IMECE2002-33598 CONFIGURATION-SPACE SEARCHING AND OPTIMIZING TOOL ORIENTATIONS FOR 5-AXIS MACHINING

ABSTRACT This paper presents a methodology and algorithms of optimizing and smoothing the tool orientation control for 5-axis sculptured surface machining. A searching method in the machining configuration space (C-space) is proposed to find the optimal tool orientation by considering the local gouging, rear gouging and global tool collision in machining. Based on the machined surface error analysis, a boundary search method is developed first to find a set of feasible tool orientations in the Cspace to eliminate gouging and collision. By using the minimum cusp height as the objective function, we first determine the locally optimal tool orientation in the C-space to minimize the machined surface error. Considering the adjacent part geometry and the alternative feasible tool orientations in the C-space, tool orientations are then globally optimized and smoothed to minimize the dramatic change of tool orientation during machining. The developed method can be used to automate the planning and programming of tool path generation for high performance 5-axis sculptured surface machining. Computer implementation and examples are also provided in the paper

sTarPicker: A Method for Efficient Prediction of Bacterial sRNA Targets Based on a Two-Step Model for Hybridization

Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/

Matching-Adjusted Indirect Comparison of Crisaborole Ointment 2% vs. Topical Calcineurin Inhibitors in the Treatment of Patients with Mild-to-Moderate Atopic Dermatitis

INTRODUCTION: Crisaborole topical ointment, 2%, is a nonsteroidal, topical anti-inflammatory phosphodiesterase-4 (PDE4) inhibitor that is approved for the treatment of mild-to-moderate atopic dermatitis (AD). The objective of the current analysis was to compare the efficacy of crisaborole 2% relative to pimecrolimus 1%, tacrolimus 0.03% and tacrolimus 0.1% in patients aged ≥ 2 years with mild-to-moderate AD by comparing improvement in Investigator’s Static Global Assessment scores ( (ISGA scores of 0/1 indicating “clear or almost clear”). ISGA was selected as the primary efficacy outcome given the US Food and Drug Administration’s recommendations on the use of ISGA for assessment of global severity in AD and to align with efficacy measurements in the crisaborole registration trials. Safety endpoints could not be analyzed due to differences in outcome definitions across studies. METHODS: Efficacy of crisaborole was evaluated using individual patient data (IPD) from two pivotal phase III randomized controlled trials (RCTs), and efficacy of comparators was evaluated using published RCTs included in a previous network meta-analysis. Vehicle controls were not comparable due to differences in ingredients and population imbalance and, therefore, an unanchored matching-adjusted indirect comparison (MAIC) was used, which reweighted IPD for crisaborole to estimate absolute response in comparator populations. RESULTS: The odds of achieving an improvement in ISGA score was higher with crisaborole 2% versus pimecrolimus 1% (odds ratio [OR] 2.03; 95% confidence interval [CI] 1.45–2.85; effective sample size =  627, reduced from 1021; p value < 0.001) and for crisaborole 2% versus tacrolimus 0.03% (OR 1.50; 95% CI 1.09–2.05; effective sample size = 311, reduced from 1021; p = 0.012). CONCLUSION: The unanchored MAIC suggests that the odds of achieving an improvement in ISGA score is greater with crisaborole 2% than with pimecrolimus 1% or tacrolimus 0.03% in patients aged ≥ 2 years with mild-to-moderate AD. These results are consistent with findings from the previously published network meta-analysis, which used a different methodology for performing indirect treatment comparisons. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13555-021-00646-1

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (S ( I ), THI, and SHI, where S ( I ) is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (S ( I ),THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services

Re-conceptualising the link between research and practice in social work: a literature review on knowledge utilisation

Despite the recent movement towards greater research use in many areas of social work, criticisms persist that decision making in practice is seldom informed by sound research evidence. Discourse about the research-to-practice gap in social work has tended to focus on the feasibility of evidence-based practice for the profession, but has rarely drawn from the broader knowledge utilisation literature. There are important understandings to be gained from the knowledge utilisation field, which spans more than six decades of interdisciplinary research.This article introduces the wider knowledge utilisation literature to a social work audience. It considers the potential of this body of literature to facilitate research use in social work, as well as conceptual issues that may be hindering it from informing improvements to research utilisation in practice

Brain-wide neural co-activations in resting human

Spontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of < 0.1 Hz. However, understanding of fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and transitional characteristics remain limited due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain functions. Using the state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and transitional functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6 Hz, 5 Hz, and 10 Hz) were embedded in the nonstationary dynamics of these functional states. We further identified a superstructure that regulated between-state immediate and long-range transitions involving the entire set of identified cCAPs and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich dynamic structures of brain-wide human neural activations.Financial support was provided by the University of Oklahoma Libraries’ Open Access Fund.Ye