Geographically distributed data management to support large-scale data analysis.

Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss in case a single data center experiences a disaster.

Face and Content Validity of an Artificial Eye Model for Secondary Intraocular Lens Fixation via Yamane Technique.

Purpose: To assess the face and content validity of an artificial eye model for secondary intraocular lens (IOL) fixation via the Yamane technique.

Methods: Ophthalmologists and residents participated in a 90-minute simulation session on secondary IOL fixation via the Yamane technique. Hands-on practice of this technique was performed on an artificial eye, the Bioniko Okulo BR8.

Comparison of 3D heads-up display system with conventional surgical microscopy for minimally invasive glaucoma surgery on an artificial eye model.

Objective: To survey ophthalmic surgeons' opinions comparing a novel three-dimensional (3D) heads-up display system with a conventional surgical microscopy for minimally invasive glaucoma surgery (MIGS) on an artificial eye model.

Materials And Methods: Twenty-one ophthalmologists at the 2021 Canadian Ophthalmological Society Annual Meeting in Halifax, Nova Scotia, underwent a 90-minute skills-transfer course on MIGS. Using an artificial eye model (SimulEYE iTrack Model; InsEYE LLC, Westlake Village, Calif.

Data quality model for assessing public COVID-19 big datasets.

For decision-making support and evidence based on healthcare, high quality data are crucial, particularly if the emphasized knowledge is lacking. For public health practitioners and researchers, the reporting of COVID-19 data need to be accurate and easily available. Each nation has a system in place for reporting COVID-19 data, albeit these systems' efficacy has not been thoroughly evaluated.

A novel correlation Gaussian process regression-based extreme learning machine.

An obvious defect of extreme learning machine (ELM) is that its prediction performance is sensitive to the random initialization of input-layer weights and hidden-layer biases. To make ELM insensitive to random initialization, GPRELM adopts the simple an effective strategy of integrating Gaussian process regression into ELM. However, there is a serious overfitting problem in kernel-based GPRELM (GPRELM).

Trajectories of Neurologic Recovery 12 Months After Hospitalization for COVID-19: A Prospective Longitudinal Study.

Background And Objective: Little is known about trajectories of recovery 12 months after hospitalization for severe COVID-19.

Methods: We conducted a prospective, longitudinal cohort study of patients with and without neurologic complications during index hospitalization for COVID-19 from March 10, 2020, to May 20, 2020. Phone follow-up batteries were performed at 6 and 12 months after COVID-19 onset.

FINC: An Efficient and Effective Optimization Method for Normalized Cut.

The optimization methods for solving the normalized cut model usually involve three steps, i.e., problem relaxation, problem solving and post-processing.

Comparison of serum neurodegenerative biomarkers among hospitalized COVID-19 patients versus non-COVID subjects with normal cognition, mild cognitive impairment, or Alzheimer's dementia.

Introduction: Neurological complications among hospitalized COVID-19 patients may be associated with elevated neurodegenerative biomarkers.

Methods: Among hospitalized COVID-19 patients without a history of dementia (N = 251), we compared serum total tau (t-tau), phosphorylated tau-181 (p-tau181), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), ubiquitin carboxy-terminal hydrolase L1 (UCHL1), and amyloid beta (Aβ40,42) between patients with or without encephalopathy, in-hospital death versus survival, and discharge home versus other dispositions. COVID-19 patient biomarker levels were also compared to non-COVID cognitively normal, mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia controls (N = 161).

Toxic Metabolic Encephalopathy in Hospitalized Patients with COVID-19.

Background: Toxic metabolic encephalopathy (TME) has been reported in 7-31% of hospitalized patients with coronavirus disease 2019 (COVID-19); however, some reports include sedation-related delirium and few data exist on the etiology of TME. We aimed to identify the prevalence, etiologies, and mortality rates associated with TME in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive patients.

Methods: We conducted a retrospective, multicenter, observational cohort study among patients with reverse transcriptase-polymerase chain reaction-confirmed SARS-CoV-2 infection hospitalized at four New York City hospitals in the same health network between March 1, 2020, and May 20, 2020.

Governance models for nature-based solutions: Seventeen cases from Germany.

Nature-based solutions (NBS) for mitigating climate change are gaining popularity. The number of NBS is increasing, but research gaps still exist at the governance level. The objectives of this paper are (i) to give an overview of the implemented NBS for flood risk management and mitigation in Germany, (ii) to identify governance models that are applied, and (iii) to explore the differences between these models.

Activeness and Loyalty Analysis in Event-Based Social Networks.

Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks.

Treatment with Zinc is Associated with Reduced In-Hospital Mortality Among COVID-19 Patients: A Multi-Center Cohort Study.

Background: Zinc impairs replication of RNA viruses such as SARS-CoV-1, and may be effective against SARS-CoV-2. However, to achieve adequate intracellular zinc levels, administration with an ionophore, which increases intracellular zinc levels, may be necessary. We evaluated the impact of zinc with an ionophore (Zn+ionophore) on COVID-19 in-hospital mortality rates.

The knowledge transfer potential of online data pools on nature-based solutions.

Improving the adoption of Nature-based Solutions (NBS) requires learning from successes and failures. Knowledge derived from implemented cases helps to identify for instance drivers and barriers of NBS implementation, generates lessons learned, and supports their upscaling. Online data pools that catalogue information from NBS case studies may help scientists and practitioners to create this knowledge.

A Prospective Study of Neurologic Disorders in Hospitalized Patients With COVID-19 in New York City.

Objective: To determine the prevalence and associated mortality of well-defined neurologic diagnoses among patients with coronavirus disease 2019 (COVID-19), we prospectively followed hospitalized severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive patients and recorded new neurologic disorders and hospital outcomes.

Methods: We conducted a prospective, multicenter, observational study of consecutive hospitalized adults in the New York City metropolitan area with laboratory-confirmed SARS-CoV-2 infection. The prevalence of new neurologic disorders (as diagnosed by a neurologist) was recorded and in-hospital mortality and discharge disposition were compared between patients with COVID-19 with and without neurologic disorders.

Prevalence and Impact of Hyponatremia in Patients With Coronavirus Disease 2019 in New York City.

Objectives: Hyponatremia occurs in up to 30% of patients with pneumonia and is associated with increased morbidity and mortality. The prevalence of hyponatremia associated with coronavirus disease 2019 and the impact on outcome is unknown. We aimed to identify the prevalence, predictors, and impact on outcome of mild, moderate, and severe admission hyponatremia compared with normonatremia among coronavirus disease 2019 patients.

SS-31 Peptide Reverses the Mitochondrial Fragmentation Present in Fibroblasts From Patients With DCMA, a Mitochondrial Cardiomyopathy.

We used patient dermal fibroblasts to characterize the mitochondrial abnormalities associated with the dilated cardiomyopathy with ataxia syndrome (DCMA) and to study the effect of the mitochondrially-targeted peptide SS-31 as a potential novel therapeutic. DCMA is a rare and understudied autosomal recessive disorder thought to be related to Barth syndrome but caused by mutations in , a protein of unknown function localized to the mitochondria. The clinical disease is characterized by 3-methylglutaconic aciduria, dilated cardiomyopathy, abnormal neurological development, and other heterogeneous features.

LABIN: Balanced Min Cut for Large-Scale Data.

Although many spectral clustering algorithms have been proposed during the past decades, they are not scalable to large-scale data due to their high computational complexities. In this paper, we propose a novel spectral clustering method for large-scale data, namely, large-scale balanced min cut (LABIN). A new model is proposed to extend the self-balanced min-cut (SBMC) model with the anchor-based strategy and a fast spectral rotation with linear time complexity is proposed to solve the new model.

Local Adaptive Projection Framework for Feature Selection of Labeled and Unlabeled Data.

Most feature selection methods first compute a similarity matrix by assigning a fixed value to pairs of objects in the whole data or to pairs of objects in a class or by computing the similarity between two objects from the original data. The similarity matrix is fixed as a constant in the subsequent feature selection process. However, the similarities computed from the original data may be unreliable, because they are affected by noise features.

An Algorithm for Clustering Categorical Data With Set-Valued Features.

In data mining, objects are often represented by a set of features, where each feature of an object has only one value. However, in reality, some features can take on multiple values, for instance, a person with several job titles, hobbies, and email addresses. These features can be referred to as set-valued features and are often treated with dummy features when using existing data mining algorithms to analyze data with set-valued features.

Subspace Weighting Co-Clustering of Gene Expression Data.

Microarray technology enables the collection of vast amounts of gene expression data from biological experiments. Clustering algorithms have been successfully applied to exploring the gene expression data. Since a set of genes may be only correlated to a subset of samples, it is useful to use co-clustering to recover co-clusters in the gene expression data.

Unbiased feature selection in learning random forests for high-dimensional data.

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data.

Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests.

Background: Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases.

Post-processing strategies for improving local gene expression pattern analysis.

This paper proposes a new analytical process highlighted by a soft subspace clustering method, a changing window technique, and a series of post-processing strategies to enhance the identification and characterisation of local gene expression patterns. The proposed method can be conducted in an interactive way, facilitating the exploration and analysis of local gene expression patterns in real applications. Experimental results have shown that the proposed method is effective in identification and characterization of functional gene groups in terms of both local expression similarities and biological coherence of genes in a cluster.

On the impact of dissimilarity measure in k-modes clustering algorithm.

This correspondence describes extensions to the k-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.