VISION - an open-source software for automated multi-dimensional image analysis of cellular biophysics.

Environment-sensitive probes are frequently used in spectral/multi-channel microscopy to study alterations in cell homeostasis. However, the few open-source packages available for processing of spectral images are limited in scope. Here, we present VISION, a stand-alone software based on Python for spectral analysis with improved applicability. In addition to classical intensity-based analysis, our software can batch-process multidimensional images with an advanced single-cell segmentation capability and apply user-defined mathematical operations on spectra to calculate biophysical and metabolic parameters of single cells. VISION allows for 3D and temporal mapping of properties such as membrane fluidity and mitochondrial potential. We demonstrate the broad applicability of VISION by applying it to study the effect of various drugs on cellular biophysical properties; the correlation between membrane fluidity and mitochondrial potential; protein distribution in cell-cell contacts; and properties of nanodomains in cell-derived vesicles. Together with the code, we provide a graphical user interface for facile adoption.

Does facial growth pattern affect the perception of lower facial asymmetry?

OBJECTIVES: To investigate the influence of vertical facial type on esthetic perception of lower facial asymmetry as evaluated by orthodontists, dentists, and laypeople. MATERIALS AND METHODS: Three adult females were selected with normal growth patterns (NGP), vertical growth patterns (VGP), and horizontal growth patterns (HGP). Frontal photographs were made symmetric and digitally altered, rotating the lower facial third clockwise, ranging from 0° to 6° in 1° increments. A web-based survey was designed with 24 images (eight images for each model) in random order. Each image was rated using a scale ranging from 0 (unattractive) to 10 (the most attractive) by 75 orthodontists, 73 dentists, and 78 laypeople. Kruskal-Wallis test was used to determine whether differences among groups were significant. Pairwise comparisons were made with Mann-Whitney U test. The significance level was set at P = .05. RESULTS: In NGP, orthodontists and dentists could recognize slighter deviations (2°), while deviations in VGP and HGP under 3° were not recognized by all groups. Severe deviations (≥4°) were distinguished better in HGP by orthodontists and laypeople. In VGP and NGP, there was no significant difference over 4°. CONCLUSIONS: Growth pattern has a significant influence on perception of lower facial asymmetry. Less severe asymmetry can be detected better in NGP. In severe degrees, increments of asymmetry can be perceived more in HGP by orthodontists and laypeople.

High-Dimensional Overdispersed Generalized Factor Model With Application to Single-Cell Sequencing Data Analysis.

The current high-dimensional linear factor models fail to account for the different types of variables, while high-dimensional nonlinear factor models often overlook the overdispersion present in mixed-type data. However, overdispersion is prevalent in practical applications, particularly in fields like biomedical and genomics studies. To address this practical demand, we propose an overdispersed generalized factor model (OverGFM) for performing high-dimensional nonlinear factor analysis on overdispersed mixed-type data. Our approach incorporates an additional error term to capture the overdispersion that cannot be accounted for by factors alone. However, this introduces significant computational challenges due to the involvement of two high-dimensional latent random matrices in the nonlinear model. To overcome these challenges, we propose a novel variational EM algorithm that integrates Laplace and Taylor approximations. This algorithm provides iterative explicit solutions for the complex variational parameters and is proven to possess excellent convergence properties. We also develop a criterion based on the singular value ratio to determine the optimal number of factors. Numerical results demonstrate the effectiveness of this criterion. Through comprehensive simulation studies, we show that OverGFM outperforms state-of-the-art methods in terms of estimation accuracy and computational efficiency. Furthermore, we demonstrate the practical merit of our method through its application to two datasets from genomics. To facilitate its usage, we have integrated the implementation of OverGFM into the R package GFM.

A study on the impact of ultrasonic-stimulated clean fracturing fluid on the pore structure of medium to high rank coal.

The pore structure of coal plays a key role in the effectiveness of gas extraction. Conventional hydraulic fracturing techniques have limited success in modifying the pore structure using clean fracturing fluid (CFF), and the stimulating effects of ultrasonic can enhance the effectiveness of CFF in modifying coal pore structures. To research the effects of ultrasonic stimulation on the pore structure of medium to high-rank coal when using CFF, this study employed mercury intrusion porosimetry (MIP) and low-temperature nitrogen adsorption (LT-N2A) methods to analyze the changes in pore structures after cooperative modification. The results indicate that the pore volume and surface area of medium to high rank coal exhibit an increase and followed by a decrease with increasing Ro,max values, while the average pore diameter and permeability demonstrate a decrease and followed by an increase with Ro,max. Although there are some variations in the results of MIP and LT-N2A analysis for different pore size ranges, the overall findings suggest that ultrasonic stimulation in conjunction with CFF effectively alters the coal pore structure. The most significant improvement was observed in coking coal, where pore volume increased by 22%, pore area decreased by 11% and tortuosity decreased by 47%. The improvement of lean coal is the smallest, the pore volume increases by about 7%, and the surface area decreases by about 14%. It is found that the modification of coal pore volume is mainly concentrated in transition pores and macropores. These research outcomes provide valuable insights into the application of ultrasonic technology in coalbed gas extraction.

Experimental study on mechanical properties and breakage of high temperature carbon fiber-bar reinforced concrete under impact load.

To investigate the effects of high temperature and carbon fiber-bar reinforcement on the dynamic mechanical properties of concrete materials, a muffle furnace was used to treat two kinds of specimens, plain and carbon fiber-bar reinforced concrete, at high temperatures of 25, 200, 400 and 600 °C. Impact compression tests were carried out on two specimens after high-temperature exposure using a Hopkinson pressure bar (SHPB) test setup combined with a high-speed camera device to observe the crack extension process of the specimens. The effects of high temperature and carbon fiber-bar reinforcement on the peak stress, energy dissipation density, crack propagation and fractal dimension of the concrete were analyzed. The results showed that the corresponding peak strengths of the plain concrete specimens at 25, 200, 400, and 600 °C were 88.37, 93.21, 68.85, and 54.90 MPa, respectively, and the peak strengths after the high-temperature exposure first increased slightly and then decreased rapidly. The mean peak strengths corresponding to the carbon fiber-bar reinforced concrete specimens after high-temperature action at 25, 200, 400, and 600 °C are 1.13, 1.13, 1.21, and 1.19 times that of plain concrete, respectively, and the mean crushing energy consumption densities are 1.27, 1.31, 1.73, and 1.59 times that of plain concrete, respectively. The addition of carbon fiber-bar reinforcement significantly enhanced the impact resistance and energy dissipation of the concrete structure, and the higher the temperature was, the more significant the increase. An increase in temperature increases the number of crack extensions and width, and the high tensile strength of the carbon fiber-bar reinforcement and the synergistic effect with the concrete material reduce the degree of crack extension in the specimen. The fractal dimension of the concrete ranged from 1.92 to 2.68, that of the carbon fiber-bar reinforced concrete specimens ranged from 1.61 to 2.42, and the mean values of the corresponding fractal dimensions of the plain concrete specimens after high-temperature effects at 25, 200, 400, and 600 °C were 1.19, 1.21, 1.10, and 1.11 times those of the fiber-reinforced concrete specimens, respectively. The incorporation of carbon fiber-bar reinforcement reduces the degree of rupture and fragmentation of concrete under impact loading and improves the safety and stability of concrete structures.

Assessing the Prevalence and Length of the Anterior Loop of Inferior Alveolar Nerve and the Position and Dimension of Mental Foramen Using Cone Beam Computed Tomography.

Background To prevent harm to the neurovascular bundle during clinical and surgical operations, it is crucial to understand the position and dimension of the mental foramen, as well as the prevalence and length of the anterior loop (AL). Methods An iCAT Vision (CT Dent, London, UK) was used to take a cone beam computed tomography (CBCT) scan. Measurement of height, length of anterior loop, position and width of mental foramen was examined. Results Assessed prevalence of anterior loop and saw difference among genders, which was found to be more among males than females; anterior loop decreased as age advances. Mean anterior loop length of study subjects in different age group on right side was more than the left side, and the most common location of the mental foramen (61.87% in females and 61.36% in males) is located below the apex of the second premolar. The mean value of the distance from inferior border was 9.72mm in females and 10.78mm in males. Conclusion The current study was done with all of these characteristics in mind to assess the effectiveness of CBCT in determining anterior looping of the inferior alveolar nerve (IAN) and the position and dimension of mental foramen. In more than half of the cases analyzed, an anterior loop was discovered.

Tongue and jaw movement assessed by 3D motion capture during gum chewing.

Introduction: The tongue plays an important role in mastication, swallowing, and articulation, but it cannot be directly observed because of its location inside the oral cavity. This study aimed to clarify detailed 3D tongue movements during chewing using electromagnetic articulography (EMA). Materials and Methods: The participants were 10 healthy, young volunteers (average age 26.8 ± 2.1 years; 5 males, 5 females). Tongue and jaw movement during gum chewing was measured and recorded using EMA. Four EMA sensors were attached to the anterior, posterior, left, and right surfaces of the tongue, and one sensor was also attached to the mandibular left incisor. The tongue motion during the chewing cycle was spatially and sequentially analyzed based on the motion trajectories of the tongue and mandible. Results and Discussion: The tongue moved downward and to the masticatory side in a manner similar to the movement of the jaw. The anterior tongue marker moved downward to a greater extent than the other tongue markers. However, the tongue moved forward as the jaw moved backward. The anterior marker reached the most anterior position during the jaw-opening phase and the posterior markers reached the most anterior position during the jaw-closing phase. Just before maximum jaw-opening, all markers on the tongue reached the bottom lowest position. During the jaw-closing phase, the tongue reached the dominant farthest position in the masticatory side. All the markers reached the most posterior position during the occlusal phase. Conclusion: These findings demonstrate the sequence of tongue motion patterns during gum chewing.

Quantification of immiscible displacement in a rough-walled fracture through direct visualization.

Subsurface substance migration in the fractured rock aquifer is mainly controlled by fractures, and immiscible fluid-fluid displacement in fractures is important to many geophysical processes and engineering activities. Using a fracture-visualization system, we present the qualitative and quantitative assessment of fracture geometry associated with fluid movement and distribution in the rough fracture. Based on fracture geometry and statistical analysis, we first conducted a quantitative study of fracture surface roughness and aperture distribution. Then, fractal dimensions of displacement front and residual oil distribution were determined by image processing procedures. Influenced by wettability and micro-scale roughness, at the end of the displacement, residual oil saturation of molded sample is lower (6.45%-25.74%), and displacement pattern is more uniform, indicating that displacement effect is better. Due to smaller differences in residual oil saturation (9.08%) under different injection directions, the impact of wettability on the displacement process is greater than that of anisotropic roughness. Additionally, the fractal dimension of the displacement front increased under low injection rates initially but decreased when the rate was increased later. Overall, visualized temporal monitoring of experimental images enabled us to provide a preliminary assessment of the impact of anisotropic roughness and the material constituting the fracture wall on invading fluid saturation and the fractal dimension of the displacement front under various injection rates.

Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment.

Objective.We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing.Approach.Employing the unsupervised manifold learning algorithm, dynamic diffusion map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database.Main results.Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis.Significance.The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature.

Attention deficit hyperactivity disorder (ADHD) detection for IoT based EEG signal.

ADHD is a prevalent childhood behavioral problem. Early ADHD identification is essential towards addressing the disorder and minimizing its negative impact on school, career, relationships, as well as general well-being. The present ADHD diagnosis relies primarily on an emotional assessment which can be readily influenced by clinical expertise and lacks a basis of objective markers. In this paper, an innovative IoT based ADHD detection is proposed using an EEG signal. To the input EEG signal, the min-max normalization technique is processed. Features are extracted as the subsequent step, where improved fuzzy feature, in which the entropy is estimated to increase the effectiveness of recognizing the vector along with, fractal dimension, wavelet transform and non-linear features are extracted. Also, proposes the new hybrid PUDMO algorithm to select the optimal features from the extracted feature set. Subsequently, the selected features are fed to the proposed hybrid detection system that including IDBN and LSTM classifier to detect whether it is ADHD or not. Further, the weights of both classifiers are tuned optimally as per the hybrid PUDMO algorithm to enhance the detection performance. The PUDMO achieved an accuracy of 0.9649 in the best statistical metric, compared to the SLO's 0.8266, SOA's 0.8201, SMA's 0.8060, BRO's 0.8563, DE's 0.8083, POA's 0.8537, and DMOA's 0.8647, respectively. Thus, the assessments and detection help the clinicians to take appropriate decision.

Factors Influencing Treatment Preference in Patients with Diabetic Macular Edema: A Study Using Conjoint Analysis.

INTRODUCTION: Despite the wide range of treatment options available for diabetic macular edema (DME), adherence to treatment remains a barrier. Therefore, this study used conjoint analysis to examine the factors that patients with DME prioritize when choosing a course of treatment and investigated differences in quality of life and levels of disease self-management in patients with or without experience of anti-vascular endothelial growth factor (VEGF) treatment. METHODS: A cross-sectional study was conducted through an online survey in Japan between May 31, 2022, and June 30, 2022. Questionnaires were sent to 27,236 individuals registered in the diabetes panels, with experience of treatment for DME within the last 10 years. Conjoint analysis was employed to calculate the relative importance, i.e., degree of influence on patients' treatment choices, considering the trade-offs among five factors: cost per treatment, frequency of visits, anticipated post-treatment visual acuity, physician's explanation about disease and treatment, and frequency of treatment-related side effects. RESULTS: A total of 237 responses were used to assess the relative importance of cost per treatment, frequency of visits, anticipated post-treatment visual acuity, physician's explanation about the disease, treatment, and frequency of treatment-related side effects using conjoint analysis. The importance of each factor was anticipated post-treatment visual acuity at 30.0, frequency of treatment-related side effects at 25.5, treatment frequency at 17.7, cost per treatment at 16.5, and physician explanation about the disease and treatment at 10.4. The average EuroQoL 5 dimension 5 level index value in patients with and without anti-VEGF treatment experience was 0.785 and 0.825, respectively, with no major difference. CONCLUSIONS: Anticipated post-treatment visual acuity was identified as the most important factor in selecting a treatment regardless of the anti-vascular endothelial growth factor treatment experience demonstrating when patients with DME make treatment choices, anticipated post-treatment visual acuity is prioritized over treatment frequency and cost.

Integrating a multi-variable scenario with Attention-LSTM model to forecast long-term coastal beach erosion.

Beach erosion is an adverse impact of climate change and human development activities. Effective beach management necessitates integrating natural and anthropogenic factors to address future erosion trends, while most current prediction models focus only on natural factors, which may provide an incomplete and potentially inaccurate representation of erosion dynamics. This study enhances prediction methods by integrating both natural and anthropogenic factors, thereby enhancing the accuracy and reliability of erosion projections. By extracting historical shorelines through CoastSat model from 1986 to 2020, we develop multivariable scenarios with Attention-LSTM model to predict the regional impacts of natural and anthropogenic factors on erosion to sandy beaches along the typical shoreline of Shenzhen in China. Results reveal that Shenzhen's beaches experienced erosion up to 12 m over the past 35 years. Here we project a decrease in the mean erosion rate of the beaches, identifying population growth (21.0 %) as the main controlling factor before the mid-century in a range of scenarios. We find that Attention-LSTM multi-model ensemble approach can provide overall improved accuracy and reliability over a wide range of beach erosion compared to scenario prediction model of Attention-LSTM and statistical model of Digital Shoreline Analysis System (DSAS), yielding an average uncertainty of 10.99 compared to 13.29. These insights reveal policies to safeguard beaches because of the rising demand for beaches due to human factors, coupled with decreased impervious surfaces through ecological conservation, lead to mitigation for beach erosion. Accurate forecasts empower policymakers to implement effective coastal management strategies, safeguard resources, and mitigate erosion's adverse effects. Our study offers finely-tuned predictions of coastal erosion, providing crucial insights for future coastal conservation efforts and climate change adaptation along the shoreline, and serving as a foundation for further research aimed at understanding the evolving environmental impacts of beach erosion in Shenzhen.

Robust unsupervised texture segmentation for motion analysis in ultrasound images.

PURPOSE: Ultrasound imaging has emerged as a promising cost-effective and portable non-irradiant modality for the diagnosis and follow-up of diseases. Motion analysis can be performed by segmenting anatomical structures of interest before tracking them over time. However, doing so in a robust way is challenging as ultrasound images often display a low contrast and blurry boundaries. METHODS: In this paper, a robust descriptor inspired from the fractal dimension is presented to locally characterize the gray-level variations of an image. This descriptor is an adaptive grid pattern whose scale locally varies as the gray-level variations of the image. Robust features are then located based on the gray-level variations, which are more likely to be consistently tracked over time despite the presence of noise. RESULTS: The method was validated on three datasets: segmentation of the left ventricle on simulated echocardiography (Dice coefficient, DC), accuracy of diaphragm motion tracking for healthy subjects (mean sum of distances, MSD) and for a scoliosis patient (root mean square error, RMSE). Results show that the method segments the left ventricle accurately ( DC = 0.84 ) and robustly tracks the diaphragm motion for healthy subjects ( MSD = 1.10 mm) and for the scoliosis patient ( RMSE = 1.22 mm). CONCLUSIONS: This method has the potential to segment structures of interest according to their texture in an unsupervised fashion, as well as to help analyze the deformation of tissues. Possible applications are not limited to US image. The same principle could also be applied to other medical imaging modalities such as MRI or CT scans.

Joint modeling of an outcome variable and integrated omics datasets using GLM-PO2PLS.

In many studies of human diseases, multiple omics datasets are measured. Typically, these omics datasets are studied one by one with the disease, thus the relationship between omics is overlooked. Modeling the joint part of multiple omics and its association to the outcome disease will provide insights into the complex molecular base of the disease. Several dimension reduction methods which jointly model multiple omics and two-stage approaches that model the omics and outcome in separate steps are available. Holistic one-stage models for both omics and outcome are lacking. In this article, we propose a novel one-stage method that jointly models an outcome variable with omics. We establish the model identifiability and develop EM algorithms to obtain maximum likelihood estimators of the parameters for normally and Bernoulli distributed outcomes. Test statistics are proposed to infer the association between the outcome and omics, and their asymptotic distributions are derived. Extensive simulation studies are conducted to evaluate the proposed model. The method is illustrated by modeling Down syndrome as outcome and methylation and glycomics as omics datasets. Here we show that our model provides more insight by jointly considering methylation and glycomics.

The "Smile Design and Space" Concept for Altering Vertical Dimension of Occlusion and Esthetic Restorative Material Selection.

OBJECTIVE: This article introduces the smile design and space (SDS) concept for managing the vertical dimension of occlusion (VDO) in full-mouth reconstructions and its influence on the selection of esthetic restorative materials. Limitations of traditional VDO adjustment methods are being addressed through a facially driven treatment approach that enhances esthetics improves function and minimizes invasiveness. CLINICAL CONSIDERATIONS: The SDS concept involves detailed evaluations of the incisal edges of the maxillary central incisors in relation to the upper lip at rest and at full smile. This includes analyzing variations in incisal exposure due to differing lip mobility, which can significantly impact gingival esthetics, particularly in cases with gingival exposure during smiling. The concept employs a strategic formula to determine the necessary alterations in VDO based on specific dental relationships and wear patterns. CONCLUSIONS: The SDS concept provides guidelines for full-mouth reconstructions, emphasizing minimal invasiveness and the integration of esthetics with functional dynamics to enhance the predictability of clinical outcomes and reduce complications associated with traditional restoration techniques. CLINICAL SIGNIFICANCE: The SDS concept enhances the precision of full-mouth reconstructions through individualized adjustment of the VDO, tailored to the esthetic and functional requirements of each patient.

Dimension compensation of printed master molds by a desktop LCD 3D printer for high-precision microfluidic applications.

Recent advances in low-cost liquid crystal display (LCD) 3D printing have popularized its use in creating microfluidic master molds and complete devices. However, the quality and precision of these fabrications often fall short of the rigorous standards required for advanced microfluidic applications. This study introduces a novel approach to enhance the dimensional accuracy of microchannels produced using a desktop LCD 3D printer. We propose a method for dimension compensation, optimize the printing parameters, and provide a straightforward post-treatment technique to ensure high-quality curing of polydimethylsiloxane (PDMS) in master molds made from photosensitive resin. Our investigation assesses the precision of 3D printing across three different scales of square cross-section microchannels by measuring their widths and heights, leading to the determination of optimal printing parameters that minimize dimensional errors. The dimensional errors are further reduced by introducing a series of dimension compensation factors, which correct the nominal dimensions of the microchannels by using the compensation factors in 3D printing. The dimensional accuracy is significantly improved after compensation even in fabricating complex microchannels of triangular cross-sections. Finally, a spiral channel of trapezoidal-like cross-section with tilted edges is fabricated for microfluidic application, and highly efficient particle separation is realized in the channel. The proposed method provides new insights for utilizing desktop LCD 3D printers to achieve high-accuracy microstructures necessary for advanced microfluidic applications.

Effects of thinning on structural complexity of Larix olgensis plantation.

In this paper, we collected the individual tree point cloud data in the plots of Larix olgensis plantations with different thinning intensities in Mengjiagang Forest Farm, applied the fractal analysis theory to extract box dimensions (Db) on MATLAB platform, and characterized the structural complexity of L. olgensis. We assessed the effect of different thinning intensities and tree attributes on the structural complexity of L. olgensis. The results showed significant differences in L. olgensis Db between control (CK: 1.68±0.07), low and medium intensity thinning (T1, T2, T3: 1.74±0.07), and high intensity thinning (T4: 1.81±0.06), which indicated that the thinning intensity increased tree structural complexity. For trunk attribute, the diameter at breast height and tree height was significantly positively correlated with Db, while the height-to-diameter ratio was significantly negatively correlated with Db. For canopy attribute, crown volume, surface area, projected area, and crown diameter was significantly positively correlated with Db. Hegyi competition index was significantly negatively correlated with Db in the control and low-moderate-intensity thinning treatments, but not significantly correlated with Db in the high-intensity thinning treatment. It indicated that thinning influenced L. olgensis structural complexity, with trunk attribute and canopy attribute as the main drivers of L. olgensis structural complexity.

Analysis of human ambulation as a chaotic time-series: with nonlinear dynamics tools.

The aim of the present study is to investigate the complexity and stability of human ambulation and the implications on robotic prostheses control systems. Fourteen healthy individuals participate in two experiments, the first group run at three different speeds. The second group ascended and descended stairs of a five-level building block at a self-selected speed. All participants completed the experiment with seven inertial measurement units wrapped around the lower body segments and waist. The data were analyzed to determine the fractal dimension, spectral entropy, and the Lyapunov exponent (LyE). Two methods were used to calculate the long-term LyE, first LyE calculated using the full size of data sets. And the embedding dimensions were calculated using Average Mutual Information (AMI) and the False Nearest Neighbor (FNN) algorithm was used to find the time delay. Besides, a second approach was developed to find long-term LyE where the time delay was based on the average period of the gait cycle using adaptive event-based window. The average values of spectral entropy are 0.538 and 0.575 for stairs ambulation and running, respectively. The degree of uncertainty and complexity increases with the ambulation speed. The short term LyEs for tibia orientation have the minimum range of variation when it comes to stairs ascent and descent. Using two-way analysis of variance we demonstrated the effect of the ambulation speed and type of ambulation on spectral entropy. Moreover, it was shown that the fractal dimension only changed significantly with ambulation speed.

Fiscal expenditure efficiency of China's coal to clean heating policy.

Government subsidies are the backbone of the large-scale Coal to Clean Heating Policy launched in China in 2017. We analyze data from 27 pilot cities from 2017 to 2019. We use a slacks-based measure model with undesirable outputs to evaluate the optimal design of central and local government subsidies under the policy. While the overall efficiency is high, provincial capitals and cities with lower economic development levels show less efficiency. Our findings suggest that distributing central government subsidies based solely on administrative levels is not the best way to achieve perfect efficiency. Additionally, perfect efficiency at the city level may take years to achieve. A sweeping phase-out of government subsidies could lead to undesirable policy outcomes. Content analysis of normative documents from the six best-performing cities and six least efficient cities indicates that better-formulated legal and policy documents may contribute to higher efficiency. As lessons for designing and evaluating large-scale energy policies, we recommend considering 17 factors that could impact subsidy efficiency. We also suggest implementing a progressive phase-out schedule as a measure of policy efficiency and identifying factors that could ensure the long-term success of nationally implemented government subsidies.

Effects of beta-blockers on quality of life and well-being in patients with myocardial infarction and preserved left ventricular function-a prespecified substudy from REDUCE-AMI.

AIMS: In the Randomized Evaluation of Decreased Usage of Beta-Blockers after Acute Myocardial Infarction (REDUCE-AMI) study, long-term beta-blocker use in patients after acute myocardial infarction (AMI) with preserved left ventricular ejection fraction demonstrated no effect on death or cardiovascular outcomes. The aim of this prespecified substudy was to investigate effects of beta-blockers on self-reported quality of life and well-being. METHODS AND RESULTS: From this parallel-group, open-label, registry-based randomized clinical trial, EQ-5D, and World Health Organization well-being index-5 (WHO-5) questionnaires were obtained at 6-10 weeks and 11-13 months after AMI in 4080 and 806 patients, respectively. We report results from intention-to-treat and on-treatment analyses for the overall population and relevant subgroups using Wilcoxon rank sum test and adjusted ordinal regression analyses. Of the 4080 individuals reporting EQ-5D (median age 64 years, 22% female), 2023 were randomized to beta-blockers. The main outcome, median EQ-5D index score, was 0.94 [interquartile range (IQR) 0.88, 0.97] in the beta-blocker group, and 0.94 (IQR 0.88, 0.97) in the no-beta-blocker group 6-10 weeks after AMI, OR 1.00 [95% CI 0.89-1.13; P > 0.9]. After 11-13 months, results remained unchanged. Findings were robust in on-treatment analyses and across relevant subgroups. Secondary outcomes, EQ-VAS and WHO-5 index score, confirmed these results. CONCLUSION: Among patients after AMI with preserved left ventricular ejection fraction, self-reported quality of life and well-being was not significantly different in individuals randomized to routine long-term beta-blocker therapy as compared to individuals with no beta-blocker use. These results appear consistent regardless of adherence to randomized treatment and across subgroups which emphasizes the need for a careful individual risk-benefit evaluation prior to initiation of beta-blocker treatment.