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Methods (San Diego, Calif.) Jun 2024Asparagine peptide lyase (APL) is among the seven groups of proteases, also known as proteolytic enzymes, which are classified according to their catalytic residue. APLs...
Asparagine peptide lyase (APL) is among the seven groups of proteases, also known as proteolytic enzymes, which are classified according to their catalytic residue. APLs are synthesized as precursors or propeptides that undergo self-cleavage through autoproteolytic reaction. At present, APLs are grouped into 10 families belonging to six different clans of proteases. Recognizing their critical roles in many biological processes including virus maturation, and virulence, accurate identification and characterization of APLs is indispensable. Experimental identification and characterization of APLs is laborious and time-consuming. Here, we developed APLpred, a novel support vector machine (SVM) based predictor that can predict APLs from the primary sequences. APLpred was developed using Boruta-based optimal features derived from seven encodings and subsequently trained using five machine learning algorithms. After evaluating each model on an independent dataset, we selected APLpred (an SVM-based model) due to its consistent performance during cross-validation and independent evaluation. We anticipate APLpred will be an effective tool for identifying APLs. This could aid in designing inhibitors against these enzymes and exploring their functions. The APLpred web server is freely available at https://procarb.org/APLpred/.
PubMed: 38944134
DOI: 10.1016/j.ymeth.2024.05.014 -
The Journal of Biological Chemistry Jun 2024Z-nucleic acid structures play vital roles in cellular processes and have implications in innate immunity due to their recognition by Zα domains containing proteins...
Z-nucleic acid structures play vital roles in cellular processes and have implications in innate immunity due to their recognition by Zα domains containing proteins (Z-DNA/Z-RNA binding proteins, ZBPs). Although Zα domains have been identified in six proteins, including viral E3L, ORF112, and I73R, as well as, cellular ADAR1, ZBP1, and PKZ, their prevalence across living organisms remains largely unexplored. In this study, we introduce a computational approach to predict Zα domains, leading to the revelation of previously unidentified Zα domain-containing proteins in eukaryotic organisms, including non-metazoan species. Our findings encompass the discovery of new ZBPs in previously unexplored giant viruses, members of the Nucleocytoviricota phylum. Through experimental validation, we confirm the Zα functionality of select proteins, establishing their capability to induce the B-to-Z conversion. Additionally, we identify Zα-like domains within bacterial proteins. While these domains share certain features with Zα domains, they lack the ability to bind to Z-nucleic acids or facilitate the B-to-Z DNA conversion. Our findings significantly expand the ZBP family across a wide spectrum of organisms and raise intriguing questions about the evolutionary origins of Zα-containing proteins. Moreover, our study offers fresh perspectives on the functional significance of Zα domains in virus sensing and innate immunity and opens avenues for exploring hitherto undiscovered functions of ZBPs.
PubMed: 38944123
DOI: 10.1016/j.jbc.2024.107504 -
Environmental Research Jun 2024The Mediterranean Basin has experienced substantial land use changes as traditional agriculture decreased and population migrated from rural to urban areas, which have...
The Mediterranean Basin has experienced substantial land use changes as traditional agriculture decreased and population migrated from rural to urban areas, which have resulted in a large forest cover increase. The combination of Landsat time series, providing spectral information, with lidar, offering three-dimensional insights, has emerged as a viable option for the large-scale cartography of forest structural attributes across long time spans. Here we develop and test a comprehensive framework to map forest above ground biomass, canopy cover and forest height in two regions spanning the most representative biomes in the peninsular Spain, Mediterranean (Madrid region) and temperate (Basque Country). As reference, we used lidar-based direct estimates of stand height and forest canopy cover. The reference biomass and volume were predicted from lidar metrics. Landsat time series predictors included annual temporal profiles of band reflectance and vegetation indices for the 1985-2023 period. Additional predictor variables including synthetic aperture radar, disturbance history, topography and forest type were also evaluated to optimize forest structural attributes retrieval. The estimates were independently validated at two temporal scales, i) the year of model calibration and ii) the year of the second lidar survey. The final models used as predictor variables only Landsat based metrics and topographic information, as the available SAR time-series were relatively short (1991-2011) and disturbance information did not decrease the estimation error. Model accuracies were higher in the Mediterranean forests when compared to the temperate forests (R = 0.6-0.8 vs. 0.4-0.5). Between the first (1985-1989) and the last (2020-2023) decades of the monitoring period the average forest cover increased from 21 ± 2% to 32 ± 1%, mean height increased from 6.6 ± 0.43 m to 7.9 ± 0.18 m and the mean biomass from 31.9 ± 3.6 t ha to 50.4 ± 1 t ha for the Mediterranean forests. In temperate forests, the average canopy cover increased from 55 ± 4% to 59 ± 3%, mean height increased from 15.8 ± 0.77 m to 17.3 ± 0.21m, while the growing stock volume increased from 137.8 ± 8.2 to 151.5 ± 3.8 m ha. Our results suggest that multispectral data can be successfully linked with lidar to provide continuous information on forest height, cover, and biomass trends.
PubMed: 38944104
DOI: 10.1016/j.envres.2024.119432 -
The Journal of Arthroplasty Jun 2024The purpose of this study was to reconstruct three-dimensional (3D) computed tomography (CT) images from single anteroposterior (AP) postoperative total hip arthroplasty...
Measurement of the Acetabular Cup Orientation after Total Hip Arthroplasty Based on Three-Dimensional Reconstruction from a Single X-ray Image Using Generative Adversarial Networks.
BACKGROUND
The purpose of this study was to reconstruct three-dimensional (3D) computed tomography (CT) images from single anteroposterior (AP) postoperative total hip arthroplasty (THA) X-ray images using a deep learning algorithm known as generative adversarial networks (GANs) and to validate the accuracy of cup angle measurement on GAN-generated CT.
METHODS
We used two GAN-based models, CycleGAN and X2CT-GAN, to generate 3D CT images from X-ray images of 386 patients who underwent primary THAs using a cementless cup. The training dataset consisted of 522 CT images and 2,282 X-ray images. The image quality was validated using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). The cup anteversion and inclination measurements on the GAN-generated CT images were compared with the actual CT measurements. Statistical analyses of absolute measurement errors were performed using Mann-Whitney U tests and nonlinear regression analyses.
RESULTS
The study successfully achieved 3D reconstruction from single AP postoperative THA X-ray images using GANs, exhibiting excellent PSNR (37.40) and SSIM (0.74). The median absolute difference in radiographic anteversion (RA) was 3.45° and the median absolute difference in radiographic inclination (RI) was 3.25°, respectively. Absolute measurement errors tended to be larger in cases with cup malposition than in those with optimal cup orientation.
CONCLUSION
This study demonstrates the potential of GANs for 3D reconstruction from single AP postoperative THA X-ray images to evaluate cup orientation. Further investigation and refinement of this model are required to improve its performance.
PubMed: 38944061
DOI: 10.1016/j.arth.2024.06.059 -
Lancet (London, England) Jun 2024
PubMed: 38944048
DOI: 10.1016/S0140-6736(24)01011-0 -
Journal of Psychiatric Research Jun 2024Depression is a growing public health concern, and exercise is an adjunctive treatment modality to improve depression, but the optimal form of exercise and the optimal... (Review)
Review
Optimal exercise modality and dose to improve depressive symptoms in adults with major depressive disorder: A systematic review and Bayesian model-based network meta-analysis of RCTs.
Depression is a growing public health concern, and exercise is an adjunctive treatment modality to improve depression, but the optimal form of exercise and the optimal dose are still unclear. This systematic review examined the efficacy of four major types of exercise (aerobic, resistance, mixed, and mind-body) on depression, as well as the dose-response relationship between total and specific exercise and depressive symptoms. We included randomized controlled trials that included participants aged 18 years or older with a diagnosis of major depressive disorder or a depressive symptom score above a threshold as determined by a validated screening measure, implemented one or more exercise therapy groups, and assessed depressive symptoms at baseline and follow-up. Forty-six studies (3164 patients) were included in the meta-analysis. Aerobic (standardised mean difference (SMD) = -0.93; 95% CI: -1.25 to -0.62) and mind-body exercise (SMD) = -0.81; 95% CI: -1.19 to -0.42) improved depressive symptoms better compared to controls, followed by mixed (SMD = -0.77; 95% CI: -1.20 to -0.34) and resistance exercise (SMD = -0.76; 95% CI: -1.24 to -0.28). This dose-response meta-analysis showed a U-shaped curve between exercise dose and depressive symptoms. The minimum effective dose was estimated to be 320 metabolic equivalent (METs) -min per week and the optimal response was 860 METs-min per week. These findings lead us to advocate that clinicians carefully select the appropriate dose of exercise based on the patient's individual characteristics and needs, in conjunction with psychological care interventions.
PubMed: 38944017
DOI: 10.1016/j.jpsychires.2024.06.031 -
Journal of Psychiatric Research Jun 2024Numerous studies on post-COVID syndrome (PCS) describe persisting symptoms of cognitive impairment. Previous studies, however, often investigated small samples or did...
Numerous studies on post-COVID syndrome (PCS) describe persisting symptoms of cognitive impairment. Previous studies, however, often investigated small samples or did not assess covariates possibly linked to cognitive performance. We aimed to describe 1) global and domain-specific cognitive performance in adults with PCS, controls with previous SARS-CoV-2 infection and healthy controls, 2) associations of sociodemographics, depressive symptoms, anxiety, fatigue, somatic symptoms and stress with cognitive performance and subjective cognitive decline (SCD), using data of the LIFE-Long-COVID-Study from Leipzig, Germany. Group differences in cognitive performance and associations with sociodemographic and neuropsychiatric covariates were assessed using multivariable regression analyses. Our study included n = 561 adults (M: 48.8, SD: 12.7; % female: 70.6). Adults with PCS (n = 410) performed worse in tests on episodic memory (b = -1.07, 95 % CI: -1.66, -0.48) and visuospatial abilities (b = -3.92, 95 % CI: -6.01, -1.83) compared to healthy controls (n = 64). No impairments were detected for executive function, verbal fluency, and global cognitive performance. Odds of SCD were not higher in PCS. A previous SARS-CoV-2 infection without PCS (n = 87) was not linked to cognitive impairment. Higher age and higher levels of stress and fatigue were linked to worse performance in several cognitive domains. Routine administration of tests for episodic memory and visuospatial abilities might aid in the identification of individuals at risk for cognitive impairment when reporting symptoms of PCS. Low numbers of participants with severe COVID-19 infections possibly limit generalizability of our findings.
PubMed: 38944016
DOI: 10.1016/j.jpsychires.2024.06.036 -
Water Research Jun 2024Quantitation of sewer inflow and infiltration (I/I) is important for maintaining efficient wastewater transport and treatment. I/I flows can be quantified based on flow...
Quantitation of sewer inflow and infiltration (I/I) is important for maintaining efficient wastewater transport and treatment. I/I flows can be quantified based on flow rate and water quality measurements. Flow rate-based methods require continuous monitoring of flow rates using flow meters that are costly and prone to fouling. In comparison, conductivity and temperature, as simple water quality parameters, are more easily measurable with more cost-effective and reliable sensors. In this study, a data-driven methodology is developed for estimating I/I flows based on online conductivity and temperature measurements. A Prophet-model-based analytic algorithm is first developed to reconstruct the temperature and conductivity profiles of the base wastewater flow (BWF) from the measured temperature and conductivity time series. The algorithm is shown to be able to reconstruct the BWF temperature and conductivity profiles in two monitored catchments. The reconstructed BWF data are then incorporated into mass/energy balance equations for estimating I/I flows from the measured temperature and conductivity data. The overall I/I quantification method is finally demonstrated using simulation studies of a real-life sewer network and validated against the known I/I flows. This work provides a reliable method for I/I quantification based on simple measurements.
PubMed: 38944000
DOI: 10.1016/j.watres.2024.122002 -
Neoplasia (New York, N.Y.) Jun 2024Cancer of unknown primary (CUP) is a rare type of metastatic cancer in which the origin of the tumor is unknown. Since the treatment strategy for patients with...
Cancer of unknown primary (CUP) is a rare type of metastatic cancer in which the origin of the tumor is unknown. Since the treatment strategy for patients with metastatic tumors depends on knowing the primary site, accurate identification of the origin site is important. Here, we developed an image-based deep-learning model that utilizes a vision transformer algorithm for predicting the origin of CUP. Using DNA methylation dataset of 8,233 primary tumors from The Cancer Genome Atlas (TCGA), we categorized 29 cancer types into 18 organ classes and extracted 2,312 differentially methylated CpG sites (DMCs) from non-squamous cancer group and 420 DMCs from squamous cell cancer group. Using these DMCs, we created organ-specific DNA methylation images and used them for model training and testing. Model performance was evaluated using 394 metastatic cancer samples from TCGA (TCGA-meta) and 995 samples (693 primary and 302 metastatic cancers) obtained from 20 independent external studies. We identified that the DNA methylation image reveals a distinct pattern based on the origin of cancer. Our model achieved an overall accuracy of 96.95 % in the TCGA-meta dataset. In the external validation datasets, our classifier achieved overall accuracies of 96.39 % and 94.37 % in primary and metastatic tumors, respectively. Especially, the overall accuracies for both primary and metastatic samples of non-squamous cell cancer were exceptionally high, with 96.79 % and 96.85 %, respectively.
PubMed: 38943996
DOI: 10.1016/j.neo.2024.101021 -
Computer Methods and Programs in... Jun 2024Atrial fibrillation (AF) is the most common cardiac arrhythmia, inducing accelerated and irregular beating. Beside well-known disabling symptoms - such as palpitations,...
BACKGROUND AND OBJECTIVE
Atrial fibrillation (AF) is the most common cardiac arrhythmia, inducing accelerated and irregular beating. Beside well-known disabling symptoms - such as palpitations, reduced exercise tolerance, and chest discomfort - there is growing evidence that an alteration of deep cerebral hemodynamics due to AF increases the risk of vascular dementia and cognitive impairment, even in the absence of clinical strokes. The alteration of deep cerebral circulation in AF represents one of the least investigated among the possible mechanisms. Lenticulostriate arteries (LSAs) are small perforating arteries mainly departing from the middle cerebral artery (MCA) and susceptible to small vessel disease, which is one of the mechanisms of subcortical vascular dementia development. The purpose of this study is to investigate the impact of different LSAs morphologies on the cerebral hemodynamics during AF.
METHODS
By combining a computational fluid dynamics (CFD) analysis of LSAs with 7T high-resolution magnetic resonance imaging (MRI), we performed different CFD-based multivariate regression analyses to detect which geometrical and morphological vessel features mostly affect AF hemodynamics in terms of wall shear stress. We exploited 17 cerebral 7T-MRI derived LSA vascular geometries extracted from 10 subjects and internal carotid artery data from validated 0D cardiovascular-cerebral modeling as inflow conditions.
RESULTS
Our results revealed that few geometrical variables - namely the size of the MCA and the bifurcation angles between MCA and LSA - are able to satisfactorily predict the AF impact. In particular, the present study indicates that LSA morphologies exhibiting markedly obtuse LSA-MCA inlet angles and small MCA size downstream of the LSA-MCA bifurcation may be more prone to vascular damage induced by AF.
CONCLUSIONS
The present MRI-based computational study has been able for the first time to: (i) investigate the net impact of LSAs vascular morphologies on cerebral hemodynamics during AF events; (ii) detect which combination of morphological features worsens the hemodynamic response in the presence of AF. Awaiting necessary clinical confirmation, our analysis suggests that the local hemodynamics of LSAs is affected by their geometrical features and some LSA morphologies undergo greater hemodynamic alterations in the presence of AF.
PubMed: 38943985
DOI: 10.1016/j.cmpb.2024.108303