The effects of small-scale impoundments and bank reinforcing on fish habitat and composition in semi-natural streams

We studied the fish assemblages of thirty one, 2(nd)-4(th) order "least- impacted" streams with a varying degree of low-level management in central Portugal, using a standardised survey to document the river habitat. Channel, banks and riparian landuse, described separately according to principal component scores, were significantly related to altitude, slope and management intervention. Species diversity was low, represented by four endemic, four pan-European and one exotic species. TWINSPAN classification distinguished 3 community types, characterised by their dominant species: trout (Salmo trutta L.), chub (Leuciscus carolitertii Doadrio) and "roach" (Squalius alburnoides Steindachner and Chondrostoma oligolepis Robalo). Community types were associated with environmental differences with PC Channel scores higher at trout sites compared to other classification groups, whilst PC Bank-1 scores, temperature and conductivity were significantly different at trout compared to "roach" sites. Ecologically important habitat features were, in turn, related to landscape (map-derived) parameters and the extent of channel and bank management. The mis-classification of sites in discriminant analysis was related to management intervention, indicating the potential difficulty in the assignment river-community types for the biological monitoring of fish communities in these stream types.FCT/MCTE

Mathematics education and technology

Recent international surveys such as the Organisation for Economic Co-operation and Development (OECD) report Students, Computers and Learning ( 2015) highlight the wide gap in students’ access to, and use of, technology in secondary mathematics in participating countries. The OECD “snapshot” methodology in which 15-year-old students were asked if they (or their teachers) had performed a range of mathematical tasks using computers in the month preceding their completion of the Programme for International Student Assessment (PISA) survey revealed low levels of technology use (see Fig. 1)

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Four Reasons to Question the Accuracy of a Biotic Index; the Risk of Metric Bias and the Scope to Improve Accuracy

<div><p>Natural ecological variability and analytical design can bias the derived value of a biotic index through the variable influence of indicator body-size, abundance, richness, and ascribed tolerance scores. Descriptive statistics highlight this risk for 26 aquatic indicator systems; detailed analysis is provided for contrasting weighted-average indices applying the example of the BMWP, which has the best supporting data. Differences in body size between taxa from respective tolerance classes is a common feature of indicator systems; in some it represents a trend ranging from comparatively small pollution tolerant to larger intolerant organisms. Under this scenario, the propensity to collect a greater proportion of smaller organisms is associated with negative bias however, positive bias may occur when equipment (e.g. mesh-size) selectively samples larger organisms. Biotic indices are often derived from systems where indicator taxa are unevenly distributed along the gradient of tolerance classes. Such skews in indicator richness can distort index values in the direction of taxonomically rich indicator classes with the subsequent degree of bias related to the treatment of abundance data. The misclassification of indicator taxa causes bias that varies with the magnitude of the misclassification, the relative abundance of misclassified taxa and the treatment of abundance data. These artifacts of assessment design can compromise the ability to monitor biological quality. The statistical treatment of abundance data and the manipulation of indicator assignment and class richness can be used to improve index accuracy. While advances in methods of data collection (i.e. DNA barcoding) may facilitate improvement, the scope to reduce systematic bias is ultimately limited to a strategy of optimal compromise. The shortfall in accuracy must be addressed by statistical pragmatism. At any particular site, the net bias is a probabilistic function of the sample data, resulting in an error variance around an average deviation. Following standardized protocols and assigning precise reference conditions, the error variance of their comparative ratio (test-site:reference) can be measured and used to estimate the accuracy of the resultant assessment.</p></div

The unimodal probability distribution applied to indicator selection in simulation models illustrating two generalized examples.

<p>a). A symmetric distribution ranging across 7 indicator classes that occurs for mid-range scores defined by probabilities = 0.05,0.10,0.20,0.30,0.2,0.1,0.05. b). A truncated distribution that occurs for end-group scores, in this case associated with a mode of 10 where the “missing” probabilities, totaling 0.35 (i.e., the right-hand probabilities = 0.20,0.10,0.05, respectively corresponding to the non-defined indicator ranks of 11,12,13) are divided in proportion of the indicator ranks present (giving truncation-adjusted probabilities = 0.8,0.15,0.31,0.46).</p

Interaction between the natural world and scientific strategy combine to define the derived value of a biotic index.

<p>Interaction between the natural world and scientific strategy combine to define the derived value of a biotic index.</p

A perfect correlation between indicator size and indicator score causes a systematic bias in index values that varies with the treatment of abundance data (see inset).

<p>(a) overall negative bias over the range of indicator scores: (b) antagonistic interaction associated with the failure to collect the smallest three size-indicator classes (indicator mode = 4); (c) synergistic interaction associated with the decimation of the three largest size-indicator classes (indicator mode = 7).</p

Truncated frequency distributions at the extremes of the range of indicator scores causes a positive (lower end) and negative (upper end) bias in the derived value for an idealized biotic index, resulting in an overall compression of the index range.

<p>Truncated frequency distributions at the extremes of the range of indicator scores causes a positive (lower end) and negative (upper end) bias in the derived value for an idealized biotic index, resulting in an overall compression of the index range.</p

Simulation results illustrating the interaction between skewed indicator richness and the statistical treatment of abundance data (see inset) along the scoring range of the BMWP.

<p>Simulation results illustrating the interaction between skewed indicator richness and the statistical treatment of abundance data (see inset) along the scoring range of the BMWP.</p

Bias (y-axis) in the derived index value associated with increasingly complex scenarios in BMWP-based indices as the mode ranges from 1 to 10 (x-axis), reflecting.

<p>(a) the misclassification of Chironomidae and Oligochaeta when they contribute 25% of individuals; (b) multiple misclassified taxa defined by Walley and Hawkes [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158383#pone.0158383.ref032" target="_blank">32</a>]; (c) Net bias associated with range compression, taxonomic skew and misclassified taxa.</p