Influence of the choice of physical and chemistry variables on interpreting patterns of sediment contaminants and their relationships with estuarine macrobenthic communities

2010 ◽  
Vol 61 (10) ◽  
pp. 1109 ◽  
Author(s):  
Anthony A. Chariton ◽  
Anthony C. Roach ◽  
Stuart L. Simpson ◽  
Graeme E. Batley
Keyword(s):  
Environmental Variables ◽  
Data Extraction ◽  
Extraction Procedure ◽  
Primary Objective ◽  
Metal Extraction ◽  
Acid Volatile Sulfide ◽  
Macrobenthic Assemblages ◽  
Sediment Contaminants ◽  
Components Analysis ◽  
Iron And Manganese

A primary objective of contaminated sediment risk assessments is to identify if contaminant enrichment is eliciting an ecological response. Using complementary environmental and biotic datasets, we examined five scenarios with respect to: dataset complexity; metal extraction; normalisation of organics; the inclusion/exclusion of acid-volatile sulfide data, and iron and manganese concentrations. Spatial distributions of abiotic variables were examined by principal components analysis, with canonical correspondence analysis used to examine the total and partitioning of biological variation. Metals were the dominant contaminant and explained the largest proportion of variation in the macrobenthic data. Extraction procedure and carbon normalisation of organics had little influence on the overall analysis. Porewater metal data was essential for interpretation, with excess of acid-volatile sulfide over simultaneously extractable metals being a poor surrogate. In the canonical correspondence analyses, the inclusion of Fe/Mn accentuated the covariation between the ecological and contaminant variables. Multimodel comparisons aided interpretation by emphasising specific relationships among environmental variables and their interactions with the biotic data. Furthermore, for future examinations of the described system, the findings can be used to reduce the collection of redundant environmental variables or variables that are poorly correlated with changes in macrobenthic assemblages.

scholarly journals Algorithms for Prediction of Clinical Deterioration on the General Wards: A Scoping Review

2021 ◽  
Author(s):  
Roel V Peelen ◽  
Yassin Eddahchouri ◽  
Mats Koeneman ◽  
Tom H van de Belt ◽  
Harry van Goor ◽  
...  
Keyword(s):  
Scoping Review ◽  
Data Extraction ◽  
Vital Signs ◽  
Population Data ◽  
Primary Objective ◽  
General Ward ◽  
Clinical Deterioration ◽  
Predictive Algorithms ◽  
Study Characteristics ◽  
Input Variables

OBJECTIVE: The primary objective of this scoping review was to identify and describe state-of-the-art models that use vital sign monitoring to predict clinical deterioration on the general ward. The secondary objective was to identify facilitators, barriers, and effects of implementing these models. DATA SOURCES: PubMed, Embase, and CINAHL databases until November 2020. STUDY SELECTION: We selected studies that compared vital signs–based automated real-time predictive algorithms to current track-and-trace protocols in regard to the outcome of clinical deterioration in a general ward population. DATA EXTRACTION: Study characteristics, predictive characteristics and barriers, facilitators, and effects. RESULTS: We identified 1,741 publications, 21 of which were included in our review. Two of the these were clinical trials, 2 were prospective observational studies, and the remaining 17 were retrospective studies. All of the studies focused on hospitalized adult patients. The reported area under the receiver operating characteristic curves ranged between 0.65 and 0.95 for the outcome of clinical deterioration. Positive predictive value and sensitivity ranged between 0.223 and 0.773 and 7.2% to 84.0%, respectively. Input variables differed widely, and predicted endpoints were inconsistently defined. We identified 57 facilitators and 48 barriers to the implementation of these models. We found 68 reported effects, 57 of which were positive. CONCLUSION: Predictive algorithms can detect clinical deterioration on the general ward earlier and more accurately than conventional protocols, which in one recent study led to lower mortality. Consensus is needed on input variables, predictive time horizons, and definitions of endpoints to better facilitate comparative research.

Heavy metal pollution and macrobenthic assemblages in soft sediments in two Sydney estuaries, Australia

1998 ◽  
Vol 49 (6) ◽  
pp. 533 ◽  
Author(s):  
Jonathan S. Stark
Keyword(s):  
Heavy Metals ◽  
Environmental Variables ◽  
Spatial Scales ◽  
Organic Matter Content ◽  
Matter Content ◽  
Soft Sediments ◽  
Lead And Zinc ◽  
Macrobenthic Assemblages ◽  
Order Of Magnitude ◽  
Copper Lead

The influence of heavy metals (copper, lead and zinc) associated with urban runoff, on assemblages of macrofauna in intertidal soft sediments was studied in two estuaries in the Sydney region. The patterns of distribution and abundance of fauna and assemblages was found to vary significantly at several spatial scales: within bays in an estuary, between bays within an estuary and between bays from different estuaries. Significant differences were found in concentrations of heavy metals in sediments, but there was very little difference among bays in other environmental variables: grain-size characteristics and organic matter content of sediments. Bays polluted by heavy metals had significantly different assemblages to unpolluted bays, were generally less diverse and were characterized by an order-of-magnitude greater abundance of capitellids, spionids, nereids and bivalves. Unpolluted bays had greater abundance of crustaceans and several polychaete families, including paraonids and nephtyids and were generally more diverse. There was a significant correlation between patterns of assemblages and concentrations of heavy metals, but not with other environmental variables.

Consistent patterns of variation in macrobenthic assemblages and environmental variables over multiple spatial scales using taxonomic and functional approaches

2016 ◽  
Vol 120 ◽  
pp. 191-201 ◽  
Author(s):  
Puri Veiga ◽  
Ana Catarina Torres ◽  
Fernando Aneiros ◽  
Isabel Sousa-Pinto ◽  
Jesús S. Troncoso ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Spatial Scales ◽  
Multiple Spatial Scales ◽  
Macrobenthic Assemblages

SEARCH PROCEDURE AND DATA EXTRACTION FOR META-ANALYSIS IN BIOMEDICAL RESEARCH

2021 ◽  
Author(s):  
Jasmina Obradovic ◽  
◽  
Vladimir Jurisic
Keyword(s):  
Biomedical Research ◽  
Data Extraction ◽  
Meta Analysis ◽  
Extraction Procedure ◽  
Microsoft Excel ◽  
Pooled Data ◽  
Reference Manager ◽  
Reference Number ◽  
Meta Analyses ◽  
Precise Time

The meta-analysis provides a unique scientific conclusion with precise statistical analysis of pooled data extracted from previously reported relevant studies. That gives a better insight into the current issue with more statistical certainty than any single study observation in biomedical research. Occasionally, meta-analyses don’t provide a precise time for each step of the search strategy. The complete meta-analysis procedure is usually time-consuming, with 6-18 months reported, but it depends on the numbers of collected articles manually reviewed by two or more researchers to prevent potential bias. The purpose of this paper was to present a part of meta-analysis research with a focus on a timeline manner for extraction procedure and suggestions for preparing the database of collected articles. PRISMA guidelines were followed, and Pub Med, Scopus, and ISI Web of Science for the search were used. EndNote reference manager v.7 and Microsoft Excel 2007 were used for base preparation. Results showed that the final reference number was 4918, and 99.88% of them were excluded. A month was necessary for the search of the electronic databases. For reading titles and abstracts and extracting the papers was needed the fourth month. A month was needed for an additional search of bibliographies of the eligible papers. Even with the dedication of the team of reviewers, it is hard to predict the exact time for conducting the meta-analysis, indeed. Our results could be applicable in planning the potential systematic reviews, with or without meta-analysis, and overcoming the obstacles in the single database preparation.

Nonviolent Discipline Options for Caregivers and Teachers: A Systematic Overview of the Evidence

2020 ◽  
pp. 152483802096734
Author(s):  
Karen R. Quail ◽  
Catherine L. Ward
Keyword(s):  
Corporal Punishment ◽  
Systematic Reviews ◽  
Challenging Behavior ◽  
Data Extraction ◽  
Primary Objective ◽  
Physical Punishment ◽  
Systematic Overview ◽  
Wide Range ◽  
Overview Of Systematic Reviews ◽  
State Of The Science

Violence against children is a widespread problem with devastating consequences, and corporal punishment is a risk factor for more serious forms of physical abuse. One reason for the persistence of corporal punishment may be the lack of awareness of positive disciplinary alternatives. Nonviolent options offered to caregivers and teachers must be effective in addressing challenging behavior, or they may be rejected in favor of a return to physical punishment. There is an urgent need to determine which discipline options are evidence-supported and what that evidence says so that robust alternatives to corporal punishment can be made available. The primary objective of this research was to find, and explore the state of the science on, individual nonviolent interventions for challenging behavior, in so doing forming a “tool kit” for use by caregivers and teachers. A systematic overview of systematic reviews was conducted. Included systematic reviews were peer-reviewed and published in English between 1999 and 2018. Screening, quality assessment using AMSTAR, and data extraction were performed independently by two reviewers. A total of 223 reviews were included, covering data from 3,921 primary studies. A wide range of evidence-supported interventions exist, many of which have been found effective with severely challenging behavior. Important positive outcomes shown suggest that the use of these tools should be promoted not only for the prevention of violence but also for optimum child development. More research is needed on the use of these methods in home situations and on de-escalation skills.

scholarly journals Chemical and Biological Leaching Methods to Remove Heavy Metals from Sewage Sludge: A Review

2008 ◽  
Vol 4 (3) ◽  
pp. 509-517
Author(s):  
Lugard N. Ukiwe ◽  
Allinor J.I ◽  
Ejele A.E ◽  
Anyadiegwu C.I.C ◽  
Ibeneme S.I
Keyword(s):  
Heavy Metals ◽  
Sewage Sludge ◽  
Thiobacillus Ferrooxidans ◽  
Extraction Procedure ◽  
Metal Extraction ◽  
High Yield ◽  
Chemical Leaching ◽  
Solids Concentration ◽  
Acid Consumption ◽  
Removal Of Heavy Metals

The removal of heavy metals (HMs) in sewage sludge (SS) is important since sludge is often disposed or applied on farmland to enhance soil fertility. The present study reviewed two conceptual approaches (chemical and biological leaching) of removing HMs present in SS. In the chemical leaching method, traditional acid treatment together with novel methods such as aeration, complexation and sequential extraction procedure have been reviewed extensively. Certain factors influence the removal of HMs in SS. These factors include; pH, leaching agent, redox potential, and contact time. Nitric acid (HNO3), hydrochloric acid (HCl), sulphuric acid (H2SO4), phosphoric acid (H3PO4), ethylenediamine tetraacetic acid (EDTA), as well as Thiobacillus thiooxidans and Thiobacillus ferrooxidans are the most widely studied leaching agents and substrates involved in the chemical and bioleaching processes. However, the bioleaching process has been proposed as a safe, efficient, economical, environmental friendly method to remove HMs in SS due to its simplicity, high yield of metal extraction, low acid consumption, and low sludge solids concentration. Nevertheless, the present review has noted that most researchers are of the opinion that more studies are needed in the bioleaching method in order to enhance its commercial attraction.  

scholarly journals Utilisation de l’intelligence artificielle en pharmacie : une revue narrative

2021 ◽  
Vol 74 (2) ◽  
Author(s):  
Laura Gosselin ◽  
Maxime Thibault ◽  
Denis Lebel ◽  
Jean-François Bussières
Keyword(s):  
Data Extraction ◽  
The United States ◽  
Primary Objective ◽  
Narrative Review ◽  
Advanced Technology ◽  
Electronic Prescription ◽  
Data Synthesis ◽  
Study Selection ◽  
Patient Prioritization ◽  
Secondary Objective

RÉSUMÉ Contexte : L’intelligence artificielle (IA) est une avancée technologique qui consiste à amener une machine à imiter une forme d’intelligence. Objectifs : L’objectif principal est d’effectuer une revue narrative des études évaluant la faisabilité et l’impact de l’IA en pharmacie. L’objectif secondaire est de développer une carte heuristique entourant l’IA en santé. Sources des données : Nous avons consulté quatre bases de données, soit PubMed, Medline, Embase et CINAHL. Sélection des études et extraction des données : Quatre stratégies de recherche ont été élaborées. Sélection des articles sur la base du titre, de l’abrégé puis du texte par une assistante de recherche, suivie d’une révision par un pharmacien de l’équipe. Les articles pris en compte doivent décrire ou évaluer la faisabilité ou l’impact de l’IA en pharmacie. Synthèse des données : À partir de la revue documentaire, 362 articles ont été sélectionnés au départ, 18 d’entre eux ont été retenus selon les critères d’inclusion. De façon générale, on note que les études ont été surtout menées aux États-Unis (72 %, 13/18). Les études portent, par ordre d’importance décroissant, sur la prédiction de la réponse aux traitements et la prédiction d’effets indésirables (33 %, 6/18), la priorisation des patients (28 %, 5/18), l’adhésion thérapeutique (22 %, 4/18), la validation d’ordonnances et la prescription électronique (17 %, 3/18) et d’autres thèmes (p. ex. diagnostic, coûts, assurance, vérification de volumes de seringue). Conclusions : Cette revue narrative met en évidence 18 études évaluant la faisabilité et l’impact de l’IA en pharmacie. Ces études ont utilisé différentes approches méthodologiques dans divers domaines d’application, en officine comme en établissement de santé. Il est encore trop tôt pour prédire les retombées de l’IA en pharmacie, mais ces études soulignent l’importance de s’y intéresser. ABSTRACT Background: Artificial intelligence (AI) can be described as an advanced technology in which machines display a certain form of intelligence. Objectives: The primary objective was to perform a narrative review of studies evaluating the feasibility and impact of AI in pharmacy. The secondary objective was to create a mind map of AI in health care. Data Sources: Four databases were consulted: PubMed, Medline, Embase, and CINAHL. Study Selection and Data Extraction: Four search strategies were developed. Initial selection of articles was based on their titles and abstracts; the full texts were then evaluated by a research assistant, with review by a pharmacist. Articles were included if they described or evaluated the feasibility or impact of AI in pharmacy. Data Synthesis: A total of 362 articles were identified by the literature review, of which 18 met the inclusion criteria. The studies were mainly conducted in the United States (72%, 13/18). The article topics were, in decreasing order, prediction of response to treatments and adverse effects (33%, 6/18), patient prioritization (28%, 5/18), treatment adherence (22%, 4/18), validation of prescriptions and electronic prescription (17%, 3/18), and other themes (e.g., diagnosis, costs, insurance, and verification of syringe volume). Conclusions: This narrative review highlighted 18 studies evaluating the feasibility and impact of AI in pharmacy. The studies used various methodologies in different settings, both retail pharmacies and hospital pharmacies. It is still too soon to predict the implications of AI for pharmacy, but these studies emphasize the importance of attention in this area.

scholarly journals Educational Initiatives and Implementation of Electroencephalography into the Acute Care Environment: a protocol of a Systematic Review

2020 ◽  
Author(s):  
Shaurya Taran ◽  
Wael Ahmed ◽  
Esther Bui ◽  
Lara Prisco ◽  
Cecil Hahn ◽  
...  
Keyword(s):  
Systematic Review ◽  
Acute Care ◽  
Training Programs ◽  
Data Extraction ◽  
Meta Analysis ◽  
Case Series ◽  
Primary Objective ◽  
Eeg Abnormalities ◽  
Wide Range ◽  
Structure Patterns

Abstract Background: Use of electroencephalography (EEG) is currently recommended by the American Clinical Neurophysiology Society for a wide range of indications, including diagnosis of nonconvulsive status epilepticus and evaluation of unexplained disorders of consciousness. Data interpretation usually occurs by expert personnel (e.g. epileptologists, neurophysiologists), with information relayed to the primary care team. However, data cannot always be read in time-sensitive fashion, leading to potential delays in EEG interpretation and patient management. Multiple training programs have recently been described to enable non-experts to rapidly interpret EEG at the bedside. A comprehensive review of these training programs, including the tools used, outcomes obtained, and potential pitfalls, is currently lacking. Therefore, the optimum training program and implementation strategy remain unknown. Methods: We will conduct a systematic review of descriptive studies, case series, cohort studies and randomized controlled trials assessing training programs for EEG interpretation by non-experts. Our primary objective is to comprehensively review educational programs in this domain and report their structure, patterns of implementation, limitations, and trainee feedback. Our secondary objective will be to compare the performance of non-experts for EEG interpretation with a gold standard (e.g. interpretation by a certified electroencephalographers). Studies will be limited to those performed in acute care settings in both adult and paediatric populations (intensive care unit, emergency department, or post-anesthesia care units). Comprehensive search strategies will be developed for MEDLINE, EMBASE, WoS, CINAHL, and CENTRAL to identify studies for review. The gray literature will be scanned for further eligible studies. Two reviewers will independently screen the search results to identify studies for inclusion. A standardized data extraction form will be used to collect important data from each study. If possible, we will attempt to meta-analyse the quantitative data. If heterogeneity between studies is too high, we will present meaningful quantitative comparisons of secondary outcomes as per the synthesis without meta-analysis (SWiM) reporting guidelines. Discussion: We will aim to summarize the current literature in this domain to understand the structure, patterns, and pitfalls of EEG training programs for non-experts. This review is undertaken with a view to inform future education designs, potentially enabling rapid detection of EEG abnormalities and timely intervention by the treating physician. PROSPERO registration: Submitted and undergoing review (URL currently unavailable).

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