We consider statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters. Examples include data on ...individuals with clustering on village or region or other category such as industry, and state-year differences-in-differences studies with clustering on state. In such settings, default standard errors can greatly overstate estimator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. We outline the basic method as well as many complications that can arise in practice. These include cluster-specific fixed effects, few clusters, multiway clustering, and estimators other than OLS.
The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the ‘rule of 42’ is not true for unbalanced clusters. ...Rejection frequencies are higher for datasets with 50 clusters proportional to US state populations than with 50 balanced clusters. Using critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of clusters is treated. We explain why CRVE t statistics and the wild bootstrap fail in this case, study the ‘effective number’ of clusters and simulate placebo laws with dummy variable regressors.
Drug discovery and development are long and financially taxing processes. On an average it takes 12–15 years and costs 1.2 billion USD for successful drug discovery and approval for clinical use. ...Many lead molecules are not developed further and their potential is not tapped to the fullest due to lack of resources or time constraints. In order for a drug to be approved by FDA for clinical use, it must have excellent therapeutic potential in the desired area of target with minimal toxicities as supported by both pre-clinical and clinical studies. The targeted clinical evaluations fail to explore other potential therapeutic applications of the candidate drug. Drug repurposing or repositioning is a fast and relatively cheap alternative to the lengthy and expensive de novo drug discovery and development. Drug repositioning utilizes the already available clinical trials data for toxicity and adverse effects, at the same time explores the drug's therapeutic potential for a different disease. This review addresses recent developments and future scope of drug repositioning strategy.
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•Repurposed drugs categorized based on biological activity.•Various approaches to drug repurposing.•Various resources for drug repurposing.•Drug repurposing in various diseases latest information.
Accurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle to provide timely and ...accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency.
We propose CDSKNN
(Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure.
We evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods.
The CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.
Through a survey of rose diseases in the South Tropical Garden in Kunming, China, it was found that black spot was the most common and serious disease of rose cultivated in the open air there, with ...an incidence of more than 90%. In this study, fungus isolation was performed on leaf samples of five black spot susceptible varieties of rose from the South Tropical Garden by tissue isolation. 18 strains of fungus were initially obtained, and seven of them were finally identified to cause black spot symptoms on healthy leaves of rose after verification by Koch's rule. By observing the morphology of colonies and spores, and constructing a phylogenetic tree by combining molecular biology and multiple genes, two pathogenic fungus were identified, namely, Alternaria alternata and Gnomoniopsis rosae. G. rosae was the first pathogenic fungi of rose black spot isolated and identified in this study. The results of this study can provide a reference base for further research and control of the black spot disease of rose in Kunming.
Abstract
Introduction
Sleep bruxism (BXM) is the result of rhythmic muscular masticatory activity (RMMA) and can be captured by masseters surface electromyography (sEMG). Despite the multiple adverse ...negative consequences of BXM, a simple reliable home diagnostic device is currently unavailable, with in laboratory audio-video polysomnography (type I PSG) remaining the gold standard diagnostic tool. Mandibular movements (MM) recordings during sleep can readily identify RMMA, are simple to set up and can be easily repeated from night to night. Here, we aimed to identify stereotypical MM in patients with BXM, and to develop RMMA automatic detection and BXM diagnosis using an artificial intelligence-based approach.
Methods
MM were recorded by a dedicated sensor (Sunrise, Namur, Belgium) in 12 patients with BXM during type I PSG. The Sunrise system consists of a coin-sized hardware that is comfortably placed on the subject’s chin. Its embedded inertial measurement unit communicates via Bluetooth with a smartphone and automatically transfers MM signals to a cloud-based infrastructure at the end of the night. Data processing and analysis are then performed in Python programming language.
A time series cluster analysis was applied to sequences of masseters sEMG and MM signals during BXM episodes (n=300) and during spontaneous micro-arousals (n=300). Then, a convolutional neuronal network (CNN) was developed to identify BXM and distinguish it from spontaneous micro-arousals while exclusively relying on MM signal.
Results
Based on the cluster analysis, BXM periods were characterized by a specific pattern of MM signals (higher frequency and amplitude), which was closely associated with the sEMG signals but clearly differed from the MM signal patterns during micro-arousals.
CNN-based classifier distinguished the BXM events from other RMMAs during micro-arousals and respiratory efforts with an overall accuracy of 91%.
Conclusion
Sleep bruxism can be automatically identified, quantified, and characterized with mandibular movements analysis supported by artificial intelligence technology.
Support
This work was supported by the French National Research Agency (ANR-12-TECS-0010), in the framework of the “Investissements d’avenir” program (ANR-15-IDEX-02). https://life.univ-grenoble-alpes.fr.
Background: Clinical trials for traumatic spine injury (TSI) are difficult to conduct because of the amount of variation in neurologic impairments and patient characteristics within a population. ...Identifying homogeneous subgroups may improve the effective implementation of treatment strategies and patient trajectories. We hypothesize that using automated clustering can mimic or improve preexisting (traditional) classification Methods: Our objective was to identify the features influencing the optimized clusters creating each homogenous group of patients. Methods: Spectral cluster analysis was deployed to group similar patients on the basis of baseline prognostic factors: location of injury, severity of injury, baseline Functional Independence Measure (FIM) motor score and demographic data (age, body mass index). The FIM motor score at follow-up and the total length of stay were superimposed on identified clusters as outcome variables. Prognostic factors used in spectral cluster analysis and superimposed outcome variables were assessed statistically. The performance of the spectral algorithm was evaluated by the silhouette coefficient, the Davies-Bouldin index and the elbow method to determine the optimal number of clusters. The identified clusters were qualitatively and systematically described to explore patient-focused insights. Results: A total of 338 patients with TSI from the Rick Hansen Spinal Cord Injury Registry (RHSCIR) were analyzed from baseline to follow-up. Five distinct clusters were identified with a statistically significant difference (p < 0.05) for prognostic factors. Outcome variables were statistically significant among all clusters. Conclusion: Using automated clustering, we found significant differences between prognostic variables and superimposed outcome variables across all 5 clusters. Qualitative descriptions of distinguishing prognostic variables for each cluster revealed that the spectral cluster analysis provided clinically intuitive groupings of patients. Additionally, we observed a relationship between the location of the injury and the mechanism of injury among all clusters, which adds significant clinical categorizations of patients with TSI in their classification. This data-driven approach identified homogenous subgroups without depending on a priori decisions, which is a step toward improving traditional classification.
Defining clusters of related industries Delgado, Mercedes; Porter, Michael E.; Stern, Scott
Journal of economic geography,
01/2016, Volume:
16, Issue:
1
Journal Article
Peer reviewed
Clusters are geographic concentrations of industries related by knowledge, skills, inputs, demand and/or other linkages. There is an increasing need for cluster-based data to support research, ...facilitate comparisons of clusters across regions and support policymakers in defining regional strategies. This article develops a novel clustering algorithm that systematically generates and assesses sets of cluster definitions (i.e., groups of closely related industries). We implement the algorithm using 2009 data for U.S. industries (six-digit NAICS), and propose a new set of benchmark cluster definitions that incorporates measures of inter-industry linkages based on co-location patterns, input–output links, and similarities in labor occupations. We also illustrate the algorithm’s ability to compare alternative sets of cluster definitions by evaluating our new set against existing sets in the literature. We find that our proposed set outperforms other methods in capturing a wide range of inter-industry linkages, including the grouping of industries within the same three-digit NAICS.
Reply to Rose et al Anderson, Evan J; Kamidani, Satoshi; Orenstein, Walt ...
Clinical infectious diseases,
01/2022, Volume:
74, Issue:
1
Journal Article
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