Assessment of the effect of management activities like a drought salinity barrier and herbicide treatments on the spread of submersed and floating aquatic estuary macrophytes.

Estuaries are complex ecosystems, being difficult to determine the way management actions affect them. This study quantitatively evaluated the spread of invasive submerged and floating aquatic macrophyte vegetation in Franks Tract of the Sacramento-San Joaquin Delta in response to two types of management actions, drought salinity barriers in years 2015, 2021 and 2022, and herbicide treatments in years 2004-2022. A Random Forest algorithm applied to airborne hyperspectral and satellite multispectral images generated maps of macrophyte cover in 2004-2022.

A community-of-practice-based evaluation methodology for knowledge intensive computational methods and its application to multimorbidity decision support.

Objective: The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings.

Modelling and assessing one- and two-drug dose titrations.

In health-care, there is a need to quantify medical errors. Among these errors, we observe wrong dose prescriptions. Drug dose titration (DT) is the process by which dosage is progressively adjusted to the patient till a steady dose is reached.

A remote sensing-based three-source energy balance model to improve global estimations of evapotranspiration in semi-arid tree-grass ecosystems.

It is well documented that energy balance and other remote sensing-based evapotranspiration (ET) models face greater uncertainty over water-limited tree-grass ecosystems (TGEs), representing nearly 1/6th of the global land surface. Their dual vegetation strata, the grass-dominated understory and tree-dominated overstory, make for distinct structural, physiological and phenological characteristics, which challenge models compared to more homogeneous and energy-limited ecosystems. Along with this, the contribution of grasses and trees to total transpiration (T), along with their different climatic drivers, is still largely unknown nor quantified in TGEs.

Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning.

Acute respiratory distress syndrome (ARDS) is an intense inflammatory process of the lungs. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration over time.

Novel criteria to classify ARDS severity using a machine learning approach.

Background: Usually, arterial oxygenation in patients with the acute respiratory distress syndrome (ARDS) improves substantially by increasing the level of positive end-expiratory pressure (PEEP). Herein, we are proposing a novel variable [PaO/(FiOxPEEP) or P/FP] for PEEP ≥ 5 to address Berlin's definition gap for ARDS severity by using machine learning (ML) approaches.

Methods: We examined P/FP values delimiting the boundaries of mild, moderate, and severe ARDS.

Methods and measures to quantify ICU patient heterogeneity.

Patients in intensive care units are heterogeneous and the daily prediction of their days to discharge (DTD) a complex task that practitioners and computers are not always able to solve satisfactorily. In order to make more precise DTD predictors, it is necessary to have tools for the analysis of the heterogeneity of the patients. Unfortunately, the number of publications in this field is almost non-existent.

Author Correction: Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Ten years of knowledge representation for health care (2009-2018): Topics, trends, and challenges.

Background: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide.

Objectives: Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future.

Persistence of data-driven knowledge to predict breast cancer survival.

Background: Machine learning predictive models for breast cancer survival can improve if they are made specific to the stage of the cancer at the time of diagnosis. However, the relevance of the clinical parameters in that prediction, and the predictive quality of these models may change over time.

Objective: To determine whether the findings on the influence of clinical parameters and the performance of machine learning models in the prediction of breast cancer survival have to be considered temporary or permanent, and if temporary what is the period of validity of the new generated knowledge.

Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications.

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC.

Knowledge Formalization to Support Decision-Making in Heart Failure Treatment.

This paper presents medical knowledge representation of data provided within Clinical Practice Guidelines for Heart Failure. The formalization is provided in order to support taking decisions on an appropriate treatment strategy for a specific patient. An intuitive and efficient mechanism of medical knowledge formalization, called extended Timed Transition Diagram (eTTD), is used to represent acquired medical knowledge.

Improving resident's skills in the management of circulatory shock with a knowledge-based e-learning tool.

Background: Correct clinical management of circulatory shock in emergency departments (ER) and intensive care units (ICU) is critical. In this context, the transmission of professional skills by means of the practical supervision of real cases at the point of care entails important issues that can be widely overcome with the use of computer knowledge-based e-learning tools.

Objective: Shock-Instructor is a web-based e-learning tool implementing the already tested training program model (TPM) that uses a knowledge base about the evidence found in the clinical practice guidelines about seven types of shock.

Formalization and acquisition of temporal knowledge for decision support in medical processes.

Background: In medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care centers use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions.

Computer technologies to integrate medical treatments to manage multimorbidity.

The high prevalence of multimorbid cases is a challenge for Health-Care Systems today. Clinical practice guidelines are the means to register and transmit the available evidence-based medical knowledge concerning concrete diseases. Several computer languages have been defined to represent this knowledge in a way that computers could use to help physicians in the daily practice of medicine.

Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms.

The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain.

Bee pathogens found in Bombus atratus from Colombia: A case study.

Bombus atratus bumblebees from Colombia that were caught in the wild and from breeding programs were screened for a broad set of bee pathogens. We discovered for the first time Lake Sinai Virus and confirmed the infection by other common viruses. The prevalence of Apicystis bombi, Crithidia bombi and Nosema ceranae was remarkably high.

An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients.

Chronically ill patients are complex health care cases that require the coordinated interaction of multiple professionals. A correct intervention of these sort of patients entails the accurate analysis of the conditions of each concrete patient and the adaptation of evidence-based standard intervention plans to these conditions. There are some other clinical circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases or prevention, whose detection depends on the capacities of deduction of the professionals involved.

High adiponectin concentrations are associated with the metabolically healthy obese phenotype.

Context: In the ob/ob mice, keeping adiponectin concentrations in the physiological range (through overexpression of this gene in the adipose tissue) results in expansion of fat mass and protection against metabolic co-morbidities.

Objective: The aim of the study was to test in humans whether plasma adiponectin levels, similar to those found in lean subjects, are associated with the metabolically healthy obese phenotype.

Design And Setting: A cross-sectional analysis was performed of a cohort of obese and nonobese subjects aged 18-70 yr.

Knowledge engineering as a support for building an actor profile ontology for integrating Home-Care systems.

One of the tasks towards the definition of a knowledge model for home care is the definition of the different roles of the users involved in the system. The roles determine the actions and services that can or must be performed by each type of user. In this paper the experience of building an ontology to represent the home-care users and their associated information is presented, in a proposal for a standard model of a Home-Care support system to the European Community.

The ATP-binding cassette transporter A1 R230C variant affects HDL cholesterol levels and BMI in the Mexican population: association with obesity and obesity-related comorbidities.

Although ATP-binding cassette transporter A1 (ABCA1) is well known for its role in cholesterol efflux and HDL formation, it is expressed in various tissues, where it may have different functions. Because hypoalphalipoproteinemia is highly prevalent in Mexico, we screened the ABCA1 coding sequence in Mexican individuals with low and high HDL cholesterol levels to seek functional variants. A highly frequent nonsynonymous variant (R230C) was identified in low-HDL cholesterol but not in high-HDL cholesterol individuals (P = 0.

[Fecal incontinence in the elderly].

Unlabelled: Fecal incontinence (FI) is a devastating condition that affects quality of life. Age and gender are related with the prevalence of FI. In adults, prevalence varies from 2% to 7%.