Single Time Research Articles

A cryptically diverse microbial community drives organic matter decomposition in forests

Despite the critical role of microorganisms in plant and fungal residue decomposition, our understanding of their full diversity remains limited. This is due largely to the rapid microbial succession during decomposition, a scarcity of studies including multiple sampling times, and the omission of a species richness index encompassing all decay stages. To address these gaps, we conducted a meta-analysis of 12 studies, each examining bacterial and fungal communities at multiple time points during decomposition. We aimed to determine the overall microbial diversity involved in decomposition processes by aggregating microbial richness at different time points. By comparing cumulative microbial OTU (operational taxonomic unit) richness with single time point microbial richness, we show that the cumulative richness was 2–5 times greater, indicating that a high yet frequently overlooked diversity of microorganisms is involved in the decomposition process. This pattern was consistent across different organic matter types (plant and fungal residues) for both major microbial domains (bacteria and fungi). Moreover, the appearance rate of novel OTUs generally decreased over time for most organic matter types, except for dead wood, which accumulated new fungal OTUs at a notable pace. Our results collectively emphasize the importance of considering various microbial domains, organic matter types, and time points to successfully characterize the diversity of microorganisms involved in decomposition. Further, given the hidden cumulative number of bacterial and fungal species held within plant and fungal residues across decay stages, we propose that these substrates are crucial microbial reservoirs to include to accurately assess global terrestrial microbial diversity.

Deep Learning for Automated Triaging of Stable Chest Radiographs in a Follow-up Setting.

Background Most artificial intelligence algorithms that interpret chest radiographs are restricted to an image from a single time point. However, in clinical practice, multiple radiographs are used for longitudinal follow-up, especially in intensive care units (ICUs). Purpose To develop and validate a deep learning algorithm using thoracic cage registration and subtraction to triage pairs of chest radiographs showing no change by using longitudinal follow-up data. Materials and Methods A deep learning algorithm was retrospectively developed using baseline and follow-up chest radiographs in adults from January 2011 to December 2018 at a tertiary referral hospital. Two thoracic radiologists reviewed randomly selected pairs of "change" and "no change" images to establish the ground truth, including normal or abnormal status. Algorithm performance was evaluated using area under the receiver operating characteristic curve (AUC) analysis in a validation set and temporally separated internal test sets (January 2019 to August 2021) from the emergency department (ED) and ICU. Threshold calibration for the test sets was conducted, and performance with 40% and 60% triage thresholds was assessed. Results This study included 3 304 996 chest radiographs in 329 036 patients (mean age, 59 years ± 14 [SD]; 170 433 male patients). The training set included 550 779 pairs of radiographs. The validation set included 1620 pairs (810 no change, 810 change). The test sets included 533 pairs (ED; 265 no change, 268 change) and 600 pairs (ICU; 310 no change, 290 change). The algorithm had AUCs of 0.77 (validation), 0.80 (ED), and 0.80 (ICU). With a 40% triage threshold, specificity was 88.4% (237 of 268 pairs) and 90.0% (261 of 290 pairs) in the ED and ICU, respectively. With a 60% triage threshold, specificity was 79.9% (214 of 268 pairs) and 79.3% (230 of 290 pairs) in the ED and ICU, respectively. For urgent findings (consolidation, pleural effusion, pneumothorax), specificity was 78.6%-100% (ED) and 85.5%-93.9% (ICU) with a 40% triage threshold. Conclusion The deep learning algorithm could triage pairs of chest radiographs showing no change while detecting urgent interval changes during longitudinal follow-up. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Czum in this issue.

Time–frequency domain based optimization of hedging strategy: Evidence from CSI 500 spot and futures

Considering the significance of the frequency domain information in price series, we propose a decomposition-integration hedging model from the perspective of time–frequency domain to improve the hedging effectiveness and robustness. First, the returns series of spot and futures are decomposed and reconstructed into high frequency, medium frequency, and low frequency sequences to incorporate the frequency information into the hedging model. Then, we construct a model pool to fit the various characteristics of the data in each frequency domain and further extract the corresponding information. Finally, by taking four moments of investors risk appetite into consideration, the multi-objective optimization is employed to integrate the final hedging ratio. The empirical results indicate that the decomposition-integration hedging model is superior to the single time domain models in comprehensive evaluation indices. In addition, within a certain range of market fluctuations, the structure of our model has some characteristics that are compatible with reality. To recapitulate briefly, the hedging ratio of our model is mainly determined by the high frequency and medium frequency information under drastic market fluctuations. While when the market volatility is small, the hedging ratio largely depends on low frequency information.

Dosimetry Reconstruction in Radiopharmaceutical Therapy Using a Sparse Network of External γ-Sensors

Radiopharmaceutical therapy (RPT) has demonstrated promise in the treatment of neuroendocrine and prostate cancer. Due to the highly varied biodistribution and non-homogeneity of total integrated dose across cancer patients, a system for real-time dosimetry based on continuous measurement is desirable to deliver sufficient dose for tumor ablation while preventing toxicity from off-target uptake by organs at risk (OAR). Single time point imaging (mostly SPECT)-based dosimetry offers a snapshot of the body-wide dose distribution at a given time point, but even single SPECT imaging is generally limited in availability, often leading to significant inaccuracies in estimating total integrated dose. Therefore, accurate personalized dosimetry in RPT is an unmet need and requires continuous dosimetry measurements of tumors and OARs across multiple half-lives of the therapeutic radiopharmaceutical. Using a priori knowledge of tumor and OAR locations from pretherapy imaging, we have developed a novel algorithm that utilizes a network of custom uncollimated, optical fiber-based γ-counting probes to isolate the real-time in vivo tumor and OAR uptake in 177Lu-PSMA-617 and 225Ac-MACROPA-YS5 therapy. The proposed system was successfully validated in athymic mice models bearing varying numbers of tumors from two human prostate cancer cell lines (PC3-pip, PC3-flu). Uncollimated γ counts using the developed probes were acquired outside of the mice for 10 minutes, starting at 0 hr, 6 hrs, 12 hrs, 24 hrs, and 48 hrs after the injection of 177Lu-PSMA-617. The percent injected activity per mL of tissue (%IA/mL) of each tumor and OAR was reconstructed at every time point and compared to the %IA/mL extracted from SPECT/CT immediately after the recording. The developed system's %IA/mL reconstruction in PC3-pip tumors, PC3-flu tumors, kidneys, and bladders is highly correlative with the %IA/mL extracted from state-of-art in vivo dosimetry techniques, with %IA/mL ranging from 0.1% to 160% assuming a 29.6 MBq 177Lu-PSMA-617 administration. The least squares linear regression fit between the reconstructed activity and the activity measured from SPECT/CT is given by Estimated %IA/mL = 0.91 x SPECT %IA/mL, with an R2 of 0.991, and Pearson's r of 0.9975. There is a nearly 1:1 mapping between the proposed model and SPECT/CT. A novel dose reconstruction algorithm for personalized dosimetry in RPT that utilizes a sparse set of external γ-counters and a priori knowledge of tumor and OAR locations was developed and validated in in vivo human prostate cancer murine models. The proposed system enables continuous dosimetry measurements of multiple tumors and OAR noninvasively, with high accessibility, high temporal resolution, and across multiple classes of ɑ and β-based RPT. Similar experiments using 225Ac-MACROPA-YS5 are ongoing and additional results will be reported.

Vibration response and safety control for blasting vibration of the existing tunnel with defects

Current studies on blasting construction of small clear-distance tunnels have not considered the impact of existing tunnel lining defects when establishing safety controls. This paper offers a series of study results based on the blasting project of a new tunnel adjacent to the existing defect Xinling tunnel to thoroughly examine the dynamic response, safety control standards, and measures of the existing defect tunnel. First, structural models were developed to investigate the influence of the presence or absence of specific defects (like lining cracks and cavities behind the lining) on the dynamic response of the current tunnel lining to identify the most unfavorable defect distribution. Then, establish safety control standards for intact linings and those with the most unfavorable defects. Eventually, two types of control measures, single safe charge and reasonable delay time, were studied based on the established safety control standards. In particular, the most adverse position of cracks was the wall facing the explosion, the rise in depth was more unfavorable for vibration response, and the impact of the longitudinal crack was restricted to the vicinity of the crack. While the vault was the most adverse cavity position, the rise in cavity area was more damaging, and the influence range varied with longitudinal cavity length. Moreover, the impact of cracks was mainly evident in the amplification effect of stress at the crack region. In contrast, cavities had varied degrees of amplification effects on the vibration velocity and stress response and a relatively extensive influence range. Safety control research was conducted, when the tunnel was intact, with a right wall crack, a vault cavity, and both vault cavity and crack for this project, the peak particle velocity (PPV) of the safety control standard for vibration velocity was 13, 10, 13, and 8 cm/s, respectively, and the respective single safe charge could be adjusted at 64, 53, 37, and 25 kg. However, the presence of different defects had a relatively negligible effect on the reasonable delay time; 25 ms was recommended for existing tunnel lining with and without the defect.

A Vector Field Visualization Method for Trajectory Big Data

With the rapid growth of trajectory big data, there is a need for more efficient methods to extract, analyze, and visualize these data. However, existing research on trajectory big data visualization mainly focuses on displaying trajectories for a specific period or showing spatial distribution characteristics of trajectory points in a single time slice using clustering, filtering, and other techniques. Therefore, this paper proposes a vector field visualization model for trajectory big data, aiming to effectively represent the inherent movement trends in the data and provide a more intuitive visualization of urban traffic congestion trends. The model utilizes the motion information of vehicles to create a travel vector grid and employs WebGL technology for vector field visualization rendering. The vector field effects are effectively displayed by generating many particles and simulating their movements. Furthermore, this research also designs and implements congestion trend point identification and hotspot congestion analysis, thus validating the practicality and effectiveness of trajectory big data vector field visualization. The results indicate that compared to traditional visualization methods, the vector field visualization method can demonstrate the direction and density changes in traffic flow and predict future traffic congestion. This work provides valuable data references and decision support for urban traffic management and planning.

A-108 Validation of Plasma Oxalate Measurement on the Roche Cobas c501

Abstract Background Measurement of plasma oxalate is important in patients with renal failure as oxalate may build up due to impaired excretion and lead to organ damage due to precipitation of calcium oxalate crystals. However, the measurement of plasma oxalate is challenging due to the low concentrations present in individuals with healthy kidney function and the pre-analytical steps needed to prevent erroneous oxalate results due to conversion from ascorbate. In order to provide improved turn-around-time for plasma oxalate testing, our objective was to develop and validate a method to measure plasma oxalate on the Roche cobas c501. Methods Samples used in the validation study included both fresh collections and residual plasma from specimens collected in lithium heparin, no-gel tubes. Samples from fresh collections were kept on ice prior to centrifugation, and then frozen if not analyzed immediately. Thawed plasma was acidified by addition of 12.1 M HCl to a pH between 2 and 3 and mixed thoroughly by vortexing. One milliliter of the acidified plasma was added to a Centrifree filter (Millipore). The samples were then centrifuged for 90 min at 1500 × g 20°C in a fixed angle rotor to remove plasma proteins. The resulting filtrate was assayed on the Roche cobas c501. The assay (Trinity Biotech) uses oxalate oxidase to produce an indamine dye through the production of hydrogen peroxide from oxalate in the sample, which is measured spectrophotometrically at 600 nm at a single time point. The analytical measuring range (AMR) was evaluated by performing serial dilutions of a high sample. Interferences from hemolysis, icterus, and lipemia were assessed at both low and high oxalate concentrations. Precision was evaluated using two QC concentrations, and patient samples with low and high concentrations of oxalate. Samples were tested ten times in a single day, or twice per day for ten days to estimate intra- and inter-precision, respectively. Spike recovery studies were used to assess method accuracy. Lastly, reference intervals were verified by assaying samples from twenty healthy volunteers. Total allowable error was ±30% or 2 µmol/L with a maximum 15%CV. Results The assay was validated to be linear across an AMR of 2 to 30 µmol/L, with an average recovery of 105% across five concentrations. The total assay imprecision was <15%. Initial spike recovery studies across the AMR showed an average recovery of 99.4% (n = 12), with a range of 76.6 to 132.4%. In a mixing study, hemolyzed and lipemic sample recovery was on average 82.1% and 76.1% respectively, which was low, but acceptable. Samples mixed with icteric specimens demonstrated unacceptably low recovery (average of 64.1%). The reference interval of <2 µmol/L was successfully verified as all twenty healthy volunteers had results <2 µmol/L. Conclusion Initial validation studies support the clinical utility of the newly developed method. Plasma oxalate methods are documented to have significant inter-method variability and variable recovery, which is hypothesized to be due to variation at the filtration step. This semi-automated assay will require less technologist time and will reduce the possibility for human error affecting the result.

Healing from intergenerational trauma: narratives of connection, belonging, and truth-telling in two Aboriginal healing camps

ABSTRACT Addressing intergenerational trauma caused through the impacts of colonization requires healing processes that are specific to the experiences and needs of First Nations peoples. This paper details an evaluation of two Aboriginal healing camps held in South Australia in 2021 and 2022. The camps focused on supporting members of the Stolen Generations, through a combination of First Nations and western healing practices. This paper details the framework used to structure the camps and provides an analysis of interviews with attendees. For the 2021 camp, nine attendees were interviewed before the camp and eight took part in a follow-up interview after the camp. For the 2022 camp, four attendees took part in a single time point interview. Thematic analysis of the interviews resulted in the development of five interrelated themes, focused on connections, belonging, healing, and truth-telling. The paper concludes by considering what the findings suggest for institutional change and growth in terms of future iterations of the camps, and the importance of First Nations-led opportunities for healing from intergenerational trauma.

Hemodynamics in AVF over time: A protective role of vascular remodeling toward flow stabilization.

The mechanisms underlying vascular stenosis formation in the arteriovenous fistula (AVF) for hemodialysis (HD) remain mostly unknown. Several computational fluid dynamics (CFD) studies have suggested a potential role for unsteady flow in inducing intimal hyperplasia and AVF stenosis, but the majority of these observations have been limited to a single time point after surgical creation. The aim of the present study was to investigate the relation between hemodynamic conditions and AVF vascular remodeling through a CFD longitudinal study. Non contrast-enhanced MR images and Doppler Ultrasound (US) examinations were acquired at 3 days, 40 days, 6 months, 1 year, and 1.5 years after surgery in a 72-year male referred for native radio-cephalic AVF. Three-dimensional AVF models were generated and high fidelity CFD simulations were performed using pimpleFoam, setting patient-specific boundary conditions derived from US. Morphological and hemodynamic changes over time were then analyzed. Analysis of vessel morphology and hemodynamics during follow-up showed that the AVF had a successful maturation process, characterized by a massive arterial and venous dilatation within the 6 months after surgery, a corresponding increase in blood flow volume and important flow instabilities. Between 6 months and 1 year, a stenosis developed in the juxta-anastomotic vein and caused AVF failure at 1.5 years. The development of stenosis was paralleled by the regularization of blood flow velocity pattern and consequent decrease in the near-wall disturbed flow metrics. These results suggest that development of intimal hyperplasia and vessel stenosis, triggered by unsteady flow, could be the result of vascular inward remodeling toward regularization of turbulent-like flow.

Comparative assessment of Alzheimer's disease-related biomarkers in plasma and neuron-derived extracellular vesicles: a nested case-control study.

Introduction: Alzheimer's disease (AD) is currently defined according to biomarkers reflecting the core underlying neuropathological processes: Aβ deposition, Tau, and neurodegeneration (ATN). The soluble phase of plasma and plasma neuron-derived extracellular vesicles (NDEVs) are increasingly being investigated as sources of biomarkers. The aim of this study was to examine the comparative biomarker potential of these two biofluids, as well as the association between respective biomarkers. Methods: We retrospectively identified three distinct diagnostic groups of 44 individuals who provided samples at baseline and at a mean of 3.1years later; 14 were cognitively unimpaired at baseline and remained so (NRM-NRM), 13 had amnestic MCI that progressed to AD dementia (MCI-DEM) and 17 had AD dementia at both timepoints (DEM-DEM). Plasma NDEVs were isolated by immunoaffinity capture targeting the neuronal markers L1CAM, GAP43, and NLGN3. In both plasma and NDEVs, we assessed ATN biomarkers (Aβ42, Aβ40, total Tau, P181-Tau) alongside several other exploratory markers. Results: The Aβ42/Aβ40 ratio in plasma and NDEVs was lower in MCI-DEM than NRM-NRM at baseline and its levels in NDEVs decreased over time in all three groups. Similarly, plasma and NDEV-associated Aβ42 was lower in MCI-DEM compared to NRM-NRM at baseline and its levels in plasma decreased over time in DEM-DEM. For NDEV-associated proBDNF, compared to NRM-NRM, its levels were lower in MCI-DEM and DEM-DEM at baseline, and they decreased over time in the latter group. No group differences were found for other exploratory markers. NDEV-associated Aβ42/Aβ40 ratio and proBDNF achieved the highest areas under the curve (AUCs) for discriminating between diagnostic groups, while proBDNF was positively associated with Mini-Mental State Examination (MMSE) score. No associations were found between the two biofluids for any assessed marker. Discussion: The soluble phase of plasma and plasma NDEVs demonstrate distinct biomarker profiles both at a single time point and longitudinally. The lack of association between plasma and NDEV measures indicates that the two types of biofluids demonstrate distinct biomarker signatures that may be attributable to being derived through different biological processes. NDEV-associated proBDNF may be a useful biomarker for AD diagnosis and monitoring.

A novel method for maize leaf disease classification using the RGB-D post-segmentation image data.

Maize (Zea mays L.) is one of the most important crops, influencing food production and even the whole industry. In recent years, global crop production has been facing great challenges from diseases. However, most of the traditional methods make it difficult to efficiently identify disease-related phenotypes in germplasm resources, especially in actual field environments. To overcome this limitation, our study aims to evaluate the potential of the multi-sensor synchronized RGB-D camera with depth information for maize leaf disease classification. We distinguished maize leaves from the background based on the RGB-D depth information to eliminate interference from complex field environments. Four deep learning models (i.e., Resnet50, MobilenetV2, Vgg16, and Efficientnet-B3) were used to classify three main types of maize diseases, i.e., the curvularia leaf spot [Curvularia lunata (Wakker) Boedijn], the small spot [Bipolaris maydis (Nishik.) Shoemaker], and the mixed spot diseases. We finally compared the pre-segmentation and post-segmentation results to test the robustness of the above models. Our main findings are: 1) The maize disease classification models based on the pre-segmentation image data performed slightly better than the ones based on the post-segmentation image data. 2) The pre-segmentation models overestimated the accuracy of disease classification due to the complexity of the background, but post-segmentation models focusing on leaf disease features provided more practical results with shorter prediction times. 3) Among the post-segmentation models, the Resnet50 and MobilenetV2 models showed similar accuracy and were better than the Vgg16 and Efficientnet-B3 models, and the MobilenetV2 model performed better than the other three models in terms of the size and the single image prediction time. Overall, this study provides a novel method for maize leaf disease classification using the post-segmentation image data from a multi-sensor synchronized RGB-D camera and offers the possibility of developing relevant portable devices.

Timing and level of educational attainment and late-life cognition in the KHANDLE study.

The timing of educational attainment may modify its effects on late-life cognition, yet most studies evaluate education only at a single time point. Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) Study cohort participants (N=554) reported educational attainment (dichotomized at any college education) at two time points, and we classified them as having low, high, or later-life high educational attainment. Linear mixed-effects models estimated associations between educational attainment change groups and domain-specific cognitive outcomes (z-standardized). Compared to low educational attainment, high (β=0.59 SD units; 95% confidence interval [CI]: 0.39, 0.79) and later-life high educational attainment (β =0.22; 95% CI: 0.00, 0.44) were associated with higher executive function. Only high educational attainment was associated with higher verbal episodic memory (β = 0.27; 95% CI: 0.06, 0.48). Level and timing of educational attainment are both associated with domain-specific cognition. A single assessment for educational attainment may inadequately characterize protective associations with late-life cognition. Few studies have examined both level and timing of educational attainment on cognition. Marginalized populations are more likely to attain higher education in adulthood. Higher educational attainment in late life is also associated with higher cognition.

Open-set lung sound recognition model based on conditional Gaussian capsule network and variational time–frequency feature reconstruction

Lung sound auscultation is an essential method for diagnosing lung diseases; however, most existing lung sound recognition methods fail to identify classes that are unknown in training. Thus, we proposed an open-set lung sound recognition model based on the conditional Gaussian capsule network and variational time–frequency feature reconstruction. The proposed model incorporates an inference network, a cubic encoder, an attention module, a classifier, a cubic decoder, and a generative network. First, the inference network is employed to extract the time–frequency features of lung sounds at a single time step. Then, the variational distribution of lung sounds is computed using the capsule network and optimized to approximate the Gaussian model of the class to which the sample belongs according to the labels. Time–frequency synchronized feature extraction and reconstruction are performed on the entire lung sound sample using the cubic encoder and cubic decoder. Finally, we utilize the generative network to refactor the lung sound features for open-set recognition. The proposed model was evaluated experimentally on a combined dataset using two different category assignment schemes. The results demonstrate that the proposed model achieved accuracies of 82.31% and 88.47%, respectively, thereby outperforming existing methods.

Reporting chronic kidney disease in Greenland

ABSTRACT Background: Chronic kidney disease (CKD) is a major health burden affecting more than 10% of the global population. It is a multifactorial disease with many risk factors attributed lifestyle diseases. The prevalence of CKD in Greenland is unknown; however, the prevalence of risk factors contributing to CKD is increasing. Objectives: To estimate the prevalence of CKD in Greenland. Methods: The study was a cross-sectional register-study including all Greenlandic residents aged ≥20 years with serum creatinine analysis within the last 2 years. We identified those with CKD based on eGFR and UACR and those registered with a CKD diagnosis code. Two limitations of the study are possible lack of data completeness and the reliance of a single time point to report CKD. Results: A total of 2,157 patients were identified with CKD with an age-standardised prevalence of 3.01%. Only 75 patients were registered with a diagnosis code for CKD. Approximately 80% of patients were classified with CKD stages 1–2. Conclusion: This is the first study reporting CKD in Greenland. We found a lower prevalence of CKD than reported by other studies, and a low number of patients correctly diagnosed with CKD. We call for increased awareness and diagnosis coding of CKD in Greenland.

Estimating the Lambda measure in multiple-merger coalescents

Multiple-merger coalescents, also known as Λ-coalescents, have been used to describe the genealogy of populations that have a skewed offspring distribution or that undergo strong selection. Inferring the characteristic measure Λ, which describes the rates of the multiple-merger events, is key to understand these processes. So far, most inference methods only work for some particular families of Λ-coalescents that are described by only one parameter, but not for more general models. This article is devoted to the construction of a non-parametric estimator of the density of Λ that is based on the observation at a single time of the so-called Site Frequency Spectrum (SFS), which describes the allelic frequencies in a present population sample. First, we produce estimates of the multiple-merger rates by solving a linear system, whose coefficients are obtained by appropriately subsampling the SFS. Then, we use a technique that aggregates the information extracted from the previous step through a kernel type of re-construction to give a non-parametric estimation of the measure Λ. We give a consistency result of this estimator under mild conditions on the behavior of Λ around 0. We also show some numerical examples of how our method performs.

Can developmental trajectories in gait variability provide prognostic clues in motor adaptation among children with mild cerebral palsy? A retrospective observational cohort study.

To investigate whether multiple domains of gait variability change during motor maturation and if this change over time could differentiate children with a typical development (TDC) from those with cerebral palsy (CwCP). This cross-sectional retrospective study included 42 TDC and 129 CwCP, of which 99 and 30 exhibited GMFCS level I and II, respectively. Participants underwent barefoot 3D gait analysis. Age and parameters of gait variability (coefficient of variation of stride-time, stride length, single limb support time, walking speed, and cadence; as well as meanSD for hip flexion, knee flexion, and ankle dorsiflexion) were used to fit linear models, where the slope of the models could differ between groups to test the hypotheses. Motor-developmental trajectories of gait variability were able to distinguish between TDC and CwCP for all parameters, except the variability of joint angles. CwCP with GMFCS II also showed significantly higher levels of gait variability compared to those with GMFCS I, these levels were maintained across different ages. This study showed the potential of gait variability to identify and detect the motor characteristics of high functioning CwCP. In future, such trajectories could provide functional biomarkers for identifying children with mild movement related disorders and support the management of expectations.

Multi-time Step Deterioration Prediction of Freeways Using Linear Regression and Machine Learning Approaches: A Case Study

AbstractMulti-time step deterioration prediction of road pavements can provide more options for effective maintenance and rehabilitation decision under limited resources as compared to single time step prediction. This paper presents the 1–4 time step ahead prediction of the pavement cracking, rutting and roughness using their past values together with climate and traffic data as model inputs. Three prediction models were adopted, including the simple multiple linear regression (MLR) model and two sophisticated machine learning models, namely, support vector regression (SVR) and genetic programming (GP) models. An industry dataset of spray seal freeways is used to demonstrate the application of the methodology developed in this study. After calibration with observed data, the three prediction models are tested with unseen datasets using three performance indicators, namely, mean squared error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R squared). Among many results, all three models are unable to predict cracking with acceptable prediction accuracy. On the other hand, the rutting and roughness can be predicted with relatively good accuracy up to 4 time steps ahead. The sensitivity analysis shows that roughness and rutting prediction depends significantly on their previous or lagged values and not on remaining inputs such as annual average daily traffic (AADT) and rainfall. The methodology developed in this study is also applied to another dataset of asphalt freeways, which have similar model inputs. Similar findings are found with this dataset of asphalt freeways to that of spray seal freeways. The simple MLR model can produce similar prediction performance to the sophisticated SVR and GP models for rutting and roughness, suggesting the use of the MLR model as a benchmark for any development of prediction models for pavement deterioration.

Microwave Absolute Distance Measurement Method with Ten-Micron-Level Accuracy and Meter-Level Range Based on Frequency Domain Interferometry.

A microwave absolute distance measurement method with ten-micron-level accuracy and meter-level range based on frequency domain interferometry is proposed and experimentally demonstrated for the first time. Theoretical analysis indicates that an interference phenomenon occurs instantaneously in the frequency domain when combining two homologous broad-spectrum microwave beams with different paths, and the absolute value of the distance difference between the two paths is only inversely proportional to the period of frequency domain interference fringes. The proof-of-principle experiments were performed to prove that the proposed method can achieve absolute distance measurement in the X-band with standard deviations of 15 μm, 17 μm, and 26 μm and within ranges of 1.69 m, 2.69 m, and 3.75 m. Additionally, a displacement resolution of 100 microns was realized. The multi-target recognition performance was also verified in principle. Furthermore, at the expense of a slight decrease in ranging accuracy, a fast distance measurement with the single measurement time of 20 μs was achieved by using a digitizer combined with a Fourier transform analyzer. Compared with the current microwave precision ranging technologies, the proposed method not only has the advantages of high precision, large range, and rapid measurement capability, but the required components are also easily obtainable commercial devices. The proposed method also has better complex engineering applicability, because the ten-micron-level ranging accuracy is achievable only by using a simple Fourier transform without any phase estimation algorithm, which greatly reduces the requirement for signal-to-noise ratio.

Selecting (or not) physical education as an elective subject: Spanish high school students’ views

ABSTRACT Background: The interactions between behavior, environment and personal factors are the basis of the Social Cognitive Theory (Bandura, A. 1986. Social Foundations of Thought and Action. NJ: Prentice Halls). Within this theory, students’ behavior and motivation toward an activity is dependent on their feelings, thoughts and beliefs (Lodewyck and Pybus 2013. “Investigating Factors in the Retention of Students in High School Physical Education.” Journal of Teaching in Physical Education 32 (1): 61–77). Physical education can provide attractive contexts to promote students’ learning, but this depends on the teachers’ ability to offer them relevant educational experiences (Bailey, R. 2018. “Sport, Physical Education and Educational Worth.” Educational Review 70 (1): 51–66). Meaningful physical education is a pedagogical approach whose aim is to help teachers design significant experiences for all students (Beni, S., T. Fletcher, and D. Ní Chróinín. 2022. “‘It’s not a Linear Thing; There are a Lot of Intersecting Circles’: Factors Influencing Teachers’ Implementation of Meaningful Physical Education.” Teaching and Teacher Education 117: 103806). Physical education is a compulsory subject in Spain, but it is not included in the final year of high school. In 2018, one autonomous community decided to offer it as an elective subject in this course. Scholars and institutions claim for more hours of physical education. But, what about the students? Do they really want more? Purpose: The goal of the present study was to uncover the reasons why students selected, or not, physical education when it was included as an elective subject in their final year of high school. Participants and settings: A total of 795 year-13 students (17–18 years), enrolled in 13 different high schools participated. 444 (55.8%) had not selected physical education (61.71% females), while 351 (44.2%) did selected it (44.44% females). Convenience sampling was used to include participants, considering the possibilities of the researchers to access them. Research design: The study followed an ex post facto, transversal, prospective research design to gather data in a single time point (Cohen, L., K. Manion, and K. Morrison. 2011. Research Methods in Education. Routledge). Within this framework, a qualitative research design was selected to obtain rich, detailed, and heavily contextualized information from the source (Creswell, J. W. 2013. Qualitative Inquiry and Research Design: Choosing among Five Approaches. Sage). The participants responded to an open-ended question 3–4 weeks after the beginning of the school year to ‘fully explain the reason(s) for (not) selecting physical education in the final year of high school’. The qualitative software package MAXQDA 11 was used to help with data administration. Results: The deep analysis of the participants’ responses produced five themes for not selecting physical education (from strong to weak): academic stress, negative experiences, out-of-school physical activity, timetable, and importance, and four themes for selecting it: grade point average, importance, sport, and positive experiences. Conclusions: Results indicate that many students would eliminate physical education from their timetables if they had the chance. In many cases, it does not provide meaningful experiences, which produces low domain value. If physical education wants to survive as an academic subject in the schools, it must listen to its students, and attend their demands.

Differential Susceptibility to Benzo[a]pyrene Exposure during Gestation and Lactation in Mice with Genetic Variations in the Aryl Hydrocarbon Receptor and Cyp1 Genes.

Polycyclic aromatic hydrocarbons are ubiquitous air pollutants, with additional widespread exposure in the diet. PAH exposure has been linked to adverse birth outcomes and long-term neurological consequences. To understand genetic differences that could affect susceptibility following developmental exposure to polycyclic aromatic hydrocarbons, we exposed mice with variations in the aryl hydrocarbon receptor and the three CYP1 enzymes from gestational day 10 (G10) to weaning at postnatal day 25 (P25). We found unexpectedly high neonatal lethality in high-affinity AhrbCyp1b1(-/-) knockout mice compared with all other genotypes. Over 60% of BaP-exposed pups died within their first 5 days of life. There was a significant effect of BaP on growth rates in surviving pups, with lower weights observed from P7 to P21. Again, AhrbCyp1b1(-/-) knockout mice were the most susceptible to growth retardation. Independent of treatment, this line of mice also had impaired development of the surface righting reflex. We used high-resolution mass spectrometry to measure BaP and metabolites in tissues from both dams and pups. We found the highest BaP levels in adipose from poor-affinity AhrdCyp1a2(-/-) dams and identified three major BaP metabolites (BaP-7-OH, BaP-9-OH, and BaP-4,5-diol), but our measurements were limited to a single time point. Future work is needed to understand BaP pharmacokinetics in the contexts of gestation and lactation and how differential metabolism leads to adverse developmental outcomes.