Rand Index Research Articles

Adjusting the adjusted Rand Index

Adjusting the adjusted Rand Index

Evaluation of Fourier Transform Infrared Spectroscopy as a First-Line Typing Tool for the Identification of Extended-Spectrum β-Lactamase-Producing Klebsiella pneumoniae Outbreaks in the Hospital Setting.

Early detection of pathogen cross-transmission events and environmental reservoirs is needed to control derived nosocomial outbreaks. Whole-genome sequencing (WGS) is considered the gold standard for outbreak confirmation, but, in most cases, it is time-consuming and has elevated costs. Consequently, the timely incorporation of WGS results to conventional epidemiology (CE) investigations for rapid outbreak detection is scarce. Fourier transform infrared spectroscopy (FTIR) is a rapid technique that establishes similarity among bacteria based on the comparison of infrared light absorption patterns of bacterial polysaccharides and has been used as a typing tool in recent studies. The aim of the present study was to evaluate the performance of the FTIR as a first-line typing tool for the identification of extended-spectrum β-lactamase-producing Klebsiella pneumoniae (ESBL-Kp) outbreaks in the hospital setting in comparison with CE investigations using WGS as the gold standard method. Sixty-three isolates of ESBL-Kp collected from 2018 to 2021 and classified according to CE were typed by both FTIR and WGS. Concordance was measured using the Adjusted Rand index (AR) and the Adjusted Wallace coefficient (AW) for both CE and FTIR clustering considering WGS as the reference method. Both AR and AW were significantly higher for FTIR clustering than CE clustering (0.475 vs. 0.134, p = 0.01, and 0.521 vs. 0.134, p = 0.009, respectively). Accordingly, FTIR inferred more true clustering relationships than CE (38/42 vs. 24/42, p = 0.001). However, a similar proportion of genomic singletons was detected by both FTIR and CE (13/21 vs. 12/21, p = 1). This study demonstrates the utility of the FTIR method as a quick, low-cost, first-line tool for the detection of ESBL-Kp outbreaks, while WGS analyses are being performed for outbreak confirmation and isolate characterization. Thus, clinical microbiology laboratories would benefit from integrating the FTIR method into CE investigations for infection control measures in the hospital setting.

The effect of disease co-occurrence measurement on multimorbidity networks: a population-based study.

BackgroundNetwork analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic health conditions. The effect that co-occurrence measurement has on disease network structure and resulting inferences has not been well studied. The purpose of the study was to compare structural differences among multimorbidity networks constructed using different co-occurrence measures.MethodsA retrospective cohort study was conducted using four fiscal years of administrative health data (2015/16 – 2018/19) from the province of Manitoba, Canada (population 1.5 million). Chronic conditions were identified using diagnosis codes from electronic records of physician visits, surgeries, and inpatient hospitalizations, and grouped into categories using the Johns Hopkins Adjusted Clinical Group (ACG) System. Pairwise disease networks were separately constructed using each of seven co-occurrence measures: lift, relative risk, phi, Jaccard, cosine, Kulczynski, and joint prevalence. Centrality analysis was limited to the top 20 central nodes, with degree centrality used to identify potentially influential chronic conditions. Community detection was used to identify disease clusters. Similarities in community structure between networks was measured using the adjusted Rand index (ARI). Network edges were described using disease prevalence categorized as low (< 1%), moderate (1 to < 7%), and high (≥7%). Network complexity was measured using network density and frequencies of nodes and edges.ResultsRelative risk and lift highlighted co-occurrences between pairs of low prevalence health conditions. Kulczynski emphasized relationships between high and low prevalence conditions. Joint prevalence focused on highly-prevalent conditions. Phi, Jaccard, and cosine emphasized associations involving moderately prevalent conditions. Co-occurrence measurement differences significantly affected the number and structure of identified disease clusters. When limiting the number of edges to produce visually interpretable graphs, networks had significant dissimilarity in the percentage of co-occurrence relationships in common, and in their selection of the highest-degree nodes.ConclusionsMultimorbidity network analyses are sensitive to disease co-occurrence measurement. Co-occurrence measures should be selected considering their intrinsic properties, research objectives, and the health condition prevalence relationships of greatest interest. Researchers should consider conducting sensitivity analyses using different co-occurrence measures.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12874-022-01607-8.

An evidence accumulation based block diagonal cluster model for intent recognition from EEG

Most of the probabilistic mixture models perform clustering by observing the eigenvectors of the data sample and these models rely on the layout of features. Clustering ensemble based on similarity matrices avoids complex processing of samples by only accessing basic clusters. However, while there are many literatures on the probability mixture model for clustering, there is almost no study focusing on applying the similarity matrix to the probability mixture model. Therefore, a new clustering method called block clustering structure of evidence accumulation matrix (BEAM) is proposed in this study by combining the clustering ensemble and the probability mixture model. Specifically, evidence accumulation (EA) is developed to obtain a similarity matrix of samples. The interpretability of the similarity matrix can be improved due to sample-based similarity measures, and then the diagonal block model is designed to identify representative block cluster structures from the similarity matrix. The proposed method has been evaluated on the BCI Competition IV Data set 1 and the block-diagonal structure of the similarity matrix is discovered, which ensures high similarity within the same cluster and large separation between the clusters. In addition, the Davies-Bouldin index (DBI) and adjusted rand index (ARI) are used to evaluate BEAM performance. The results show that the proposed method is superior to the state-of-the-art approaches.

Identifying DNA methylation signatures in high-grade serous ovarian cancer: Results vary by control tissue type.

e17559 Background: High grade serous ovarian cancer (HGSC) is the most common and fatal epithelial ovarian cancer and is often diagnosed in late stages. DNA methylation has emerged as a potential biomarker for the early detection of cancer, including ovarian cancer. Studies examining DNA methylation in ovarian tumor tissue have used adjacent non-tumor tissues or tissues from unaffected women as the control; however, few have used paired tissue and research is sparse on how results may vary by the control non-tumor tissue type. Therefore, we examined DNA methylation signatures in HGSC tumor tissue using different control non-tumor tissue types. Methods: We examined DNA methylation signatures using the Illumina Infinium MethylationEPIC BeadChip (850k) in formalin-fixed paraffin-embedded tissue. In women with HGSC, we compared DNA methylation patterns between paired adjacent non-tumor ovary (n = 74) and fallopian tube (n = 80) tissues. We compared DNA methylation patterns in these adjacent non-tumor tissues with non-tumor ovary (n = 8) and fallopian tube (n = 8) tissues from unaffected women without ovarian cancer carrying a pathogenic variant in BRCA1/2 ( BRCA1/2+). Lastly, we compared the overlap in differentially methylated CpGs identified in HGSC tumor tissue compared to paired adjacent non-tumor ovary (n = 50) and fallopian tube (n = 52) tissues. We processed the methylation data and used principal components analysis (PCA) and the adjusted Rand Index to compare the non-tumor tissue type methylation patterns. We used a paired t-test between individual-matched tumor and adjacent non-tumor tissue for each CpG site and then applied Bonferroni’s method to adjust the obtained p-values. Results: PCA comparing methylation patterns between paired adjacent non-tumor ovary and fallopian tube tissues from women with HGSC showed an adjusted Rand Index of 0.78, revealing separate clusters that cannot be combined. PCA comparing methylation patterns from the adjacent non-tumor tissues from women with HGSC and the non-tumor tissues from unaffected BRCA1/2+ women showed an adjusted Rand Index of -0.10 and 0.07 for the ovary and fallopian tube tissues, respectively, revealing overlapping clusters that can be combined. Lastly, comparison of the top differentially methylated CpGs identified in HGSC tumor tissue when using paired adjacent non-tumor ovary versus fallopian tube tissues as the control showed minimal overlap (6.8% and 4.0% for hypermethylated and hypomethylated CpGs, respectively). Conclusions: These results suggest that paired adjacent non-tumor ovary and fallopian tube tissues from women with HGSC have different DNA methylation patterns and result in different methylation signatures identified in HGSC tumor tissue. When comparing results across studies, including for validation, the type of non-tumor tissue control must be considered.

DP-k-modes: A self-tuning k-modes clustering algorithm

• Self-tuning cutoff distance is proposed to extend DPC. • Optimal initial seeds and number of clusters determined simultaneously. • New dissimilarity measure is proposed calculating dissimilarities between objects. • Self-tuning k-modes clustering algorithm , referred to as DP-k-modes, is proposed. • DP-k-modes is superior to the compared ones and is significant different to them. The k -modes clustering algorithm was proposed by Huang for handling datasets with categorical attributes, however, the dissimilarity measure used limits its applicability. Ng et al. improved on Huang’s k -modes algorithm by proposing a new dissimilarity measure between objects. Moreover, both k -modes algorithms require the initial seeds to be randomly chosen and the number of clusters be specified manually. To overcome the limitations of Huang’s and Ng’s k -modes clustering algorithms, we first extend the clustering algorithm published in Science in 2014 (“clustering by fast search and find of density peaks”). The optimal initial seeds and the number of clusters of a dataset are determined simultaneously by taking the standard deviation as the self-tuning cutoff distance and the simple match dissimilarity as the distance measurement in the definition of the density of a point. A new dissimilarity measure is proposed to calculate the dissimilarities between objects to improve on that of Ng’s k -modes algorithm. The performance of our resulting self-tuning k -modes clustering algorithm was tested on nine datasets (three being relatively large) from the UCI (University of California in Irvine) machine learning repository. The clustering results were compared to those produced by Huang’s and Ng’s algorithms. Statistical tests of three k -modes algorithms were undertaken to determine whether or not there is significant difference between our self-tuning k -modes algorithm and Huang’s and Ng’s k -modes algorithms. All these experimental results demonstrate that our proposed k -modes clustering algorithm is superior to Hang’s and Ng’s k -modes algorithms in terms of clustering accuracy ( ACC ) and the well-known Adjusted Rand Index ( ARI ) metric. Our self-tuning k -modes algorithm is significantly different from both Huang’s and Ng’s k -modes algorithms, and there is no statistically significant difference between Ng’s and Huang’s k -modes algorithms.

Novel Enhanced Unsupervised Quantum Clustering Algorithm

Every day, humans generate approximately 2.5 billion gigabytes of new data. While this data is crucial in applications such as drug development and cybersecurity, it is mostly unstructured and unlabeled which makes it difficult to process effectively. Businesses and governments alike solve this problem by using machine learning algorithms to characterize the data and make decisions with it. However, conventional machine learning processes consume excessive computational resources and time: one of the most prominent, K-means clustering, has an approximate time complexity of O(n^2), where n is the input data size. This quadratic time complexity causes scalability problems with large datasets. In this project, I have created an improved, more scalable clustering algorithm by leveraging quantum computation. The approach (new Q-means) involves an Angle Embedding Feature Map, an d-dimensional SWAP Test, and a dynamic, threshold distance-based termination criteria. After implementing this algorithm using the IBM Qiskit SDK, the new Q-means algorithm was run several times on different dimensional datasets. On an average, the new Q-means performed 18% better than the K-means algorithm based on the Adjusted Rand Index. In terms of the simulation execution time, the new Q-means was about six times faster than the existing Q-means due to a multi-threaded implementation and faster convergence. The time complexity of the new Q-means without a Quantum Random Access Memory (QRAM) is O(n^2). While the existing Q-means algorithm, which utilizes QRAM, has a time complexity of O(n(log n)^p), the time complexity of the new Q-means with QRAM is significantly better—O(n).

Chimp optimization algorithm in multilevel image thresholding and image clustering

Multilevel image thresholding and image clustering, two extensively used image processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach of yielding multiple threshold values for each color channel to generate clustered and segmented images appears to be quite efficient and it provides significant performance, although this method is computationally heavy. To ease this complicated process, nature inspired optimization algorithms are quite handy tools. In this paper, the performance of Chimp Optimization Algorithm (ChOA) in image clustering and segmentation has been analyzed, based on multilevel thresholding for each color channel. To evaluate the performance of ChOA in this regard, several performance metrics have been used, namely, Segment evolution function, peak signal-to-noise ratio, Variation of information, Probability Rand Index, global consistency error, Feature Similarity Index and Structural Similarity Index, Blind/Referenceless Image Spatial Quality Evaluatoe, Perception based Image Quality Evaluator, Naturalness Image Quality Evaluator. This performance has been compared with eight other well known metaheuristic algorithms: Particle Swarm Optimization Algorithm, Whale Optimization Algorithm, Salp Swarm Algorithm, Harris Hawks Optimization Algorithm, Moth Flame Optimization Algorithm, Grey Wolf Optimization Algorithm, Archimedes Optimization Algorithm, African Vulture Optimization Algorithm using two popular thresholding techniques-Kapur’s entropy method and Otsu’s class variance method. The results demonstrate the effectiveness and competitive performance of Chimp Optimization Algorithm.

CDSImpute: An ensemble similarity imputation method for single-cell RNA sequence dropouts

BackgroundSingle-cell RNA-sequencing enables the opportunity to investigate cell heterogeneity, discover new types of cells and to perform transcriptomic reconstruction at a single-cell resolution. Due to technical inadequacy, the presence of dropout events hinders the downstream and differential expression analysis. Therefore, it demands an efficient and accurate approach to recover the true gene expression. To fill the gap, we present a novel Single-cell RNA dropout imputation method to retrieve the original gene expression of the genes with excessive zero and near-zero counts. ResultHere we have developed CDSImpute (Correlation Distance Similarity Imputation) to identify dropouts induced in scRNA-seq data rather than biological zeros and recover true gene expression. By taking into consideration correlation and negative distance between cells, a similar cell list has been created and by borrowing the gene expression from similar cells dropout has been detected and corrected simultaneously.The improvement is consistent with simulation data and several publicly available scRNA-seq datasets. The clustering accuracy of CDSImpute is evaluated by adjusted rand index on Kolod, Pollen and Usoskin datasets are 1.00, 0.79 and 0.34 respectively. CDSImpute achieves improved performance compared to the three existing methods evaluated by precise cell-type identification and differentially expressed gene detection from scRNA-seq Data. ConclusionCDSImpute is a novel effective method to impute the dropout events of a scRNA-seq expression matrix. The package is implemented in the R language and is available at https://github.com/riasatazim/CDSImpute.

A hierarchical clustering approach for examining potential risk factors for bone stress injury in runners

Bone stress injuries (BSI) are overuse injuries that commonly occur in runners. BSI risk is multifactorial and not well understood. Unsupervised machine learning approaches can potentially elucidate risk factors for BSI by looking for groups of similar runners within a population that differ in BSI incidence. Here, a hierarchical clustering approach is used to identify groups of collegiate cross country runners (32 females, 21 males) based on healthy pre-season running (4.47 m·s−1) gait data which were aggregated and dimensionally reduced by principal component analysis. Five distinct groups were identified using the cluster tree. Visual inspection revealed clear differences between groups in kinematics and kinetics, and linear mixed effects models showed between-group differences in metrics potentially related to BSI risk. The groups also differed in BSI incidence during the subsequent academic year (Rand index = 0.49; adjusted Rand index = −0.02). Groups ranged from those including runners spending less time contacting the ground and generating higher peak ground reaction forces and joint moments to those including runners spending more time on the ground with lower loads. The former groups showed higher BSI incidence, indicating that short stance phases and high peak loads may be risk factors for BSI. Since ground contact duration may itself account for differences in peak loading metrics, we hypothesize that the percentage of time a runner is in contact with the ground may be a useful metric to include in machine learning models for predicting BSI risk.

Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering.

The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent groupings of textual content documents in the last decade. However, it is challenging to discover informative representations and define relevant articles from the rapidly growing biomedical literature due to the unsupervised nature of document clustering. Moreover, empirical investigations demonstrated that traditional text clustering methods produce unsatisfactory results in terms of non-contextualized vector space representations because that neglect the semantic relationship between biomedical texts. Recently, pre-trained language models have emerged as successful in a wide range of natural language processing applications. In this paper, we propose the Gaussian Mixture Model-based efficient clustering framework that incorporates substantially pre-trained (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) BioBERT domain-specific language representations to enhance the clustering accuracy. Our proposed framework consists of main three phases. First, classic text pre-processing techniques are used biomedical document data, which crawled from the PubMed repository. Second, representative vectors are extracted from a pre-trained BioBERT language model for biomedical text mining. Third, we employ the Gaussian Mixture Model as a clustering algorithm, which allows us to assign labels for each biomedical document. In order to prove the efficiency of our proposed model, we conducted a comprehensive experimental analysis utilizing several clustering algorithms while combining diverse embedding techniques. Consequently, the experimental results show that the proposed model outperforms the benchmark models by reaching performance measures of Fowlkes mallows score, silhouette coefficient, adjusted rand index, Davies-Bouldin score of 0.7817, 0.3765, 0.4478, 1.6849, respectively. We expect the outcomes of this study will assist domain specialists in comprehending thematically cohesive documents in the healthcare field.

Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations

Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer timescales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in Northern Hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations, respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the adjusted Rand index – a method for comparing spatial patterns of variability – and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability in the AO relatively well, although they overestimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean and underestimate the variability over northern Africa and southern Europe. They also underestimate the importance of regions such as the Beaufort, East Siberian, and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.

Neurobiologically Based Stratification of Recent-Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes

BackgroundIdentifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. MethodsHYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). ResultsThe optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. ConclusionsWe identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Semantics of European poetry is shaped by conservative forces: The relationship between poetic meter and meaning in accentual-syllabic verse.

Recent advances in cultural analytics and large-scale computational studies of art, literature and film often show that long-term change in the features of artistic works happens gradually. These findings suggest that conservative forces that shape creative domains might be underestimated. To this end, we provide the first large-scale formal evidence of the association between poetic meter and semantics in 18-19th century European literatures, using Czech, German and Russian collections with additional data from English poetry and early modern Dutch songs. Our study traces this association through a series of unsupervised classifications using the abstracted semantic features of poems that are inferred for individual texts with the aid of topic modeling. Topics alone enable recognition of the meters in each observed language, as may be seen from the same-meter samples clustering together (median Adjusted Rand Index between 0.48 and 1 across traditions). In addition, this study shows that the strength of the association between form and meaning tends to decrease over time. This may reflect a shift in aesthetic conventions between the 18th and 19th centuries as individual innovation was increasingly favored in literature. Despite this decline, it remains possible to recognize semantics of the meters from past or future, which suggests the continuity in meter-meaning relationships while also revealing the historical variability of conditions across languages. This paper argues that distinct metrical forms, which are often copied in a language over centuries, also maintain long-term semantic inertia in poetry. Our findings highlight the role of the formal features of cultural items in influencing the pace and shape of cultural evolution.

Unsupervised learning monitors the carbon-dioxide plume in the subsurface carbon storage reservoir

Subsurface sequestration of carbon dioxide (CO2) requires long-term monitoring of the injected CO2 plume to prevent CO2 leakage along the wellbore or across the caprock. Accurate knowledge of the location and movement of the injected CO2 is crucial for risk management at a geological CO2-storage complex. Conventional methods for locating/assessing the injected CO2 plume in the subsurface assume a geophysical model, which is specific and may not be applicable to all types of CO2-injection reservoirs and scenarios. We developed an unsupervised-learning-based visualization of the subsurface CO2 plume that adapts and scales based on the data without requiring an assumption of the geophysical model. The data-processing workflow was applied to the cross-well seismic tomography data from the SECARB Cranfield carbon geo-sequestration project. A multi-level clustering approach was developed to account for data imbalance due to the absence of CO2 in the large portion of the imaged reservoir. The first level of clustering differentiated CO2-bearing regions from the non-CO2 bearing regions and achieved a silhouette score of 0.88, a Calinski-Harabasz index of 271145, and a Davies-Bouldin index of 0.30, which are indicative of high quality, reliable clustering. The second level of clustering further differentiated the CO2-bearing regions into regions containing low, medium–low, medium–high, and high CO2 content. Overall, the multi-level clustering achieved a silhouette score, Calinski-Harabasz index, and Davies-Bouldin index of 0.68, 86750, and 0.46, which confirm the high quality and reliability of the newly proposed unsupervised-learning-based visualization. Three distinct clustering techniques, namely k-means, mean-shift, and agglomerative, generated similar visualizations. In terms of the adjusted Rand index, the similarity of clusters identified by the three distinct clustering techniques is around 0.98, which indicates the robustness of the cluster labels assigned to various regions of the geological carbon-storage reservoir. Further, we find certain geophysical signatures, such as Fourier transform and wavelet transform, represent the distribution of CO2 content in subsurface.

Surveillance of Enterobacter cloacae complex colonization and comparative analysis of different typing methods on a neonatal intensive care unit in Germany

BackgroundEnterobacter cloacae complex is a group of common opportunistic pathogens on neonatal intensive care units. Active microbiological screening to guide empirical antimicrobial treatment or to detect transmission events is recommended in high-risk preterm neonates. A rise in colonization with E. cloacae complex was observed in a German perinatal centre. The aim of this study was to evaluate the performance of different typing techniques using whole genome sequencing (WGS) as a reference.MethodsEnterobacter cloacae complex isolates from clinical and screening specimens with an epidemiological link to the neonatal intensive care units were further assessed. Identification and antibiotic susceptibility testing was performed by a combination of VITEK2 (bioMérieux) and MALDI-TOF (Bruker Daltonics), followed by RAPD/rep-PCR and PFGE (XbaI). Retrospectively, all isolates were analyzed by Fourier-transform infrared (FTIR) spectroscopy (IR Biotyper, Bruker Daltonics). Whole genome sequencing with SNP-based clustering was used as the reference method. Furthermore, resistome analysis, sequence type and species identification were derived from the WGS data. Transmission analysis was based on epidemiological and typing data.ResultsBetween September 2017 and March 2018 32 mostly preterm neonates were found to be colonized with E. cloacae complex and 32 isolates from 24 patients were available for further typing. RAPD/rep-PCR and PFGE showed good concordance with WGS whereas FTIR displayed mediocre results [adjusted rand index (ARI) = 0.436]. A polyclonal increase and two dominant and overlapping clonal clusters of two different E. hormaechei subspecies were detected. Overall, four different species were identified. Genotyping confirmed third-generation cephalosporin resistance development in isolates of the same patient. During the six-month period several infection prevention interventions were performed and no E. cloacae complex isolates were observed during the following months.ConclusionsInterpretation of the microbiological results alone to detect transmission events is often challenging and bacterial typing is of utmost importance to implement targeted infection control measures in an epidemic occurrence of E. cloacae complex. WGS is the most discriminatory method. However, traditional methods such as PFGE or RAPD/rep-PCR can provide reliable and quicker results in many settings. Furthermore, research is needed to quickly identify E. cloacae complex to the species level in the microbiological laboratory.

Density Space Clustering Algorithm Based on Users Behaviors

&lt;p&gt;At present, insider threat detection requires a series of complex projects, and has certain limitations in practical applications; in order to reduce the complexity of the model, most studies ignore the timing of user behavior and fail to identify internal attacks that last for a period of time. In addition, companies usually categorize the behavior data generated by all users and store them in different databases. How to collaboratively process large-scale heterogeneous log files and extract characteristic data that accurately reflects user behavior is a difficult point in current research. In order to optimize the parameter selection of the DBSCAN algorithm, this paper proposes a Psychometric Data &amp; Attack Threat Density Based Spatial Clustering of Applications with Noise algorithm (PD&amp;AT-DBSCAN). This algorithm can improve the accuracy of clustering results. The simulation results show that this algorithm is better than the traditional DBSCAN algorithm in terms of Rand index and normalized mutual information.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

STICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. A subregion is created for each geographic object serving as the basic unit when performing clustering. A Markov random field is then constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in two use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1 score. Join count statistics is also calculated and shows that the spatial contiguity is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc.

A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI

We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent gray-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired five times at a resolution of 0.547 mm × 0.547 mm × 0.800 mm and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing Adjusted Rand Indices (ARIs) between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject’s resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.

Triku: a feature selection method based on nearest neighbors for single-cell data.

BackgroundFeature selection is a relevant step in the analysis of single-cell RNA sequencing datasets. Most of the current feature selection methods are based on general univariate descriptors of the data such as the dispersion or the percentage of zeros. Despite the use of correction methods, the generality of these feature selection methods biases the genes selected towards highly expressed genes, instead of the genes defining the cell populations of the dataset.ResultsTriku is a feature selection method that favors genes defining the main cell populations. It does so by selecting genes expressed by groups of cells that are close in the k-nearest neighbor graph. The expression of these genes is higher than the expected expression if the k-cells were chosen at random. Triku efficiently recovers cell populations present in artificial and biological benchmarking datasets, based on adjusted Rand index, normalized mutual information, supervised classification, and silhouette coefficient measurements. Additionally, gene sets selected by triku are more likely to be related to relevant Gene Ontology terms and contain fewer ribosomal and mitochondrial genes.ConclusionTriku is developed in Python 3 and is available at https://github.com/alexmascension/triku.