Clustering Tendency Research Articles

High Occurrence Among Calves and Close Phylogenetic Relationships With Human Viruses Warrants Close Surveillance of Rotaviruses in Kuwaiti Dairy Farms.

Rotavirus, one of the main pathogens causing morbidity and mortality in neonatal dairy calves worldwide, is responsible for 30–44% of cattle deaths. It is considered to be the most common etiologic agent of diarrhea in neonatal dairy calves and children, the dominant type being group A. Two hundred seventy animals from 27 farms from 2 regions of Kuwait were tested for the presence of Rotavirus serogroup A (RVA) using latex agglutination test (LAT) and reverse transcription–polymerase chain (RT-PCR) testing. RVA non-structural proteins NSP1-2, NSP4-5 and capsid protein genes VP1-7 were characterized by next generation sequencing. LAT was positive in 15.56% of the animals, and RT-PCR in 28.89%. Using RT-PCR as a reference method, LAT was 100% specific but only 83.33% sensitive. ANOVA analysis showed correlation only with the location of the farms but no significant correlation with the age and sex of the animals. Although there was a tendency of clustering of RVA positive animals, it did not reach statistical significance (p = 0.035 for LAT). The phylogenetic analysis showed that Kuwaiti isolates of group A rotavirus clustered with human rotaviruses. Taken together, it seems that rotavirus was present in most of the dairy farms in Kuwait. The high occurrence of the virus in calves in Kuwaiti dairy farms and the close phylogenetic affinity with human isolates warrants urgent action to minimize and control its spread between calves in farms.

Style Recognition of Door God Paintings by Hypothesis Testing for Texture Features of Painting Patterns

Many studies in the literature have presented multiple remote sensing techniques for defect inspection of paintings. At present, however, papers on defect inspection and restoration of oriental architectural arts—such as door god paintings—are still rare. If an aged and damaged door god painting needs a restoration, then following the style and treatment skill of the original artist as much as possible is important for the restoration. Unfortunately, it is usually difficult to access the original artists for some of the aged door god paintings. This paper considers the texture features of auspicious patterns of armors on warrior door gods as useful information to recognize styles of door god paintings by unknown artists. First, a two-level two-dimensional discrete wavelet transform coupled with co-occurrence matrix calculation was adopted to analyze the texture features, based on the descriptors of angular second moment (ASM), entropy (ENT), contrast (CON), homogeneity (HOM), dissimilarity (DIS), correlation (COR), and cluster tendency (CLU), in the four orientations of 0° (horizontal), 45° (vertical), and 90° and 135° (double diagonal). Second, a two-tailed t-test based on the analyzed texture features was introduced into the hypothesis testing for demonstrating the master and apprentice relationships between the surveyed artists, and for recognizing the door god painting styles of unknown artists as well. The experimental results show that the proposed method effectively describes the texture features of the auspicious patterns of the surveyed door god paintings, and is able to determine the useful co-occurrence features for recognizing unknown artists’ painting styles.

A Study of Morbidity and Mortality from COVID-19 in India

The recent Human Coronavirus 2019 (hCoV-19) pandemic has devastated the whole world and impacted all aspects of human life. One of the most comprehensively recorded data for this outbreak is the daily morbidities and mortalities record. The analysis of this dataset would provide insight into the pattern and progression of this disease. The present study focused on the quantitative investigation and descriptive statistical examination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as part of a series of evaluations for this epidemic in the primarily affected geopolitical regions. The year 2021 is worse than 2020 in terms of the recorded daily newly emerging cases and deaths, and there are no signs that there would be an improvement in 2022, as could be estimated from early warning signs, even if there could be an apparent decline in the outbreak waves. India is one of the major countries that have been adversely affected by this global pandemic. The present study addressed this nation as a detailed record of COVID-19 cases and deaths extracted from a chronologically arranged dataset for the newly emerged cases and deaths on a daily basis. Cumulative counts were calculated and logarithmically transformed. Two significant peaks - embracing multiple waves - were observed with tailing for morbidity and mortality, which were highly correlated. There were no signs of a recession in the outbreak census. However, relative calm periods between waves might be detected. There were rising trends in morbidities and mortalities with a clustering tendency upon examination of the run charts. The Morgan-Morgan-Finney (MMF) model was found to demonstrate the best-fitting non-linear curve for the transformed cumulative database. Derivatization of the model equation demonstrated a factor that could be used in the assessment of the outbreak effect numerically to show influence on the impacted population. Doi: 10.28991/SciMedJ-2022-0401-03 Full Text: PDF

First-principles study of fission products Xe and Cs behaviors in U3Si2

In the past several decades, the U3Si2 has received much attention for the development of accident tolerant fuel in light water reactors because of its superior thermal conductivity and higher uranium density. In this study, density functional theory calculations have been carried out to study the occupation and diffusion behaviors of fission products Xe and Cs in U3Si2. It is revealed that the occupation sites of Xe and Cs depend on the chemical environment, and both of Xe and Cs are favorable to substitute for U or Si sites. The diffusions of Xe and Cs in U3Si2 are predicted to be via the vacancy mechanism and both of Xe and Cs form cluster easily. As compared with Cs, the Xe exhibits a smaller solubility, faster diffusion as well as stronger clustering tendency, which may cause larger bubble size for Xe than Cs under the same conditions in U3Si2. The differences in the diffusion behaviors between Xe and Cs mainly result from their different valence electronic configurations and different atomic radii.

SERS Liquid Biopsy Profiling of Serum for the Diagnosis of Kidney Cancer.

Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach.

A novel sampling-based visual topic models with computational intelligence for big social health data clustering.

Twitter is a popular social network for people to share views or opinions on various topics. Many people search for health topics through Twitter; thus, obtaining a vast amount of social health data from Twitter is possible. Topic models are widely used for social health-care data clustering. These models require prior knowledge about the clustering tendency. Determining the number of clusters of given social health data is known as the health cluster tendency. Visual techniques, including visual assessment of the cluster tendency, cosine-based, and multiviewpoint-based cosine similarity features VAT (MVCS-VAT), are used to identify social health cluster tendencies. The recent MVCS-VAT technique is superior to others; however, it is the most expensive technique for big social health data cluster assessment. Thus, this paper aims to enhance the work of the MVCS-VAT using a sampling technique to address the big social health data assessment problem. Experimental is conducted on different health datasets for demonstrating an efficiency of proposed work. Accuracy of social health data clustering is improved at a rate of 5 to 10% in the proposed S-MVCS-VAT when compared to MVCS-VAT. From obtained results, it also proved that the proposed S-MVCS-VAT is a faster and memory efficient for discovering social health data clusters.

Re-Estimation of Agricultural Production Efficiency in China under the Dual Constraints of Climate Change and Resource Environment: Spatial Imbalance and Convergence

Climate change and farmland environmental pollution have put greater pressure on the sustainability of agricultural production. Based on the provincial panel data of mainland China from 1978 to 2018, climate variables such as precipitation, temperature, and sunshine hours are included into the input indicators, and agricultural non-point source pollution and carbon emissions are taken as undesirable outputs, the agricultural production efficiency (APE) under the dual constraints of climate change and the resource environment was estimated by the super slacks-based measure (SBM)-undesirable model. On the basis of the trajectory of the imbalanced spatiotemporal evolution of APE shown by Kernel density estimation and the standard deviational ellipse (SDE)–center of gravity (COG) transfer model, the spatial convergence model was used to test the convergence and differentiation characteristics of APE. Under the dual constraints, APE presents a “bimodal” distribution with a stable increase in fluctuation, but it is still at a generally low level and does not show polarization, among which the APE in the northeast region is the highest. The COG of APE tends to transfer towards the northeast, and the coverage of the SDE is shrinking, so the overall spatial pattern is characterized by a tendency of clustering towards the north in the north-south direction and a tendency of imbalance in the east-west direction. APE has significant spatial convergence, and there is a trend of “latecomer catching-up” in low-efficiency regions. The introduction of spatial correlation accelerates the convergence rate and shortens the convergence period. The convergence rate is the highest in the central and western regions, followed by that in the northeastern region, and the convergence rate is the lowest in the eastern region. In addition, the convergence rate in different time periods has a phase change. The process of improving the quality and efficiency of agricultural production requires enhancing the adaptability of climate change, balancing the carrying capacity of the resource environment, and strengthening inter-regional cooperation and linkage in the field of agriculture.

Modelling inter-firm competitive rivalry in a port logistics cluster: a case study of Melbourne, Australia

PurposeThe paper models the effect of spatial clustering on various dimensions of inter-firm competitive rivalry among port logistics firms using Porter's five forces model.Design/methodology/approachA survey-based quantitative approach is adopted to collect data from logistics firms, which are directly or indirectly dependent on the Port of Melbourne in Australia. A structural equation modelling (SEM) technique is used to examine the theorised relationships between various dimensions of inter-firm competitive rivalry and the tendency of spatial clustering of logistics firms in the vicinity of Port of Melbourne.FindingsThe results indicate that the inter-firm competitive rivalry increases significantly when logistics firms are spatially clustered. This effect is further augmented when they cluster around the port. Co-location of firms near the port tends to increase “bargaining power of buyers”, whilst indirectly affecting “competitive rivalry” via “threats of substitutes”. This indicates that co-location enhances the bargaining power of buyers through the greater availability of substitute services that in turn promotes competitive rivalry among firms. However, co-location has an insignificant effect on “barriers to entry” and “bargaining power of suppliers”. Low entry barrier thus favours high competitive rivalry among firms. Hence, this paper validates the Porter's cluster and five forces models that confirm the positive effect of port logistics clusters (PLCs) on bargaining power of buyers and indirect effect on competitive rivalry partially mediated through threats of substitutes.Practical implicationsThis study provides empirically grounded evidence for firms to evaluate co-location decision choices and help buyers and sellers to devise business strategies to enhance inter-firm competitive rivalry and bargaining power.Originality/valueThis is the first systematic attempt to empirically validate Porter's five forces model in the context of PLC. Furthermore, the conceptualisation of PLC concept both as spatial and functional constructs (i.e. dependency on port) is novel. This study thus has broadened the meaning of cluster from a geographic entity to a more useful functional construct to reflect inter-firm dependencies.

Molecular Characterization of Sixty Local Mango Germplasm of Chapainawabganj

The experiment was conducted at Plant Genetic Resources Centre, Bangladesh Agricultural Research Institute (BARI) and the genotypes were collected from Chapainawabganj, the most mango variability rich district in Bangladesh. The molecular characters of mango germplasm were assessed by using six simple sequence repeat (SSR) markers. Polymerase chain reaction (PCR) amplification of the DNA isolated from 60 mango germplasm with 6 SSR primers was performed. The sizes of the alleles detected ranged from 112 to 221 bp. SSRs exhibited moderate values of polymorphic information content (PIC) range of 0.9405 to 0.6501. Genetic distances (D) between varieties were computed from combined data of the 6 primers, ranging from 0.5000 to 1.0000. Moderate degree of genetic diversity was obtained where the highest level of gene diversity value was noted 0.9433 in loci MIGA179 and the lowest level of gene diversity value was computed 0.6683 in loci MIGA253 with a mean diversity of 0.8842. The dendrogram generated from the unweighed pair group arithmetic average (UPGMA) cluster analysis broadly placed 60 mango cultivars into ten major clusters. The cluster size varied from 1 to 12 and cluster-VI was the largest cluster comprising of 9 cultivars. The tendency of clustering among mango cultivars revealed that they have strong affinity towards further breeding programme.

Cluster analysis of patients with granulomatosis with polyangiitis (GPA) based on clinical presentation symptoms: a UK population-based cohort study

BackgroundGranulomatosis with polyangiitis (GPA) is small vessel vasculitis with heterogeneous clinical presentation. In the present population-based cohort study, we classified patients with GPA based on clinical features at presentation using an unsupervised clustering approach and compared their mortality, infections and frequency of comorbidities.MethodsIn this open cohort study, de-identified primary care data of patients with GPA included in the IQVIA Medical Research Data database between 1 January 1995 and 25 September 2019 was analysed retrospectively. Latent class analysis was performed to create symptom clusters of patients based on 16 categories of symptoms representing various organ involvement. All-cause mortality of resultant clusters was compared after adjusting for age, sex, Townsend deprivation quintile and smoking status at index date using extended Cox proportional hazards models. Prescription of antibiotics, considered as an indirect indicator of recurrent bacterial infection, was compared using a recurrent event model, after adjusting for quarterly use of steroid as a time-dependent covariate. Cumulative frequencies of common comorbidities were compared among the clusters at index visit, 1-year and 3-year follow-up.ResultsAltogether, 649 patients with GPA [median age 60.0 (IQR: 49.6–70.1)] were included. Three clusters were identified: patients with limited disease mainly with involvement of ENT and cough were classified into cluster 1 (n = 426); cluster 2 had generalised non-renal disease (n = 176); while patients in cluster 3 had renal-predominant disease (n = 47). Many patients in cluster 1 developed generalised disease at the end of 1 year. Mortality in clusters 2 and 3 was higher compared with cluster 1. Mortality in cluster 1 itself was 68% higher than the general population without GPA. The duration of antibiotics prescription and frequency of coexisting medical illnesses was also higher in clusters 2 and 3.ConclusionsIn a primary care setting, patients with GPA can be classified into three distinct clusters with different prognosis, susceptibility to recurrent infections and presence of comorbidities. The tendency of cluster 1 to evolve into a more generalised disease raises questions about current immunosuppressive treatment approaches in these patients.

Relative geographic concentration of creative, other traded, and local industries using establishment data and Harvard's U.S. Cluster Mapping Benchmark Definitions

This paper examines the relative tendency of industries and industry clusters to be geographically concentrated. Creative industries defined as having distinct artistic creation and production-distribution components are examined. This extends previous observations that creative industries exhibit a relatively high degree of geographic concentration to examine whether two-sided market dynamics contribute to this concentration. Variance in the distribution of business establishments among U.S. metro areas for 978 industries is calculated using County Business Patterns data from the U.S. Census Bureau. The data is mapped to different clusters using Harvard University's U.S. Cluster Mapping Benchmark Definitions. The average variance of each cluster is calculated to measure relative concentration.•Richard Caves’ definition of creative industries is used to identify industries characterized by a two-sided structure.•Harvard University's U.S. Cluster Mapping Benchmark Definitions are used to map creative industries to specific industry codes and industry clusters.•These two methods are applied to U.S. County Business Patterns data to examine the relative geographic concentration of two-sided creative clusters.

The critical role of synthesis conditions on small polaron carrier concentrations in hematite—A first-principles study

Achieving highly efficient energy conversion with transition metal oxides necessitates overcoming conductivity limitations due to the formation of small polarons. Detailed understanding of the interplay among intrinsic defects, dopants, and electron polarons can help devise strategies for achieving higher carrier concentrations, therefore improving carrier conductivity. This work employs first-principles calculations to reliably predict electron polaron concentrations in a prominent polaronic oxide, hematite (Fe2O3), by resolving interactions between charged defects and electron polarons and keeping charge neutrality condition among all charged species. This work addresses that both VO and Fei can be primary donors in undoped hematite depending on the synthesis conditions, such as synthesis temperature and oxygen partial pressure, despite the fact that VO owns an extremely high ionization energy compared to kBT. Furthermore, from calculations of a plethora of n-type dopants (group IV and V elements), we find that Ti, Ge, Sb, and Nb are able to raise electron polaron concentrations in hematite significantly without considering dopant clustering. However, the magnitude of electron polaron concentration increase would be smaller if the dopant has a high tendency of clustering, such as Ti. We reveal the critical role of synthesis conditions on tuning electron polaron concentrations of both undoped and doped hematite. Our theoretical analysis provides important insights and general design principles for engineering more conductive polaronic oxides.

On Daphnia’s path: a semi‐automatic system to analyze three‐dimensional movement patterns

AbstractAnalyzing movement is essential for understanding complex behavioral interactions. Up to date, there are different movement analysis methods. Previous studies working with 2D movement analysis systems provided first insights into this field. 2D systems can capture only two of three spatial dimensions and thus allow analyses of movements on surfaces. 3D systems include all three spatial dimensions. Thus, 3D movement analysis is essential for analyzing movements in air or in water. Especially during the last years, 3D movement analysis has progressed a lot thanks to technological advances. Unfortunately, these technological advanced systems are often very expensive and handling flexibility is limited. Therefore, we implemented a customizable 3D movement analysis system for aquatic organisms. The system is composed of an experimental arena, backgrounds with different contrasts, three visible light diode spots, one infrared diode spot for analysis in the dark, and two Raspberry Pi 3 Model B V 1.2 equipped with cameras without infrared filters. We assembled a user‐friendly system that is controlled directly via a graphical user interface (GUI). It not only delivers raw data but also runs detailed analyses of common behavioral parameters. The accuracy of those analyses can be enhanced by the integrated manual error‐correction method that allows to interrupt the automatic track analysis and to correct detection errors by hand. In our case study, we show the spectrum of opportunities of the system by determining the swimming behavior of the freshwater crustacean Daphnia magna. We successfully recorded its swimming behavior under visible light and infrared light (simulating darkness for the daphniids) conditions and analyzed swimming velocity, motion patterns, depth selection, and tendency of clustering. In our case study, we show that using the integrated manual error‐correction method leads to a clear accuracy improvement. Our movement analysis system is not restricted to aquaria but can be adopted to movements in cages and even open areas of different sizes both in water or air, which emphasizes its potential. Together with a user manual, the open‐source MATLAB algorithm and the recommended hardware components, the here presented system enables semi‐automatic 3D movement analysis, which can be adapted to the individual needs.

Investigating the structure of star-forming regions using INDICATE

ABSTRACT The ability to make meaningful comparisons between theoretical and observational data of star-forming regions is key to understanding the star formation process. In this paper, we test the performance of INDICATE, a new method to quantify the clustering tendencies of individual stars in a region, on synthetic star-forming regions with substructured, and smooth, centrally concentrated distributions. INDICATE quantifies the amount of stellar affiliation of each individual star, and also determines whether this affiliation is above random expectation for the star-forming region in question. We show that INDICATE cannot be used to quantify the overall structure of a region due to a degeneracy when applied to regions with different geometries. We test the ability of INDICATE to detect differences in the local stellar surface density and its ability to detect and quantify mass segregation. We then compare it to other methods such as the mass segregation ratio ΛMSR, the local stellar surface density ratio ΣLDR, and the cumulative distribution of stellar positions. INDICATE detects significant differences in the clustering tendencies of the most massive stars when they are at the centre of a smooth, centrally concentrated distribution, corresponding to areas of greater stellar surface density. When applied to a subset of the 50 most massive stars, we show INDICATE can detect signals of mass segregation. We apply INDICATE to the following nearby star-forming regions: Taurus, ONC, NGC 1333, IC 348, and ρ Ophiuchi and find a diverse range of clustering tendencies in these regions.

Sheep's coping style can be identified by unsupervised machine learning from unlabeled data

The objective of this study was to define coping style of sheep by using unsupervised machine learning approaches. A total of 105 Norduz sheep (age 3–5 years) were subjected to a 5-minute arena test. Agglomerative Hierarchical Clustering (HCA) was performed on scores of selected principal components retained from Principal Components Analysis (PCA) on arena behaviors to identify sheep coping style. Initially, the variables retained for the PCA were determined with Bartlett’s test for sphericity and Kaiser-Meyer-Olkin (KMO) measure of sample adequacy. Seven behavioral variables with KMO values greater than 0.5 were used for final PCA: the average distance to group sheep (DTG), the average distance to stimulus (DTS), the duration of locomotion (LOC), the total number of zone boundaries crossed during the test (CRS), the total number of times that tested sheep sniffed stimulus (NSS), latency to the first sniff the stimulus (LSS), and subjective scores (SCR) scored by an observer on a scale from 1 to 5 (1: extremely calm, 5: extremely restless). The first two components, which were the only ones with an eigenvalue greater than one, accounted for 70.32% of the total variation and were used for clustering analysis. Clustering tendency showed that the scores for the first two components were suitable for clustering (Hopkins' H = 0.852). Several cluster validity indexes were used to obtain aggregated results to determine the most appropriate clustering method and number of clusters. Five different clustering methods: k-means and hierarchical clustering with Ward, average, single and complete linkage were compared. Bootstrap resampling was used to evaluate the stability of a given cluster using the Jaccard coefficient. The clustering method and number of clusters corresponding to the highest rank aggregation score from the bootstrap resampling indicate that the hierarchical clustering method with average linkage and 5 clusters is the most suggested clustering method. However, Ward's algorithm identified the strongest clustering structure for hierarchical clustering, as it had the highest agglomerative coefficient value (0.98). When both Jaccard and aggregation scores are considered together, Ward’s method with 3 clusters was selected as the most appropriate method. Sheep were classified into three coping styles (CS) based on HCA results as reactive (Cluster 1, n = 71), intermediate (Cluster 2, n = 22) or proactive (Cluster 3, n = 12). Coping style had significant effect on behavioral variables, DTG, DTS, LOC, CRS and NSS (P < 0.05). The individuals that have proactive coping style had the highest mean values for the variables DTG, DTS and LOC and SCR (P < 0.0001). This indicates that proactive sheep are more active then reactive sheep. The CRS, LOC and NSS mean values were higher for intermediate sheep compared to reactive sheep (P < 0.05). The NSS values were higher for intermediate sheep compare to proactive sheep (P < 0.0001). The findings of the current study show that distinct coping styles in sheep may be identified based on behaviors recorded in an arena test. The findings also revealed that sheep's coping style can be objectively identified by unsupervised machine learning from unlabeled behavioral data.

Analysis of the mandibular canal course using unsupervised machine learning algorithm.

ObjectivesAnatomical structure classification is necessary task in medical field, but the inevitable variability of interpretation among experts makes reliable classification difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for classification of three-dimensional (3D) mandibular canal (MC) courses, and to visualize standard MC courses derived from cluster analysis in the Korean population.Materials and methodsA total of 429 cone-beam computed tomography images were used. Four sites in the mandible were selected for the measurement of the MC course and four parameters, two vertical and two horizontal parameters were measured per site. Cluster analysis was carried out as follows: parameter measurement, parameter normalization, cluster tendency evaluation, optimal number of clusters determination, and k-means cluster analysis. The 3D MC courses were classified into three types with statistically significant mean differences by cluster analysis.ResultsCluster 1 showed a smooth line running towards the lingual side in the axial view and a steep slope in the sagittal view. Cluster 2 ran in an almost straight line closest to the lingual and inferior border of mandible. Cluster 3 showed the pathway with a bent buccally in the axial view and an increasing slope in the sagittal view in the posterior area. Cluster 2 showed the highest distribution (42.1%), and males were more widely distributed (57.1%) than the females (42.9%). Cluster 3 comprised similar ratio of male and female cases and accounted for 31.9% of the total distribution. Cluster 1 had the least distribution (26.0%) Distributions of the right and left sides did not show a statistically significant difference.ConclusionThe MC courses were automatically classified as three types through cluster analysis. Cluster analysis enables the unbiased classification of the anatomical structures by reducing observer variability and can present representative standard information for each classified group.

Epidemiological Characteristics of Inpatients Undergoing Surgery for Glaucoma at Tianjin Eye Hospital from 2013 to 2017.

Purpose To analyze the epidemiological characteristics of inpatients who underwent surgery for glaucoma at Tianjin Eye Hospital from 2013 to 2017. Methods All glaucoma inpatients who underwent surgery at Tianjin Eye Hospital from 2013 to 2017 were evaluated. The relationships of age and sex with different types of glaucoma were analyzed. The differences in the prevalence and family history of glaucoma among patients with different systemic diseases were compared. Additionally, the effects of different surgical methods for primary angle-closure glaucoma (PACG) and primary open-angle glaucoma (POAG) were compared. Results A total of 4539 patients with glaucoma were retrospectively analyzed. The most prevalent type was PACG (60.15%), followed by secondary glaucoma (SG, 25.53%), POAG (7.6%), uncontrollable intraocular pressure (IOP) after antiglaucoma surgery (4.71%), mixed glaucoma (MG, 10%), and congenital glaucoma (CG, 0.9%). The main surgical methods were phacoemulsification (phaco), phacotrabeculectomy (phaco-trab), and trabeculectomy (trab). The rate of phaco-trab increased, while that of trab decreased. The proportion of women in the PACG group was higher than those in the POAG and SG groups, and there was a pronounced tendency for family clustering (P < 0.001), while in the POAG and SG groups, the proportions of men and those with diabetes were higher (P < 0.05). Conclusions In Tianjin Eye Hospital from 2013 to 2017, the main type of glaucoma was PACG. Female sex and a family history of glaucoma were risk factors for PACG, while male sex and hyperglycemia were risk factors for POAG and SG. Among the antiglaucoma surgery methods, the proportion of phaco-trab increased, while the proportion of trab decreased.

Comparison of decadal water storage trends from common GRACE releases (RL05, RL06) using spatial diagnostics and a modified triple collocation approach

GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-On) satellites have provided unique insights into the evolution of Terrestrial Water Storage (TWS) in space and time. Despite such advancements, various GRACE solutions produced by different data centers display uneven spatial attributes with varying associated uncertainties. Via spatial diagnostics tools and a modified triple collocation (MTC) approach, this research evaluates the TWS (terrestrial water storage) trend estimations “on the grid-scale” from 11 gridded GRACE products of RL05 and RL06 releases between 2002 and 2017. Distinct from classic TCA (triple collocation analysis), the MTC employs a GWR (geographically weighted regression) scaling scheme with distinctive spatial coefficients. The spatial diagnostics analyses identified different autocorrelation patterns, clustering tendencies of hot (positive) and cold (negative) spots agglomeration at varying spatial width, and unique frequency distributions. The results indicated that within a 10-degree spatial radius the SHs (Spherical Harmonics) of RL05 and RL06 are highly autocorrelated compared to the mascons (mass concentration blocks) solutions. The spatial clustering results revealed that many solutions agreed on the overall directions and distribution of the hot and cold spots. The clustering among mascon products, however, reflected more localized mass anomalies. At the scale of drainage basins, the trend magnitude, as well as their associated uncertainties appeared to be driven by the occurrence of spatial clusters within the basin area. The MTC results showed that the uncertainty patterns follow the same spatial extent within each cluster. The MTC analysis underscored the added benefits of cluster analysis and the GWR scaling over the classic OLS approach.

Multi-viewpoints visual models for efficient modeling and analysis of Twitter based health-care services

PurposeThe purpose of this study for eHealth text mining domains, cosine-based visual methods (VM) assess the clusters more accurately than Euclidean; which are recommended for tweet data models for clusters assessment. Such VM determines the clusters concerning a single viewpoint or none, which are less informative. Multi-viewpoints (MVP) were used for addressing the more informative clusters assessment of health-care tweet documents and to demonstrate visual analysis of cluster tendency.Design/methodology/approachIn this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets. The authors demonstrated the effectiveness of proposed methods on different real-time Twitter health-care data sets in the experimental study. The authors also did a comparative analysis of proposed models with existing visual assessment tendency (VAT) and cVAT models by using cluster validity indices and computational complexities; the examples suggest that MVP VM were more informative.FindingsIn this paper, the authors proposed MVP-based VM by using traditional topic models with visual techniques to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets.Originality/valueIn this paper, the authors proposed multi-viewpoints distance metric in topic model cluster tendency for the first time and visual representation using VAT images using hybrid topic models to find cluster tendency, partitioning for cluster validity to propose health-care recommendations based on tweets.

Prediction of Potential Mutation of Chicken Coronavirus into Future Human Coronavirus Based on Spike S1 Glycoprotein Gene

The high mutation rates of the chicken coronavirus (IBV) cause economic threats to the poultry industry. However, the most dangerous situation is the likelihood of changing its sequences into human coronavirus (COVID-19-like virus). Therefore, in the present study we aimed to investigate the possibility of genetic mutation of IBV to COVID-19. Thus, the sequences of Spike (S1) Glycoprotein genes of both IBV and COVID-19 were aligned, analyzed and calculated to predict the possible changes that could happen in the sequences of S1. The results indicated that in the case of an independent function of probability of each cluster of S1 sequences, the potential mutation rate in the sequences of IBV to be as COVID-19 was equal to 1.87E-96. However, because the tendency for some sequence clusters of S1 gene was low or equal to zero, it is unattainable to mutate the chicken IBV into COVID-19 sequence. Furthermore, in case of the dependent function, the probability of assumed annual mutation to make IBV infectious for human may reach up to around 50% after about 260 years. As a conclusion, the mutating of chicken coronavirus into COVID-19-like virus is not impossible, but it might take a substantial period of time