Parents' Emotion‐Related Beliefs, Behaviours, and Skills Predict Children's Recognition of Emotion

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110629/1/icd1868.pd

Colorado Native Plant Society Newsletter, Vol. 4 No. 4, July-August 1980

https://epublications.regis.edu/aquilegia/1154/thumbnail.jp

Archaeobotany in Australia and New Guinea: practice, potential and prospects

Archaeobotany is the study of plant remains from archaeological contexts. Despite Australasian research being at the forefront of several methodological innovations over the last three decades, archaebotany is now a relatively peripheral concern to most archaeological projects in Australia and New Guinea. In this paper, many practicing archaeobotanists working in these regions argue for a more central role for archaeobotany in standard archaeological practice. An overview of archaeobotanical techniques and applications is presented, the potential for archaeobotany to address key historical research questions is indicated, and initiatives designed to promote archaeobotany and improve current practices are outlined

Archaeobotany in Australia and New Guinea: practice, potential and prospects

Archaeobotany is the study of plant remains from archaeological contexts. Despite Australasian research being at the forefront of several methodological innovations over the last three decades, archaebotany is now a relatively peripheral concern to most archaeological projects in Australia and New Guinea. In this paper, many practicing archaeobotanists working in these regions argue for a more central role for archaeobotany in standard archaeological practice. An overview of archaeobotanical techniques and applications is presented, the potential for archaeobotany to address key historical research questions is indicated, and initiatives designed to promote archaeobotany and improve current practices are outlined

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

FAST NONLINEAR DETERMINISTIC FORECASTING OF SEGMENTED STOCK INDICES USING PATTERN MATCHING AND EMBEDDING TECHNIQUES.

We perform out-of-sample predictions on a set of stock indices represented in a piecewise linear manner. An automated segmentation algorithm converges to an optimum segmented time series representation, which achieves considerable data compression and allows variable sampling rate of the time series depending on different segments having different length. Then, we propose a practical method to determine the minimum embedding dimension from the segmented time series. The novelty of this approach is that it is applied on segmented representations and that it returns the minimum embedding dimension measured in number of segments. It also has the following advantages: (1) does not contain subjective parameters; (2) works with any number of segments; (3) can detect deterministic time series; (4) is computationally efficient. We use the minimum embedding dimension as an indicator of the length of patterns that can be retrieved from the time series own past using our pattern matching technique. This technique enables the matching of historical patterns of similar shape which occur in different time scales. To define an appropriate similarity measure, we introduce the notation of Multiple Feature Sets (MFS) which employ Dynamic Time Warping (DTW) and first derivative and temporal features. An additional advantage of the system we propose is that the segmented representation scheme and the prediction model are both data driven and that the predictions are made using information only from the time-series own past without any a priori knowledge being injected into the model. We demonstrate that this approach may offer a useful decision support tool for stock market trading.