Machine learning modelling for predicting the utilization of invasive and non-invasive ventilation throughout the ICU duration.

The goal of this work is to develop a Machine Learning model to predict the need for both invasive and non-invasive mechanical ventilation in intensive care unit (ICU) patients. Using the Philips eICU Research Institute (ERI) database, 2.6 million ICU patient data from 2010 to 2019 were analyzed.

Machine learning prediction of the total duration of invasive and non-invasive ventilation During ICU Stay.

Predicting the duration of ventilation in the ICU helps in assessing the risk of ventilator-induced lung injury, ensuring sufficient oxygenation, and optimizing resource allocation. Prior models provided a prediction of total duration without distinguishing between invasive and non-invasive ventilation. This work proposes two independent gradient boosting regression models for predicting the duration of invasive and non-invasive ventilation based on commonly available ICU features.

Association of systolic, diastolic, mean, and pulse pressure with morbidity and mortality in septic ICU patients: a nationwide observational study.

Background: Intensivists target different blood pressure component values to manage intensive care unit (ICU) patients with sepsis. We aimed to evaluate the relationship between individual blood pressure components and organ dysfunction in critically ill septic patients.

Methods: In this retrospective observational study, we evaluated 77,328 septic patients in 364 ICUs in the eICU Research Institute database.

Accurate and interpretable prediction of ICU-acquired AKI.

Purpose: We developed and validated two parsimonious algorithms to predict the time of diagnosis of any stage of acute kidney injury (any-AKI) or moderate-to-severe AKI in clinically actionable prediction windows.

Materials And Methods: In this retrospective single-center cohort of adult ICU admissions, we trained two gradient-boosting models: 1) any-AKI model, predicting the risk of any-AKI at least 6 h before diagnosis (50,342 admissions), and 2) moderate-to-severe AKI model, predicting the risk of moderate-to-severe AKI at least 12 h before diagnosis (39,087 admissions). Performance was assessed before disease diagnosis and validated prospectively.

High-sensitivity pattern discovery in large, paired multiomic datasets.

Motivation: Modern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features are essential. In experiments featuring multiple high-dimensional datasets collected from the same set of samples, it is useful to identify groups of associated features between the datasets in a way that provides high statistical power and false discovery rate (FDR) control.

Results: Here, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for structured association discovery between paired high-dimensional datasets.

Multivariable association discovery in population-scale meta-omics studies.

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies.

Estimation of Baseline Serum Creatinine with Machine Learning.

Introduction: Comparing current to baseline serum creatinine is important in detecting acute kidney injury. In this study, we report a regression-based machine learning model to predict baseline serum creatinine.

Methods: We developed and internally validated a gradient boosting model on patients admitted in Mayo Clinic intensive care units from 2005 to 2017 to predict baseline creatinine.

A statistical model for describing and simulating microbial community profiles.

Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework.

Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome.

Heterogeneous patient populations, complex pharmacology and low recruitment rates in the Intensive Care Unit (ICU) have led to the failure of many clinical trials. Recently, machine learning (ML) emerged as a new technology to process and identify big data relationships, enabling a new era in clinical trial design. In this study, we designed a ML model for predictively stratifying acute respiratory distress syndrome (ARDS) patients, ultimately reducing the required number of patients by increasing statistical power through cohort homogeneity.

Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.

Background: Acute kidney injury (AKI) carries a poor prognosis. Its incidence is increasing in the intensive care unit (ICU). Our purpose in this study is to develop and externally validate a model for predicting AKI in the ICU using patient data present prior to ICU admission.

Including urinary output to define AKI enhances the performance of machine learning models to predict AKI at admission.

Purpose: Acute kidney injury (AKI) is a prevalent and detrimental condition in intensive care unit patients. Most AKI predictive models only predict creatinine-triggered AKI (AKI) and might underperform when predicting urine-output-triggered AKI (AKI). We aimed to describe how admission AKI prediction models perform in all AKI patients.

Understanding the Fertilizer Management Impacts on Water and Nitrogen Dynamics for a Corn Silage Tile-Drained System in Canada.

Effective management of dairy manure is important to minimize N losses from cropping systems, maximize profitability, and enhance environmental sustainability. The objectives of this study were (i) to calibrate and validate the DeNitrification-DeComposition (DNDC) model using measurements of silage corn ( L.) biomass, N uptake, soil temperature, tile drain flow, NO leaching, NO emissions, and soil mineral N in eastern Canada, and (ii) to investigate the long-term impacts of manure management under climate variability.

The transcriptome of Darwin's bark spider silk glands predicts proteins contributing to dragline silk toughness.

Darwin's bark spider () produces giant orb webs from dragline silk that can be twice as tough as other silks, making it the toughest biological material. This extreme toughness comes from increased extensibility relative to other draglines. We show dragline-producing major ampullate (MA) glands highly express a novel silk gene transcript (MaSp4) encoding a protein that diverges markedly from closely related proteins and contains abundant proline, known to confer silk extensibility, in a unique GPGPQ amino acid motif.

Satb2 regulates proliferation and nuclear integrity of pre-osteoblasts.

Special AT-rich sequence binding protein 2 (Satb2) is a matrix attachment region (MAR) binding protein. Satb2 impacts skeletal development by regulating gene transcription required for osteogenic differentiation. Although its role as a high-order transcription factor is well supported, other roles for Satb2 in skeletal development remain unclear.

A Bayesian method for detecting pairwise associations in compositional data.

Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology.

Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium.

In order for human microbiome studies to translate into actionable outcomes for health, meta-analysis of reproducible data from population-scale cohorts is needed. Achieving sufficient reproducibility in microbiome research has proven challenging. We report a baseline investigation of variability in taxonomic profiling for the Microbiome Quality Control (MBQC) project baseline study (MBQC-base).

Fluoride Depletes Acidogenic Taxa in Oral but Not Gut Microbial Communities in Mice.

Fluoridation of drinking water and dental products prevents dental caries primarily by inhibiting energy harvest in oral cariogenic bacteria (such as and ), thus leading to their depletion. However, the extent to which oral and gut microbial communities are affected by host fluoride exposure has been underexplored. In this study, we modeled human fluoride exposures to municipal water and dental products by treating mice with low or high levels of fluoride over a 12-week period.

Molecular control of gut formation in the spider Parasteatoda tepidariorum.

The development of a digestive system is an essential feature of bilaterians. Studies of the molecular control of gut formation in arthropods have been studied in detail in the fruit fly Drosophila melanogaster. However, little is known in other arthropods, especially in noninsect arthropods.

Duplication and concerted evolution of MiSp-encoding genes underlie the material properties of minor ampullate silks of cobweb weaving spiders.

Background: Orb-web weaving spiders and their relatives use multiple types of task-specific silks. The majority of spider silk studies have focused on the ultra-tough dragline silk synthesized in major ampullate glands, but other silk types have impressive material properties. For instance, minor ampullate silks of orb-web weaving spiders are as tough as draglines, due to their higher extensibility despite lower strength.

House spider genome uncovers evolutionary shifts in the diversity and expression of black widow venom proteins associated with extreme toxicity.

Background: Black widow spiders are infamous for their neurotoxic venom, which can cause extreme and long-lasting pain. This unusual venom is dominated by latrotoxins and latrodectins, two protein families virtually unknown outside of the black widow genus Latrodectus, that are difficult to study given the paucity of spider genomes. Using tissue-, sex- and stage-specific expression data, we analyzed the recently sequenced genome of the house spider (Parasteatoda tepidariorum), a close relative of black widows, to investigate latrotoxin and latrodectin diversity, expression and evolution.

Field Nitrogen Losses Induced by Application Timing of Digestate from Dairy Manure Biogas Production.

Anaerobic digestion of dairy manure has environmental benefits, but the impact of effluent (i.e., digestate [DG]) application on environmental nitrogen (N) losses from soils has not been well quantified.

The Wnt and Delta-Notch signalling pathways interact to direct pair-rule gene expression via caudal during segment addition in the spider Parasteatoda tepidariorum.

In short-germ arthropods, posterior segments are added sequentially from a segment addition zone (SAZ) during embryogenesis. Studies in spiders such as Parasteatoda tepidariorum have provided insights into the gene regulatory network (GRN) underlying segment addition, and revealed that Wnt8 is required for dynamic Delta (Dl) expression associated with the formation of new segments. However, it remains unclear how these pathways interact during SAZ formation and segment addition.