bluuebunny/crossref_metadata_2025_split huggingface dataset:
The impact of gardening on well-being, mental health, and quality of life: an umbrella review and meta-analysis
Authors: Panțiru I. , Ronaldson A. , Sima N. , Dregan A. , Sima R. 
Published: January 2024

Abstract
Background
Gardening and horticultural therapy (HT) has been widely recognised as a multicomponent approach that has affected a broad range of health and well-being outcomes. The aim of this umbrella review and meta-analysis was to compare the findings of previous reviews on the impact of multiple gardening interventions and gardening attributes on different well-being constructs.

Methods
Electronic databases including PubMed, Web of Science, Science Direct, the Cochrane Library, and Google Scholar were searched from inception to December 2022. Interventional and observational reviews were eligible for inclusion in this umbrella review. Outcome measures included mental well-being, health status and quality of life. The key exposure variables were gardening and horticultural therapy. Narrative synthesis was used to evaluate the overall impact of gardening and HT on study outcomes. For a subsample of studies with available quantitative data, a random effect meta-analysis was conducted.

Results
This umbrella review included 40 studies (10 interventional studies, 2 observational studies, and 28 mixed interventional and observational studies). The reviewed studies reported an overall positive impact of gardening activities on several measures of mental well-being, quality of life, and health status. Meta-analysis showed a significant and positive effect of gardening and HT activities on well-being (effect size (ES) 0.55, 95% confidence interval (CI) 0.23, 0.87, p < 0.001).

Conclusions
Evidence from observational and interventional studies supports a positive role for gardening and HT activities on well-being and general health. Interventional studies with horticultural-based therapies were effective in improving well-being and quality of life both in the general population and vulnerable subgroups. The high degree of heterogeneity in the included studies cautions against any direct clinical implications of the study findings.
Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg
Authors: Nyaga Victoria Nyawira , Arbyn Marc 
Published: January 2024

AbstractBackgroundDespite the widespread interest in meta-analysis of proportions, its rationale, certain theoretical and methodological concepts are poorly understood. The generalized linear models framework is well-established and provides a natural and optimal model for meta-analysis, network meta-analysis, and meta-regression of proportions. Nonetheless, generic methods for meta-analysis of proportions based on the approximation to the normal distribution continue to dominate.MethodsWe developed , a tool with advanced statistical procedures to perform a meta-analysis, network meta-analysis, and meta-regression of binomial proportions in Stata using binomial, logistic and logistic-normal models. First, we explain the rationale and concepts essential in understanding statistical methods for meta-analysis of binomial proportions and describe the models implemented in . We then describe and demonstrate the models in using data from seven published meta-analyses. We also conducted a simulation study to compare the performance of estimators with the existing estimators of the population-averaged proportion in and under a broad range of conditions including, high over-dispersion and small meta-analysis.Conclusionis a flexible, robust and user-friendly tool employing a rigorous approach to evidence synthesis of binomial data that makes the most efficient use of all available data and does not require ad-hoc continuity correction or data imputation. We expect its use to yield higher-quality meta-analysis of binomial proportions.
Functional similarity, despite taxonomical divergence in the millipede gut microbiota, points to a common trophic strategy
Authors: Nweze Julius Eyiuche , Šustr Vladimír , Brune Andreas , Angel Roey 
Published: January 2024

Abstract
Background
Many arthropods rely on their gut microbiome to digest plant material, which is often low in nitrogen but high in complex polysaccharides. Detritivores, such as millipedes, live on a particularly poor diet, but the identity and nutritional contribution of their microbiome are largely unknown. In this study, the hindgut microbiota of the tropical millipede Epibolus pulchripes (large, methane emitting) and the temperate millipede Glomeris connexa (small, non-methane emitting), fed on an identical diet, were studied using comparative metagenomics and metatranscriptomics.

Results
The results showed that the microbial load in E. pulchripes is much higher and more diverse than in G. connexa. The microbial communities of the two species differed significantly, with Bacteroidota dominating the hindguts of E. pulchripes and Proteobacteria (Pseudomonadota) in G. connexa. Despite equal sequencing effort, de novo assembly and binning recovered 282 metagenome-assembled genomes (MAGs) from E. pulchripes and 33 from G. connexa, including 90 novel bacterial taxa (81 in E. pulchripes and 9 in G. connexa). However, despite this taxonomic divergence, most of the functions, including carbohydrate hydrolysis, sulfate reduction, and nitrogen cycling, were common to the two species. Members of the Bacteroidota (Bacteroidetes) were the primary agents of complex carbon degradation in E. pulchripes, while members of Proteobacteria dominated in G. connexa. Members of Desulfobacterota were the potential sulfate-reducing bacteria in E. pulchripes. The capacity for dissimilatory nitrate reduction was found in Actinobacteriota (E. pulchripes) and Proteobacteria (both species), but only Proteobacteria possessed the capacity for denitrification (both species). In contrast, some functions were only found in E. pulchripes. These include reductive acetogenesis, found in members of Desulfobacterota and Firmicutes (Bacillota) in E. pulchripes. Also, diazotrophs were only found in E. pulchripes, with a few members of the Firmicutes and Proteobacteria expressing the nifH gene. Interestingly, fungal-cell-wall-degrading glycoside hydrolases (GHs) were among the most abundant carbohydrate-active enzymes (CAZymes) expressed in both millipede species, suggesting that fungal biomass plays an important role in the millipede diet.

Conclusions
Overall, these results provide detailed insights into the genomic capabilities of the microbial community in the hindgut of millipedes and shed light on the ecophysiology of these essential detritivores.
Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry traces the geographical source of Biomphalaria pfeifferi and Bulinus forskalii, involved in schistosomiasis transmission
Authors: Gaye Papa Mouhamadou , Ndiaye El Hadj Ibrahima , Doucouré Souleymane , Sow Doudou , Gaye Mapenda , Goumballa Ndiaw , Cassagne Carole , L’Ollivier Coralie , Medianikov Oleg , Sokhna Cheikh , Ranque Stéphane 
Published: January 2024

Abstract
Background
Freshwater snails of the genera Bulinus spp., Biomphalaria spp., and Oncomelania spp. are the main intermediate hosts of human and animal schistosomiasis. Identification of these snails has long been based on morphological and/or genomic criteria, which have their limitations. These limitations include a lack of precision for the morphological tool and cost and time for the DNA-based approach. Recently, Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight (MALDI–TOF) mass spectrometry, a new tool used which is routinely in clinical microbiology, has emerged in the field of malacology for the identification of freshwater snails. This study aimed to evaluate the ability of MALDI–TOF MS to identify Biomphalariapfeifferi and Bulinusforskalii snail populations according to their geographical origin.

Methods
This study was conducted on 101 Bi.pfeifferi and 81 Bu.forskalii snails collected in three distinct geographical areas of Senegal (the North-East, South-East and central part of the country), and supplemented with wild and laboratory strains. Specimens which had previously been morphologically described were identified by MALDI–TOF MS [identification log score values (LSV) ≥ 1.7], after an initial blind test using the pre-existing database. After DNA-based identification, new reference spectra of Bi.pfeifferi (n = 10) and Bu.forskalii (n = 5) from the geographical areas were added to the MALDI–TOF spectral database. The final blind test against this updated database was performed to assess identification at the geographic source level.

Results
MALDI–TOF MS correctly identified 92.1% of 101 Bi.pfeifferi snails and 98.8% of 81 Bu.forskalii snails. At the final blind test, 88% of 166 specimens were correctly identified according to both their species and sampling site, with LSVs ranging from 1.74 to 2.70. The geographical source was adequately identified in 90.1% of 91 Bi.pfeifferi and 85.3% of 75 Bu.forskalii samples.

Conclusions
Our findings demonstrate that MALDI–TOF MS can identify and differentiate snail populations according to geographical origin. It outperforms the current DNA-based approaches in discriminating laboratory from wild strains. This inexpensive high-throughput approach is likely to further revolutionise epidemiological studies in areas which are endemic for schistosomiasis.
An interactive augmented reality framework to enhance the user experience and operational skills in electronics laboratories
Authors: Singh Gurjinder , Ahmad Faizan 
Published: January 2024

AbstractAugmented reality (AR) stands as a widely embraced technology that significantly enhances learning experiences for students. AR offers an instructional approach supported by technological design, thereby fostering enriched learning interactions. This research proposes an interactive AR framework, intended to create an augmented reality learning environment (ARLE) for the specific needs of electronics engineering laboratory hardware operations. The ARLE functions as an active learning system designed using a user-centered methodology. It offers interactive 3D models of laboratory equipment, providing learners with preliminary training in equipment operation. The real-time connection between the laboratory apparatus and the AR environment is established using the Arduino board. This interface empowers users to control the AR simulation through the laboratory equipment seamlessly. An experimental study involving 80 engineering students was conducted to evaluate the impact of AR intervention on user experience, usability, and operational skills. The participants were divided into two groups: the experimental group (N = 40) and the control group (N = 40). The experimental group underwent electronics equipment training using ARLE, while the control group followed instructions from a standard instrument handbook. To assess the usability and user experience of ARLE, the system usability scale (SUS) and the user experience questionnaire (UEQ) were employed (N = 40). The findings revealed an SUS score of 80.9 for ARLE, categorizing it as “good” according to SUS ratings. Additionally, the UEQ results demonstrated significantly favorable scores across the six scales when compared to the benchmark dataset. The study's outcomes demonstrate that AR intervention offers learners significant pedagogical value, resulting in a substantial positive impact on operational skills in electronics laboratories.
Short-term prediction of celestial pole offsets with interpretable machine learning
Authors: Kiani Shahvandi Mostafa , Belda Santiago , Mishra Siddhartha , Soja Benedikt 
Published: January 2024

AbstractThe difference between observed and modelled precession/nutation reveals unmodelled signals commonly referred to as Celestial Pole Offsets (CPO), denoted by dX and dY. CPO are currently observed only by Very Long Baseline Interferometry (VLBI), but there is nearly 4 weeks of latency by which the data centers provide the most accurate, final CPO series. This latency problem necessitates predicting CPO for high-accuracy, real-time applications that require information regarding Earth rotation, such as spacecraft navigation. Even though the International Earth Rotation and Reference Systems Service (IERS) provides so-called rapid CPO, they are usually less accurate and therefore, may not satisfy the requirements of the mentioned applications. To enhance the quality of CPO predictions, we present a new methodology based on Neural Additive Models (NAMs), a class of interpretable machine learning algorithms. We formulate the problem based on long short-term memory neural networks and derive simple analytical relations for the quantification of prediction uncertainty and feature importance, thereby enhancing the intelligibility of predictions made by machine learning. We then focus on the short-term prediction of CPO with a forecasting horizon of 30 days. We develop an operational framework that consistently provides CPO predictions. Using the CPO series of Jet Propulsion Laboratory as the input to the algorithm, we show that NAMs predictions improve the IERS rapid products on average by 57% for dX and 25% for dY under fully operational conditions. Our predictions are both accurate and overcome the latency issue of final CPO series and thus, can be used in real-time applications.Graphical Abstract
Dual-quaternion-based iterative algorithm of the three dimensional coordinate transformation
Authors: Zeng Huaien , Wang Zhihao , Li Junfeng , Li Siyang , Wang Junjie , Li Xi 
Published: January 2024

AbstractNowadays a unit quaternion is widely employed to represent the three-dimensional (3D) rotation matrix and then applied to the 3D similarity coordinate transformation. A unit dual quaternion can describe not only the 3D rotation matrix but also the translation vector meanwhile. Thus it is of great potentiality to the 3D coordinate transformation. The paper constructs the 3D similarity coordinate transformation model based on the unit dual quaternion in the sense of errors-in-variables (EIV). By means of linearization by Taylor's formula, Lagrangian extremum principle with constraints, and iterative numerical technique, the Dual Quaternion Algorithm (DQA) of 3D coordinate transformation in weighted total least squares (WTLS) is proposed. The algorithm is capable to not only compute the transformation parameters but also estimate the full precision information of computed parameters. Two numerical experiments involving an actual geodetic datum transformation case and a simulated case from surface fitting are demonstrated. The results indicate that DQA is not sensitive to the initial values of parameters, and obtains the consistent values of transformation parameters with the quaternion algorithm (QA), regardless of the size of the rotation angles and no matter whether the relative errors of coordinates (pseudo-observations) are small or large. Moreover, the DQA is advantageous to the QA. The key advantage is the improvement of estimated precisions of transformation parameters, i.e. the average decrease percent of standard deviations is 18.28%, and biggest decrease percent is 99.36% for the scaled quaternion and translations in the geodetic datum transformation case. Another advantage is the DQA implements the computation and precision estimation of traditional seven transformation parameters (which still are frequent used yet) from dual quaternion, and even could perform the computation and precision estimation of the scaled quaternion.
Graphical Abstract
Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [18F]FDG PET
Authors: Narciso Lucas , Deller Graham , Dassanayake Praveen , Liu Linshan , Pinto Samara , Anazodo Udunna , Soddu Andrea , Lawrence Keith St 
Published: January 2024

Abstract
Background
Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data.

Methods
Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach.

Results
Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns.

Conclusions
A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.
Evaluating a radiotherapy deep learning synthetic CT algorithm for PET-MR attenuation correction in the pelvis
Authors: Wyatt Jonathan J. , Kaushik Sandeep , Cozzini Cristina , Pearson Rachel A. , Petrides George , Wiesinger Florian , McCallum Hazel M. , Maxwell Ross J. 
Published: January 2024

Abstract
Background
Positron emission tomography–magnetic resonance (PET-MR) attenuation correction is challenging because the MR signal does not represent tissue density and conventional MR sequences cannot image bone. A novel zero echo time (ZTE) MR sequence has been previously developed which generates signal from cortical bone with images acquired in 65 s. This has been combined with a deep learning model to generate a synthetic computed tomography (sCT) for MR-only radiotherapy. This study aimed to evaluate this algorithm for PET-MR attenuation correction in the pelvis.

Methods
Ten patients being treated with ano-rectal radiotherapy received a $$^{18}$$

18

F-FDG-PET-MR in the radiotherapy position. Attenuation maps were generated from ZTE-based sCT (sCTAC) and the standard vendor-supplied MRAC. The radiotherapy planning CT scan was rigidly registered and cropped to generate a gold standard attenuation map (CTAC). PET images were reconstructed using each attenuation map and compared for standard uptake value (SUV) measurement, automatic thresholded gross tumour volume (GTV) delineation and GTV metabolic parameter measurement. The last was assessed for clinical equivalence to CTAC using two one-sided paired t tests with a significance level corrected for multiple testing of $$p \le 0.05/7 = 0.007$$

p
≤
0.05
/
7
=
0.007

. Equivalence margins of $$\pm 3.5\%$$

±
3.5
%

were used.

Results
Mean whole-image SUV differences were −0.02% (sCTAC) compared to −3.0% (MRAC), with larger differences in the bone regions (−0.5% to −16.3%). There was no difference in thresholded GTVs, with Dice similarity coefficients $$\ge 0.987$$

≥
0.987

. However, there were larger differences in GTV metabolic parameters. Mean differences to CTAC in $${\mathrm {SUV}}_{\max}$$

SUV
max

were $$1.0 \pm 0.8\%$$

1.0
±
0.8
%

(± standard error, sCTAC) and $$-4.6 \pm 0.9\%$$

-
4.6
±
0.9
%

(MRAC), and $$1.0 \pm 0.7\%$$

1.0
±
0.7
%

(sCTAC) and $$-4.3 \pm 0.8\%$$

-
4.3
±
0.8
%

(MRAC) in $${\mathrm {SUV}}_{\rm mean}$$

SUV
mean

. The sCTAC was statistically equivalent to CTAC within a $$\pm 3.5\%$$

±
3.5
%

equivalence margin for $${\mathrm {SUV}}_{\max}$$

SUV
max

and $${\mathrm {SUV}}_{\rm mean}$$

SUV
mean

($$p = 0.007$$

p
=
0.007

and $$p = 0.002$$

p
=
0.002

), whereas the MRAC was not ($$p = 0.88$$

p
=
0.88

and $$p = 0.83$$

p
=
0.83

).

Conclusion
Attenuation correction using this radiotherapy ZTE-based sCT algorithm was substantially more accurate than current MRAC methods with only a 40 s increase in MR acquisition time. This did not impact tumour delineation but did significantly improve the accuracy of whole-image and tumour SUV measurements, which were clinically equivalent to CTAC. This suggests PET images reconstructed with sCTAC would enable accurate quantitative PET images to be acquired on a PET-MR scanner.
AMPK activation enhances osteoblast differentiation on a titanium disc via autophagy
Authors: Egashira Kei , Kajiya Hiroshi , Tsutsumi Takashi , Taniguchi Yusuke , Kakura Kae , Ohno Jun , Kido Hirofumi 
Published: January 2024

Abstract
Purpose
The acquisition of osseointegration during implant therapy is slower and poorer in patients with diabetes compared with healthy persons. The serum concentration of adiponectin in patients with type II diabetes is lower than that of healthy persons via the suppression of AMP-activated protein kinase (AMPK). Therefore, we hypothesized that the AMPK activation enhances bone formation around implants, resulting in the improved acquisition of osseointegration. The purpose of this study was to evaluate the impact of AMPK activation on osteoblast differentiation and its mechanism of downstream signaling on titanium disc (Ti).

Methods
Confluent mouse pre-osteoblasts (MC3T3-E1) cells (1 × 105 cells/well) were cultured with BMP-2 for osteoblast differentiation, in the presence or absence AICAR, an AMPK activator. We examined the effects of AMPK activation on osteoblast differentiation and the underlying mechanism on a Ti using a CCK8 assay, a luciferase assay, quantitative RT-PCR, and western blotting.

Results
Although the proliferation rate of osteoblasts was not different between a Ti and a tissue culture polystyrene dish, the addition of AICAR, AMPK activator slightly enhanced osteoblast proliferation on the Ti. AICAR enhanced the BMP-2-dependent transcriptional activity on the Ti, leading to upregulation in the expression of osteogenesis-associated molecules. AICAR simultaneously upregulated the expression of autophagy-associated molecules on the Ti, especially LC3-II. AdipoRon, an adiponectin receptor type1/type2 activator activated AMPK, and upregulated osteogenesis-associated molecules on Ti.

Conclusions
AMPK activation enhances osteoblast differentiation on a Ti via autophagy, suggesting that it promotes the acquisition of osseointegration during implant therapy.