudo-scan line, and a marker edge detector combining an intensity gradient with an intensity statistics histogram is presented for road marking detection. Quantitative results indicate that the proposed method achieves average recall, precision, and Matthews correlation coefficient (MCC) levels of 90%, 95%, and 92%, respectively, showing excellent performance for road marking detection from multi-beam scanning point clouds

A Study on the Adsorption Characteristics of Thiourea by Typical Minerals from the Bio-Oxidation Residue of Gold Ore

In order to improve the thiourea gold leaching rate of a low-grade arsenic–sulfur-containing refractory gold ore in Xinjiang, a microbial pretreatment was used to oxidize pyrite and arsenopyrite to obtain a bio-oxidation residue. The main minerals were quartz, mica, and some sulfides that were not fully oxidized. In this study, the static adsorption method was applied to simulate the thiourea adsorption by typical minerals. The results showed that the amount of thiourea adsorbed by the three minerals could be ordered as follows: pyrite > mica > quartz. Quartz had hardly any adsorption of thiourea. The thiourea adsorption capacities of pyrite and mica were about 8.93 mg g−1 and 2.30 mg g−1, respectively. The adsorption process for pyrite conformed to the Freundlich isotherm equation and pseudo-second-order kinetic model, indicating that the adsorption process was a monolayer chemisorption. The adsorption process for mica conformed to the Langmuir isotherm equation and pseudo-second-order kinetic model, indicating that the adsorption process was a monolayer physical adsorption. Fourier transform infrared spectroscopy showed that the adsorption of thiourea on the surface of mica relied on the formation of hydrogen bonds with Si-OH, whereas a new S-S peak was detected on the surface of pyrite, which further indicated that thiourea was chemically adsorbed on the surface of pyrite

DataSheet_2_Autoimmune thyroid disease and myasthenia gravis: a study bidirectional Mendelian randomization.pdf

BackgroundPrevious studies have suggested a potential association between AITD and MG, but the evidence is limited and controversial, and the exact causal relationship remains uncertain.ObjectiveTherefore, we employed a Mendelian randomization (MR) analysis to investigate the causal relationship between AITD and MG.MethodsTo explore the interplay between AITD and MG, We conducted MR studies utilizing GWAS-based summary statistics in the European ancestry. Several techniques were used to ensure the stability of the causal effect, such as random-effect inverse variance weighted, weighted median, MR-Egger regression, and MR-PRESSO. Heterogeneity was evaluated by calculating Cochran’s Q value. Moreover, the presence of horizontal pleiotropy was investigated through MR-Egger regression and MR-PRESSOResultsThe IVW method indicates a causal relationship between both GD(OR 1.31,95%CI 1.08 to 1.60,P=0.005) and autoimmune hypothyroidism (OR: 1.26, 95% CI: 1.08 to 1.47, P =0.002) with MG. However, there is no association found between FT4(OR 0.88,95%CI 0.65 to 1.18,P=0.406), TPOAb(OR: 1.34, 95% CI: 0.86 to 2.07, P =0.186), TSH(OR: 0.97, 95% CI: 0.77 to 1.23, P =0.846), and MG. The reverse MR analysis reveals a causal relationship between MG and GD(OR: 1.50, 95% CI: 1.14 to 1.98, P =3.57e-3), with stable results. On the other hand, there is a significant association with autoimmune hypothyroidism(OR: 1.29, 95% CI: 1.04 to 1.59, P =0.019), but it is considered unstable due to the influence of horizontal pleiotropy (MR PRESSO Distortion Test P ConclusionAITD patients are more susceptible to developing MG, and MG patients also have a higher incidence of GD.</p

DataSheet_1_Autoimmune thyroid disease and myasthenia gravis: a study bidirectional Mendelian randomization.pdf

BackgroundPrevious studies have suggested a potential association between AITD and MG, but the evidence is limited and controversial, and the exact causal relationship remains uncertain.ObjectiveTherefore, we employed a Mendelian randomization (MR) analysis to investigate the causal relationship between AITD and MG.MethodsTo explore the interplay between AITD and MG, We conducted MR studies utilizing GWAS-based summary statistics in the European ancestry. Several techniques were used to ensure the stability of the causal effect, such as random-effect inverse variance weighted, weighted median, MR-Egger regression, and MR-PRESSO. Heterogeneity was evaluated by calculating Cochran’s Q value. Moreover, the presence of horizontal pleiotropy was investigated through MR-Egger regression and MR-PRESSOResultsThe IVW method indicates a causal relationship between both GD(OR 1.31,95%CI 1.08 to 1.60,P=0.005) and autoimmune hypothyroidism (OR: 1.26, 95% CI: 1.08 to 1.47, P =0.002) with MG. However, there is no association found between FT4(OR 0.88,95%CI 0.65 to 1.18,P=0.406), TPOAb(OR: 1.34, 95% CI: 0.86 to 2.07, P =0.186), TSH(OR: 0.97, 95% CI: 0.77 to 1.23, P =0.846), and MG. The reverse MR analysis reveals a causal relationship between MG and GD(OR: 1.50, 95% CI: 1.14 to 1.98, P =3.57e-3), with stable results. On the other hand, there is a significant association with autoimmune hypothyroidism(OR: 1.29, 95% CI: 1.04 to 1.59, P =0.019), but it is considered unstable due to the influence of horizontal pleiotropy (MR PRESSO Distortion Test P ConclusionAITD patients are more susceptible to developing MG, and MG patients also have a higher incidence of GD.</p

Cardiometabolic risk profiles associated with chronic complications in overweight and obese type 2 diabetes patients in South China.

BACKGROUND: Type 2 diabetes is often accompanied by altered cardiometabolic risk profiles, including abdominal obesity, hypertension, and dyslipidaemia. The association of altered cardiometabolic risk profiles with chronic complications of diabetes is not well investigated. METHODS: We recruited 2954 type 2 diabetes patients with a body mass index ≥25 kg/m2 who visited the diabetes clinics of 62 hospitals in 21 cities in Guangdong province of China from August 2011 to March 2012. Demographic characteristics, personal and family medical histories, and data on chronic complications of diabetes were collected. Clinical examinations and laboratory assessment were conducted. RESULTS: Abdominal obesity was found in 91.6% of the study population, elevated blood pressure in 78.3%; elevated serum triacylglycerols in 57.8%, and reduced serum HDL-C in 55.9%. Among the cardiometabolic risk factors, elevated blood pressure was significantly associated with almost all the chronic complications of diabetes. After adjusting for age, gender, duration of diabetes, and HbA1c, elevated blood pressure was significantly associated with diabetic retinopathy (OR 1.63, 95% CI: 1.22-2.19), diabetic nephropathy (OR 3.16, 95% CI: 2.25-4.46), cardiovascular disease (OR 2.71, 95% CI: 1.70-4.32), and stroke (OR 1.90, 95% CI: 1.15-3.12). Abdominal adiposity was significantly associated with diabetic nephropathy (OR 1.39, 95% CI: 1.11-1.74). Elevated triacylglycerols was significantly associated with diabetic retinopathy (OR 1.29, 95% CI: 1.05-1.58) and diabetic nephropathy (OR 1.30, 95% CI: 1.05-1.58). Reduced HDL-C was significantly associated with stroke (OR 1.41, 95% CI: 1.05-1.88). CONCLUSIONS: Altered cardiometabolic risk profiles, and elevated blood pressure in particular, were significantly associated with chronic complications in overweight and obese patients with type 2 diabetes. Future studies on the prevention of chronic complications of diabetes might make lowering blood pressure a primary target

Prevalence of diabetic chronic complications according to the number of altered cardiometabolic risk factors.

<p>1, type 2 diabetes plus any other one component; 2, type 2 diabetes plus any other two components; 3, type 2 diabetes plus any other three components; 4, type 2 diabetes plus any other four components; DR, diabetic retinopathy; DN, diabetic nephropathy; DPN, diabetic peripheral neuropathy; CVD, cardiovascular disease.</p

Prevalence of diabetic chronic complications.

<p>Prevalence of diabetic chronic complications.</p

Characteristics of participants.

<p>Characteristics of participants.</p