Inverse Model Research Articles

The effect of exercise training in people with pre-dialysis chronic kidney disease: a systematic review with meta-analysis.

Chronic kidney disease (CKD) is a global health issue with high mortality and economic costs. Exercise has potential benefits for pre-dialysis CKD management. This review examines the impact of exercise on CKD patients not on dialysis, focusing on improvement in various health parameters. Findings aim to inform the role of exercise in pre-dialysis CKD care. A systematic search of MEDLINE, EMBASE, the Cochrane Library of Controlled Trials, CINAHL, and SPORTDiscus, up to August 31, 2023, used key terms relating to pre-dialysis CKD and exercise. We pooled randomized controlled trials (RCTs) comparing exercise with usual care and conducted meta-analyses based on a random effects inverse variance model with the effect measure of mean difference. Of 1162 identified studies, 37 RCTs met the inclusion criteria including 1248 participants. Significant improvements were identified for peak VO2, mean difference [MD] (2.66mL/kg/min; 95% confidence interval [CI] 1.48, 3.83; p < 0.00001); the 6-min walk (MD 58.83m; 95% CI 35.26, 82.41; p < 0.00001), timed up and go (standardised mean difference -0.35; 95% CI -0.54, -0.15; p = 0.0006), 2-min step (MD 57.48 steps; 95% CI 27.80, 87.16; p = 0.0001), and sit to stand tests (MD 4.55 repetitions; 95% CI 1.49, 7.60; p = 0.004); short form [SF]-36 general health (MD 4.26; 95% CI 0.04, 8.47; p = 0.05); SF-36 mental component summary (MD 1.84; 95% CI 0.18, 3.51; p = 0.03); estimated glomerular filtration rate (MD 2.19mL/min/1.73 m2; 95% CI 0.97, 3.50; p = 0.001); serum cystatin-C (MD -0.06mg/L; 95% CI -0.11, -0.02; p = 0.004); resting heart rate (MD -1.97 bpm; 95% CI -3.84, -0.11; p = 0.04); triglycerides (MD -12.97mg/dL; 95% CI -17.30, -8.63; p < 0.00001); glycosylated haemoglobin (MD -0.25%; 95% CI -0.50, -0.01; p = 0.04); waist circumference (MD -3.12cm; 95% CI -4.37, -1.86; p < 0.00001); and interleukin-6 (MD -2.24pg/mL; 95% CI -3.87. -0.61; p = 0.007). Analysis revealed improvements in aerobic capacity, functional ability, quality of life, estimated glomerular filtration rate, serum cystatin-C, resting heart rate, waist circumference, triglyceride, glycosylated haemoglobin, and interleukin-6 levels.

A framework for reconstructing marine heatwaves from individual foraminifera in sedimentary archives

Marine heatwaves (MHWs) are warm sea surface temperature (SST) anomalies with substantial ecological and economic consequences. Observations of MHWs are based on relatively short instrumental records, which limit the ability to forecast these events on decadal and longer timescales. Paleoclimate reconstructions can extend the observational record and help to evaluate model performance under near future conditions, but paleo-MHW reconstructions have received little attention, primarily because marine sediments lack the temporal resolution to record short-lived events. Individual foraminifera analysis (IFA) of paleotemperature proxies presents an intriguing opportunity to reconstruct past MHW variability if strong relationships exist between SST distributions and MHW metrics. Here, we describe a method to test this idea by systematically evaluating relationships between MHW metrics and SST distributions that mimic IFA data using a 2000-member linear inverse model (LIM) ensemble. Our approach is adaptable and allows users to define MHWs based on multiple duration and intensity thresholds and to model seasonal biases in five different foraminifera species. It also allows uncertainty in MHW reconstructions to be calculated for a given number of IFA measurements. An example application of our method at 12 north Pacific locations suggests that the cumulative intensity of short-duration, low-intensity MHWs is the strongest target for reconstruction, but that the error on reconstructions will rely heavily on sedimentation rate and the number of foraminifera analyzed. This is evident when a robust transfer function is applied to new core-top oxygen isotope data from 37 individual Globigerina bulloides at a site with typical marine sedimentation rates. In this example application, paleo-MHW reconstructions have large uncertainties that hamper comparisons to observational data. However, additional tests demonstrate that our approach has considerable potential to reconstruct past MHW variability at high sedimentation rate sites where hundreds of foraminifera can be analyzed.

Power or speed: Which metric is more accurate for modelling endurance running performance on track?

This study aimed to compare the accuracy of the power output, measured by a power meter, with respect to the speed, measured by an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) sport watch to determine the critical power (CP) and speed (CS), work over CP (W') and CS (D'), and long-duration performance (i.e., 60 min). Fifteen highly trained athletes randomly performed seven time trials on a 400m track. The CP/CS and W'/D' were defined through the inverse of time model using the 3, 4, 5, 10, and 20 min trials. The 60 min performance was estimated through the power law model using the 1, 3, and 10 min trials and compared with the actual performance. A lower standard error of the estimate was obtained when using the power meter (CP: 2.7 [2.1-3.3] % and W': 13.8 [10.4-17.3] %) compared to the speed reported by the IMU (CS: 3.4 [2.5-4.3] %) and D': 20.7 [16.6-24.7] %) and GNSS sport watch (CS: 3.4 [2.5-4.3] % and D': 20.6 [16.7-24.7] %). A lower coefficient of variation was also observed for the power meter (4.9 [3.7-6.1] %) Regarding the speed reported by the IMU (10.9 [7.1-14.8] %) and GNSS sport watch (10.9 [7.0-14.7] %) in the 60 min performance estimation, the power meter offered lower errors than the IMU and GNSS sport watch for modelling endurance performance on the track.

New insights and theoretical conditions for determining minor and major consolidations in the inverse DEA

Abstract Accepted by: Ali Emrouznejad In the current paper, we propose new conditions for determining major and minor consolidation using the inverse data envelopment analysis models. We categorized the causes of major consolidation into pre and post-effect. Pre-effects may happen due to the omission of pre-merged units and post-effects are associated with the fact that the merged unit may not belong to the production set. The idea is described by numerical examples and graphical illustrations and an empirical analysis in banking also helps us to show the necessity, strength, and applicability of the proposed models for real-world applications.

Association of Prehospital Rearrest With Outcome Following Out-of-Hospital Cardiac Arrest: A Systematic Review and Meta-Analysis of Observational Studies

Objectives Exposure to prehospital rearrest has previously been associated with mortality following out-of-hospital cardiac arrest (OHCA). Our objective was to conduct a systematic review and meta-analysis examining the association between prehospital rearrest and survival in adults following OHCA resuscitation. Methods We searched the PubMed, Scopus, and Web of Science bibliographic databases for observational studies that included adult OHCA patients who achieved return of spontaneous circulation in the prehospital setting following OHCA and reported survival to hospital discharge data stratified by rearrest status. The primary exposure was prehospital rearrest. The primary outcome for this study was survival to hospital discharge. Secondary outcomes included survival with a favorable neurological outcome and rearrest prevalence. We pooled data using inverse heterogeneity modeling and presented effect sizes for the survival outcomes as odds ratios with 95% confidence intervals. We quantified heterogeneity using Cochran’s Q and the I2 statistic and examined small study effects using Doi plots and the LFK index. Results Of the 84 publications screened, we included 7 observational studies containing 27,045 patients with survival to hospital discharge data. Rearrest was common (30% [18–43%]; n = 7 studies; Q = 1086.1, p < 0.001; I2 = 99%; LFK index = 1.21) and associated with both decreased odds of survival to discharge (pooled aOR: 0.27 [0.22, 0.33]; n = 7 studies; Q = 32.2, p < 0.01, I2 = 81%, LFK index = −0.08) and decreased odds of survival to discharge with a favorable neurological outcome (pooled aOR: 0.25, [0.22, 0.28]; n = 4 studies; Q = 3.5, p = 0.3; I2 = 13%, LFK index = 1.30). Conclusions Rearrest is common and associated with decreased survival following OHCA. The pooled result of this meta-analysis suggests that preventing rearrest in five patients would be necessary to save one life.

Fast neural network inverse model to maximize throughput in ultra-wideband WDM systems

Ultra-wideband systems expand the optical bandwidth in wavelength-division multiplexed (WDM) systems to provide increased capacity using the existing fiber infrastructure. In ultra-wideband transmission, power is transferred from shorter-wavelength WDM channels to longer-wavelength WDM channels due to inelastic inter-channel stimulated Raman scattering. Thus, managing launch power is necessary to improve the overall data throughput. While the launch power optimization problem can be solved by the particle swarm optimization method it is sensitive to the objective value and requires intensive objective calculations. Hence, we first propose a fast and accurate data-driven deep neural network-based physical layer in this paper which can achieve 99%−100% throughput compared to the semi-analytical approach with more than 2 orders of magnitude improvement in computational time. To further reduce the computational time, we propose an iterative greedy algorithm enabled by the inverse model to well approximate a sub-optimal solution with less than 6% performance degradation but almost 3 orders of magnitude reduction in computational time.

Linking individual experiments and multiscale models to simulate physiological perturbations on aquatic food webs

Numerous threats affect aquatic ecosystems at different biological organizational levels from individuals to ecosystems. Stresses occurring on the metabolism and physiological functions of individuals can have repercussions on the individual behavior, its ability to survive and reproduce, also known as the individual fitness, which may then influence the demography and spatial distribution of populations, and ultimately modify trophic flows and ecosystem functioning. In a context of a globally changing environment, predicting the life history traits and fitness of individuals can be relevantly performed with the association of laboratory experiments with Dynamic Energy Budget (DEB) theory, while modeling species interactions have proven to be an efficient tool to understand aquatic food webs using mass-balanced models such as linear inverse models (LIMs) or Chance and Necessity (CaN) models. However, while predictive results obtained on individuals can be provided with a thorough mechanistic interpretation, the propagation of the effects is most often limited to the closest biological hierarchical level, i.e., the population, and rarely to the food-web level. Furthermore, there is a need to understand how to avoid misleading approaches and interpretations due to the simplicity of experiments. For the moment, no clear methodology has stood out yet to do so. In this study, we provide a new methodology based on a combination of models (i.e., DEB, LIM, and CaN) aiming at upscaling information from laboratory experiments on individuals to ecosystems to address multiple ecological issues. This framework has a potential to enhance our understanding of higher-scale consequences of the effect of stressors measured at the sub-individual scale. This combination of models was chosen for the convergence of their framework but also their ability to consider a substantial portion of the projected uncertainty. The description of this methodology can help experimenters and modelers to jointly address a specific question involving upscaling from individual to ecosystem, proposes approaches, and gives tips on the pitfalls to avoid along the upscaling process.

Atelectrauma can be avoided if expiration is sufficiently brief: evidence from inverse modeling and oscillometry during airway pressure release ventilation

BackgroundAirway pressure release ventilation (APRV) has been shown to be protective against atelectrauma if expirations are brief. We hypothesize that this is protective because epithelial surfaces are not given enough time to come together and adhere during expiration, thereby avoiding their highly damaging forced separation during inspiration.MethodsWe investigated this hypothesis in a porcine model of ARDS induced by Tween lavage. Animals were ventilated with APRV in 4 groups based on whether inspiratory pressure was 28 or 40 cmH2O, and whether expiration was terminated when end-expiratory flow reached either 75% (a shorter expiration) or 25% (a longer expiration) of its initial peak value. A mathematical model of respiratory system mechanics that included a volume-dependent elastance term characterized by the parameter E2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{2}$$\\end{document} was fit to airway pressure-flow data obtained each hour for 6 h post-Tween injury during both expiration and inspiration. We also measured respiratory system impedance between 5 and 19 Hz continuously through inspiration at the same time points from which we derived a time-course for respiratory system resistance (Rrs\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{rs}$$\\end{document}).ResultsE2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{2}$$\\end{document}during both expiration and inspiration was significantly different between the two longer expiration versus the two shorter expiration groups (ANOVA, p < 0.001). We found that E2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{2}$$\\end{document} was most depressed during inspiration in the higher-pressure group receiving the longer expiration, suggesting that E2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{2}$$\\end{document} reflects a balance between strain stiffening of the lung parenchyma and ongoing recruitment as lung volume increases. We also found in this group that Rrs\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{rs}$$\\end{document} increased progressively during the first 0.5 s of inspiration and then began to decrease again as inspiration continued, which we interpret as corresponding to the point when continuing derecruitment was reversed by progressive lung inflation.ConclusionsThese findings support the hypothesis that sufficiently short expiratory durations protect against atelectrauma because they do not give derecruitment enough time to manifest. This suggests a means for the personalized adjustment of mechanical ventilation.

Gated recurrent unit (GRU)-based deep learning method for spectrum estimation and inverse modeling in plasmonic devices

Gated recurrent unit (GRU)-based deep learning method for spectrum estimation and inverse modeling in plasmonic devices

Hydrochemical Characteristics and Formation Mechanism of Geothermal Fluids in Zuogong County, Southeastern Tibet

Zuogong County is located in the southeast of Tibet, which is rich in hot spring geothermal resources, but its development and utilization degree are low, and the genetic mechanism of the geothermal system is not clear. Hydrogeochemical characteristics of geothermal water are of great significance in elucidating the genesis and evolution of geothermal systems, as well as the sustainable development and utilization of geothermal resources. The hydrogeochemical characteristics and genesis of the geothermal water in Zuogong County were investigated using hydrogeochemical analysis, a stable isotope (δD, δ18O) approach, and an inverse simulation model for water–rock reactions using the PHREEQC. The results indicated that the Zuogong geothermal system is a deep circulation heating type without a magmatic heat source. The chemical types present in the geothermal water from the Zuogong area are HCO3 and HCO3·SO4, and the main cations are Na+ and Ca2+. The groundwater is replenished by atmospheric precipitation and glacier meltwater. The salt content of geothermal water mainly comes from the interaction between water and surrounding rocks during the deep circulation process. The reservoir temperature of geothermal water in Zuogong is 120–176 °C before mixing with non-geothermal water and drops to 62–98 °C after mixing with 58 to 79% of non-geothermal water. According to the proposed conceptual model, geothermal water mainly produces water–rock interaction with aluminosilicate minerals in the deep formation, while in shallow areas it interacts mainly with sulfate minerals. These findings contribute to a better understanding of the geothermal system in Zuogong County, Tibet.

Hydrochemical characterization and health risk assessment of shallow groundwater in a northern coalfield of Anhui Province, China

In a global context, the hydrochemical characteristics of shallow groundwater in coalfields exhibit high degrees of diversity and complexity that are rooted in the intricate interplay of geological variations, diverse climatic conditions, and extensive human activities. The specific types and concentrations of ions, such as Ca2+, Cl−, and NO3−, show stark differences across geographical regions. Given the crucial roles of coalfields as energy suppliers, the potential environmental contamination risks posed by mining activities to groundwater cannot be overlooked as such pollution directly impacts human health and ecological safety. This study focuses on the Huainan coal mining area in northern Anhui Province (China), where shallow groundwater samples were systematically collected and analyzed to determine the hydrochemical characteristics and ascertain the water quality status. By integrating hydrogeochemical analysis techniques with inverse modeling methods, it was revealed that the groundwater in this region is predominantly HCO3-Ca type, exhibiting weak alkaline characteristics. The formation mechanisms are primarily governed by silicate rock weathering and mineral dissolution–precipitation processes, albeit with discernible influences from human activities. PHREEQC simulations were used to further confirm the precipitation tendencies of minerals like calcite, dolomite, and fluorite as well as the significant dissolution characteristics of halite. The inverse modeling pathway analysis reveals specific hydrochemical processes along different paths: paths I and IV are notably dominated by Ca2+ dissolution–precipitation and cation exchange–adsorption processes, whereas paths II and III are closely associated with the precipitation of calcium montmorillonite as well as dissolution of kaolinite, calcite, quartz, and mineral incongruents. Moreover, evaluations based on the entropy-weighted water quality index (EWQI) indicated overall positive trends of the groundwater quality indicators within the Huainan mining area, reflecting the effectiveness of regional water quality management efforts and providing a scientific basis for future water quality protection and improvement strategies. In summary, this study not only deepens our understanding of the groundwater chemistry in the Huainan coal mining area but also underscores the importance of scientifically assessing and managing groundwater resources to address the environmental challenges potentially arising from coal mining activities.

ECOMAN: an open-source package for geodynamic and seismological modelling of mechanical anisotropy

Abstract. Mechanical anisotropy related to rock fabrics is a proxy for constraining the Earth's deformation patterns. However, the forward and inverse modelling of mechanical anisotropy in 3D large-scale domains has been traditionally hampered by the intensive computational cost and the lack of a dedicated, open-source computational framework. Here we introduce ECOMAN (Exploring the COnsequences of Mechanical ANisotropy), a software package for modelling strain- and stress-induced rock fabrics and testing the effects of the resulting elastic and viscous anisotropy on seismic imaging and mantle convection patterns. Differently from existing analogous software, ECOMAN can model strain-induced fabrics across all mantle levels and is optimised to run efficiently on multiple CPUs. It also enables modelling of shape preferred orientation (SPO)-related structures that can be superimposed over lattice/crystallographic preferred orientation (LPO/CPO) fabrics, which allows the consideration of the mechanical effects of fluid-filled cracks, foliated and lineated grain-scale fabrics, and rock-scale layering. One of the most important innovations is the Platform for Seismic Imaging (PSI), a set of programs for performing forward and inverse seismic modelling in isotropic–anisotropic media using real or synthetic seismic datasets. The anisotropic inversion strategy is capable of recovering parameters describing a tilted transversely isotropic (TTI) medium, which is required to reconstruct 3D structures and mantle strain patterns and to validate geodynamic models.

Adaptive visual servoing control for uncalibrated robot manipulator with uncertain dead-zone constraint

This paper addresses the adaptive visual tracking control problem of an uncalibrated camera robot system with unknown dead-zone inputs. The uncertainties include camera extrinsic and intrinsic parameters, robot dynamic parameters, and feature depth parameters. The control achieves a better performance by establishing a smooth inverse model of the dead-zone input, which eliminates the nonlinear effect of the actuator constraint. A novel adaptive control scheme is presented which does not require a priori knowledge of the parameter intervals of dead-zone model, to update the parameter values online. Furthermore, adaptive laws are provided to estimate the uncertainties that exist both in robot and in camera models. Meanwhile, in order to avoid the singularity of the image Jacobian matrix, a projection algorithm is also introduced. Subsequently, a novel robust adaptive controller together with dead-zone compensation is established so that the tracking error image space converges to the origin. Simulations and experimental studies are conducted to verify the effectiveness of the proposed method.

Comparative biomechanical analysis of a conventional/novel hip prosthetic socket.

The aim of this study was to investigate and compare the biomechanical properties of the conventional and novel hip prosthetic socket by using the finite element and gait analysis. According to the CT scan model of the subject's residual limb, the bones, soft tissues, and the socket model were reconstructed in three dimensions by using inverse modeling. The distribution of normal and shear stresses at the residual limb-socket interface under the standing condition was investigated using the finite element method and verified by designing a pressure acquisition module system. The gait experiment compared and analyzed the conventional and novel sockets. The results show that the simulation results are consistent with the experimental data. The novel socket exhibited superior stress performance and gait outcomes compared to the conventional design. Our findings provide a research basis for evaluating the comfort of the hip prosthetic socket, optimizing and designing the structure of the socket of the hip.

Deformation behavior of aluminum alloy rivets for aerospace applications

For many decades, solid rivets made of high-strength aluminum alloys have been used for mechanical joining of aircraft structures. For numerical modeling of riveting processes the detailed description of the deformation behavior of the rivets is of utmost importance; however, only very little reliable material data are available. Therefore, this study reviews and investigates the deformation behavior of commercial MS20426AD3-5 countersunk rivets made of aluminum alloy AA-2117-T4 as typically used in aerospace applications. The self-consistent procedure for determining the material-specific flow curve includes (i) the exact preparation of cylindrical samples, (ii) compression testing of the samples at testing speeds of 0.05 mm/s, 0.5 mm/s and 5 mm/s, and (iii) inverse numerical modeling of the testing procedure. In general, the compliance of the testing setup must be considered for obtaining reliable flow curves, especially when testing small samples. The determined flow curve of aluminum alloy AA-2117-T4 showed higher yield stress and more distinct initial strain hardening than most of the flow curves published in literature. Although the flow curve did not show any significant strain rate dependency, notable softening due to deformation-induced adiabatic heating of the compressed sample was observed at the highest testing speed. However, the fracture strain seems to be strain rate-dependent, because samples deformed at the low testing speed did not show any signs of macroscopic fracture, whereas local fracture occurred in samples deformed at the medium and high testing speeds.

Two-dimensional thermal and dynamical strain in landfast sea ice from InSAR: results from a new analytical inverse method and field observations

Abstract Observing continuous strain in sea ice at geophysical scales of tens of meters to kilometers requires displacement measurements made with millimeter-scale precision. Satellite-based interferometric synthetic aperture radar (InSAR) provides such precise measurements of relative surface displacement over broad spatial areas at regular intervals and, unlike point displacement measurements, it allows confident delineation of continuously deforming regions. However, InSAR only captures the 1-D component of surface displacement parallel to a radar's lines-of-sight. Additional analysis is required to translate between these 1-D observations and the horizontal or vertical displacements they arose from. Previous studies utilize an iterative inverse model to constrain estimates of horizontal surface displacement from InSAR. Here we build upon that work outlining a new analytical inverse modeling method for quantifying displacement and strain over continuous regions of sea ice and provide comparison between model results and independent displacement observations. We demonstrate the inverse method over both landfast and drifting ice along the Alaskan coastline. These intercomparisons highlight environments in which displacements inverted from interferograms may be used as an independent estimator of surface strain, as well as the potential for the outlined inverse methods to be used in conjunction with other observing methods.

A transformer-based convolutional method to model inverse cascade in forced two-dimensional turbulence

The present work proposes a novel transformer-based convolutional neural network (TransCNN) method to effectively model the inverse energy cascade in two dimensional (2D) turbulence. The TransCNN structure combines large-scale features extracted by transformer with small-scale features from convolutional layers, thus is considered suitable for multi-scale modeling. The novel TransCNN method has been applied to model sub-grid scale (SGS) stress for large-eddy simulation (LES) of 2D turbulence, under the extremely challenging situation that the LES grid is too coarse to resolve the external forcing scale. The data-driven model trained by the novel TransCNN structure is compared to two deep CNN models with varying complexities. All models exhibit proficiency during a priori tests. Notably, TransCNN surpasses its counterparts in predictive accuracy and generalizability in a posteriori tests. An investigation into the receptive fields reveals that the TransCNN model can efficiently leverage global information with the transformer structure, which is key to its superior performance in representing the inverse energy cascade in the 2D turbulent simulations.

Association between sleep disturbances and suicidal behavior in adolescents: a systematic review and meta-analysis.

Adolescents' health and well-being are seriously threatened by suicidal behaviors, which have become a severe social issue worldwide. Suicide is one of the leading causes of mortality for adolescents in low and middle-income countries, with approximately 67,000 teenagers committing suicide yearly. Although an association between sleep disturbances (SDs) and suicidal behaviors has been suggested, data are still scattered and inconclusive. Therefore, to further investigate this association, we conducted a meta-analysis to verify if there is a link between SDs and suicidal behaviors in adolescents without diagnosed psychiatric disorders. PubMed, CENTRAL, EMBASE, and PsycINFO were searched from inception to August 30th, 2024. We included studies reporting the estimation of suicidal behaviors in adolescents from 12 to 21 years of age, with SDs and healthy controls. The meta-analysis was based on odds ratio (OR, with a 95% confidence interval ([CI]), estimates through inverse variance models with random-effects. The final selection consisted of 19 eligible studies from 9 countries, corresponding to 628,525 adolescents with SDs and 567,746 controls. We found that adolescents with SDs are more likely to attempt suicide (OR: 3.10; [95% CI: 2.43; 3.95]) and experience suicidal ideation (OR: 2.28; [95% CI 1.76; 2.94]) than controls. This meta-analysis suggests that SDs are an important risk factor for suicidal ideation and suicide attempts in healthy adolescents. The findings highlight the importance of early identification of SDs to prevent suicidal behaviors in this population. PROSPERO, identifier CRD42023415526.

Development and deployment of a mid-cost CO2 sensor monitoring network to support atmospheric inverse modeling for quantifying urban CO2 emissions in Paris

Abstract. To effectively monitor highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. These dense measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or to separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm for hourly mean measurements. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision cavity ring-down spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12:00–17:00 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects for integrating mid-cost measurements along with high-precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area.

Network-level enrichment provides a framework for biological interpretation of machine learning results.

Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.