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Background Systematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools. Implementation Meta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, request a conversion feature, about page, articles page, and privacy policy page. This comprehensive design supports the tool’s primary goal of simplifying the meta-analysis process. Results Since its initial release in October 2023 as Meta Converter and subsequent renaming to Meta-Analysis Accelerator, the tool has gained widespread use globally. From March 2024 to May 2024, it received 12,236 visits from countries such as Egypt, France, Indonesia, and the USA, indicating its international appeal and utility. Approximately 46% of the visits were direct, reflecting its popularity and trust among users. Conclusions Meta-Analysis Accelerator significantly enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. Its comprehensive variety of conversions, user-friendly interface, and continuous improvements make it an indispensable resource for researchers. The tool’s ability to streamline data transformation ensures that researchers can focus more on data interpretation and less on manual calculations, thus advancing the quality and ease of conducting systematic reviews and meta-analyses.

Whispers are a natural vocal communication mechanism, in which vocal cords do not vibrate normally. Lack of glottal-induced pitch leads to low energy, and an inherent noise-like spectral distribution reduces intelligibility. Much research has been devoted to processing of whispers, including conversion of whispers to speech. Unfortunately, among several approaches, the best reconstructed speech to date still contains obviously artificial muffles and suffers from an unnatural prosody. To address these issues, the novel use of multiple restricted Boltzmann machines (RBMs) is reported as a statistical conversion model between whisper and speech spectral envelopes. Moreover, the accuracy of estimated pitch is improved using machine learning techniques for pitch estimation within only voiced (V) regions. Both objective and subjective evaluations show that this new method improves the quality of whisper-reconstructed speech compared with the state-of-the-art approaches.

To evaluate the neural correlates of implicit processing of negative emotions in motor conversion disorder (CD) patients. An event related fMRI task was completed by 12 motor CD patients and 14 matched healthy controls using standardised stimuli of faces with fearful and sad emotional expressions in comparison to faces with neutral expressions. Temporal changes in the sensitivity to stimuli were also modelled and tested in the two groups. We found increased amygdala activation to negative emotions in CD compared to healthy controls in region of interest analyses, which persisted over time consistent with previous findings using emotional paradigms. Furthermore during whole brain analyses we found significantly increased activation in CD patients in areas involved in the 'freeze response' to fear (periaqueductal grey matter), and areas involved in self-awareness and motor control (cingulate gyrus and supplementary motor area). In contrast to healthy controls, CD patients exhibited increased response amplitude to fearful stimuli over time, suggesting abnormal emotional regulation (failure of habituation / sensitization). Patients with CD also activated midbrain and frontal structures that could reflect an abnormal behavioral-motor response to negative including threatening stimuli. This suggests a mechanism linking emotions to motor dysfunction in CD.

The novelty of this study consists in the formulation and characterization of three experimental dental composites (PM, P14M, P2S) for cervical dental lesion restoration compared to the commercial composites Enamel plus HRi® - En (Micerium S.p.A, Avengo, Ge, Italy), G-ænial Anterior® - Ge, (GC Europe N.V., Leuven, Belgium), Charisma® - Ch (Heraeus Kulzer, Berkshire, UK). The physio-chemical properties were studied, like the degree of conversion and the residual monomers in cured samples using FTIR-ATR (attenuated total reflectance) and HPLC-UV (ultraviolet detection), as well as the evaluation of the mechanical properties of the materials. The null hypothesis was that there would be no differences between experimental and commercial resin composites regarding the evaluated parameters. Statistical analysis revealed that water and saliva storage induced significant modifications of all mechanical parameters after three months for all tested materials, except for a few comparisons for each type of material. Storage medium seemed not to alter the values of mechanical parameters in comparison with the initial ones for: diametral tensile strength (DTS-saliva for Ge and PM, compressive strength (CS)-water for Ch, DTS-water and Young’s modulus YM-saliva for P14M and YM-water/ saliva for P2S (p > 0.05). Two of the experimental materials showed less than 1% residual monomers, which sustains good polymerization efficiency. Experimental resin composites have good mechanical properties, which makes them recommendable for the successful use in load-bearing surfaces of posterior teeth.

Global agriculture production is challenged by increasing demands from rising population and a changing climate, which may be alleviated through development of genetically improved crop cultivars. Research into increasing photosynthetic energy conversion efficiency has proposed many strategies to improve production but have yet to yield real-world solutions, largely because of a phenotyping bottleneck. Partial least squares regression (PLSR) is a statistical technique that is increasingly used to relate hyperspectral reflectance to key photosynthetic capacities associated with carbon uptake (maximum carboxylation rate of Rubisco, ) and conversion of light energy (maximum electron transport rate supporting RuBP regeneration, ) to alleviate this bottleneck. However, its performance varies significantly across different plant species, regions, and growth environments. Thus, to cope with the heterogeneous performances of PLSR, this study aims to develop a new approach to estimate photosynthetic capacities. A framework was developed that combines six machine learning algorithms, including artificial neural network (ANN), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), random forest (RF), Gaussian process (GP), and PLSR to optimize high-throughput analysis of the two photosynthetic variables. Six tobacco genotypes, including both transgenic and wild-type lines, with a range of photosynthetic capacities were used to test the framework. Leaf reflectance spectra were measured from 400 to 2500 nm using a high-spectral-resolution spectroradiometer. Corresponding photosynthesis vs. intercellular CO concentration response curves were measured for each leaf using a leaf gas-exchange system. Results suggested that the mean value of the six regression techniques for predicting ( ) ranged from 0.60 (0.45) to 0.65 (0.56) with the mean value varying from 47.1 (40.1) to 54.0 (44.7) μmol m s . Regression stacking for ( ) performed better than the individual regression techniques with increases in of 0.1 (0.08) and decreases in by 4.1 (6.6) μmol m s , equal to 8% (15%) reduction in . Better predictive performance of the regression stacking is likely attributed to the varying coefficients (or weights) in the level-2 model (the LASSO model) and the diverse ability of each individual regression technique to utilize spectral information for the best modeling performance. Further refinements can be made to apply this stacked regression technique to other plant phenotypic traits.

In recent years, scavenging vibration energy from changeable excitations for flexible self-powering of microelectronic devices has become an emerging benchmark for energy reuse. Multi-stable nonlinear energy sink (MNES) has received extensive attention due to their simple structure, small mass, high efficiency and wide effective frequency bandwidth. Vibration energy harvester (VEH) and MNES have the common characteristic of redistributing vibration energy, therefore, a new energy strategy of merging the two is expected to achieve efficient vibration suppression and energy conversion simultaneously. In this work, a novel bionic-dipteran magnetoelastic bi-stable dynamic vibration absorber (BDMB-DVA) with active adjustable stiffness is proposed. Considering the continuous harmonic vibration as the input energy, the energy conversion and targeted energy transfer (TET) performance of the BDMB-DVA system are evaluated theoretically. The energy conversion efficiency of the BDMB-DVA under harmonic excitation is investigated; Moreover, the vibration suppression performance of the BDMB-DVA under swept excitations is investigated. Finally, experimental verification is conducted to understand the practical TET and energy conversion performance. It is obviously found that the proposed BDMB-DVA with very small mass ratio achieves broadband TET and energy conversion performance under weak ambient vibrations. Efforts of archetypal experimental verification are implemented to demonstrate the state-of-the-art energy transfer and conversion performance of the BDMB-DVA. This integrated energy conversion and vibration control strategy is a potential alternative for intelligent structural health monitoring and sound broadband vibration control of state-of-the-art manufacturing equipment under multisource vibration disturbance.

The thermal stability of organic solar cells is critical for practical applications of this emerging technology. Thus, effective approaches and strategies need to be found to alleviate their inherent thermal instability. Here, we show a polymer acceptor-doping general strategy and report a thermally stable bulk heterojunction photovoltaic system, which exhibits an improved power conversion efficiency of 15.10%. Supported by statistical analyses of device degradation data, and morphological characteristics and physical mechanisms study, this polymer-doping blend shows a longer lifetime, nearly keeping its efficiency ( t = 800 h) under accelerated aging tests at 150 o C. Further analysis of the degradation behaviors indicates a bright future of this system in outer space applications. Notably, the use of polymer acceptor as a dual function additive in the other four photovoltaic systems was also confirmed, demonstrating the good generality of this polymer-doping strategy. Thermal instability is a critical bottleneck for bulk heterojunction organic solar cells. Here Yang et al. use barely 1 wt% of a polymer acceptor as an additive to simultaneously improve the device efficiency and thermal stability of several state-of-the-art organic photovoltaic systems at high temperatures.

In this study, uncoupled and coupled ocean–atmosphere simulations are carried out for the California Upwelling System to assess the dynamic ocean–atmosphere interactions, namely, the ocean surface current feedback to the atmosphere. The authors show the current feedback, by modulating the energy transfer from the atmosphere to the ocean, controls the oceanic eddy kinetic energy (EKE). For the first time, it is demonstrated that the current feedback has an effect on the surface stress and a counteracting effect on the wind itself. The current feedback acts as an oceanic eddy killer, reducing by half the surface EKE, and by 27% the depth-integrated EKE. On one hand, it reduces the coastal generation of eddies by weakening the surface stress and hence the nearshore supply of positive wind work (i.e., the work done by the wind on the ocean). On the other hand, by inducing a surface stress curl opposite to the current vorticity, it deflects energy from the geostrophic current into the atmosphere and dampens eddies. The wind response counteracts the surface stress response. It partly reenergizes the ocean in the coastal region and decreases the offshore return of energy to the atmosphere. Eddy statistics confirm the current feedback dampens the eddies and reduces their lifetime, improving the realism of the simulation. Finally, the authors propose an additional energy element in the Lorenz diagram of energy conversion: namely, the current-induced transfer of energy from the ocean to the atmosphere at the eddy scale.

Blood pressure lowering is an established strategy for preventing microvascular and macrovascular complications of diabetes, but its role in the prevention of diabetes itself is unclear. We aimed to examine this question using individual participant data from major randomised controlled trials. We performed a one-stage individual participant data meta-analysis, in which data were pooled to investigate the effect of blood pressure lowering per se on the risk of new-onset type 2 diabetes. An individual participant data network meta-analysis was used to investigate the differential effects of five major classes of antihypertensive drugs on the risk of new-onset type 2 diabetes. Overall, data from 22 studies conducted between 1973 and 2008, were obtained by the Blood Pressure Lowering Treatment Trialists’ Collaboration (Oxford University, Oxford, UK). We included all primary and secondary prevention trials that used a specific class or classes of antihypertensive drugs versus placebo or other classes of blood pressure lowering medications that had at least 1000 persons-years of follow-up in each randomly allocated arm. Participants with a known diagnosis of diabetes at baseline and trials conducted in patients with prevalent diabetes were excluded. For the one-stage individual participant data meta-analysis we used stratified Cox proportional hazards model and for the individual participant data network meta-analysis we used logistic regression models to calculate the relative risk (RR) for drug class comparisons. 145 939 participants (88 500 [60·6%] men and 57 429 [39·4%] women) from 19 randomised controlled trials were included in the one-stage individual participant data meta-analysis. 22 trials were included in the individual participant data network meta-analysis. After a median follow-up of 4·5 years (IQR 2·0), 9883 participants were diagnosed with new-onset type 2 diabetes. Systolic blood pressure reduction by 5 mm Hg reduced the risk of type 2 diabetes across all trials by 11% (hazard ratio 0·89 [95% CI 0·84–0·95]). Investigation of the effects of five major classes of antihypertensive drugs showed that in comparison to placebo, angiotensin-converting enzyme inhibitors (RR 0·84 [95% 0·76–0·93]) and angiotensin II receptor blockers (RR 0·84 [0·76–0·92]) reduced the risk of new-onset type 2 diabetes; however, the use of β blockers (RR 1·48 [1·27–1·72]) and thiazide diuretics (RR 1·20 [1·07–1·35]) increased this risk, and no material effect was found for calcium channel blockers (RR 1·02 [0·92–1·13]). Blood pressure lowering is an effective strategy for the prevention of new-onset type 2 diabetes. Established pharmacological interventions, however, have qualitatively and quantitively different effects on diabetes, likely due to their differing off-target effects, with angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers having the most favourable outcomes. This evidence supports the indication for selected classes of antihypertensive drugs for the prevention of diabetes, which could further refine the selection of drug choice according to an individual's clinical risk of diabetes. British Heart Foundation, National Institute for Health Research, and Oxford Martin School.

Dual-atom catalysts, which exhibit high activity and atom utilization, show promise for sustainable energy conversion and storage technologies. However, the rational design and synthesis of a dual-atom catalyst with structurally homogeneous and flexible active sites remains challenging. In this work, we developed a strategy for the synthesis of a carbon-based catalyst with diatomic Fe–Co sites in which the Fe and Co atoms are coordinated to N and O atoms, respectively, and linked through bridging N and O atoms (FeCo–N3O3@C). The Janus FeCo–N3O3@C quaternary dimer is a stable and efficient bifunctional catalyst in the electrocatalytic oxygen reduction reaction (half-wave potential E1/2 = 0.936 V) and oxygen evolution reaction (potential E = 1.528 V at 10 mA cm−2). When assembled in a Zn–air battery, it exhibits superior performance over a benchmark Pt/C + RuO2 air cathode. A series of ex situ and in situ characterizations, combined with theoretical calculations, revealed that the bifunctional performance of the catalyst originates from the strong coupling of the Fe–N3 and Co–O3 moieties, which alters the d-orbital energy level of the metal atoms, optimizing the adsorption–desorption of oxygenated intermediates and improving the reaction kinetics of the oxygen reduction and evolution reactions. The in-depth insights gained into the fundamental mechanism of this dual-atom catalyst at the atomic and electronic level will facilitate the rational design of further highly efficient multifunctional catalysts with customized activities for specific reactions.The rational design and synthesis of dual-atom catalysts with structurally uniform and flexible active sites remains challenging. Now the tailored synthesis of a Janus Fe–Co dual-metal catalyst is reported in which the Fe and Co atoms are coordinated to N and O, respectively, and linked through bridging N and O atoms.