Triple Nucleophilic Head-to-Tail Cascade Polycyclization of Diazodienals via Combination Catalysis: Direct Access to Cyclopentane Fused Aza-Polycycles with Six-Contiguous Stereocenters.

Reported herein are the bench stable (24)-diazohexa-2,4-dienals (diazodienals) and their unprecedented polycyclization with aldimine and arylamines enabled by Rh(II)/Brønsted acid relay catalysis. This scalable and atom-economical reaction provides direct access to the biologically important azatricyclo[6.2.

Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.

Machine learning (ML) could have advantages over traditional statistical models in identifying risk factors. Using ML algorithms, our objective was to identify the most important variables associated with mortality after dementia diagnosis in the Swedish Registry for Cognitive/Dementia Disorders (SveDem). From SveDem, a longitudinal cohort of 28,023 dementia-diagnosed patients was selected for this study.

Vital sign-based detection of sepsis in neonates using machine learning.

Aim: Sepsis is a leading cause of morbidity and mortality in neonates. Early diagnosis is key but difficult due to non-specific signs. We investigate the predictive value of machine learning-assisted analysis of non-invasive, high frequency monitoring data and demographic factors to detect neonatal sepsis.

Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms.

The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had visible scars on short-axis LGE-CMR images and were candidates for CABG surgery were selected and enrolled in this study. MR imaging was performed preoperatively using a 1.

Evaluation of debris and smear layer formation using three different NI-TI rotary instrument systems: An scanning electron microscope study.

Aim: The aim of the study is to evaluate of debris and smear layer formation after using rotary ProTaper Universal, Twisted File, and XP Endo file systems under scanning electron microscope.

Materials And Methods: Forty freshly extracted mandibular second premolar teeth were taken to decoronate at the cementoenamel junction to make the remaining root length 15 mm. Specimens were divided into four groups of 10 teeth each, Group I (control) - no instrumentation.

Effect of four different dentifrices applied by customized automated brushing device on enamel surface abrasion: An profilometric study.

Aim: The aim of this study is to evaluate the enamel surface abrasion using four different dentifrices and a customized automated brushing machine under a profilometer.

Materials And Methods: A total of 30 enamel blocks (9 mm × 9 mm × 2 mm) were prepared from freshly extracted maxillary central incisors which were randomly divided into five equal groups (Group 1: specimens brushed with Colgate Total, Group 2: specimens brushed with Colgate Lemon and Salt, Group 3: specimens brushed with Colgate Visible White, Group 4: specimens brushed with Colgate Sensitive, and Group 5: intact enamel surface). Samples were brushed using a customized automated toothbrushing machine for 60 min.

ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition.

Motivation: Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging.

Results: There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition.

SEK: sparsity exploiting k-mer-based estimation of bacterial community composition.

Motivation: Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. As the sample sequence data typically harbors reads of variable lengths and different levels of biological and technical noise, accurate statistical analysis of such data is challenging. Currently popular estimation methods are typically time-consuming in a desktop computing environment.