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Understanding the timely regulation of plant growth and phenology is crucial for assessing a terrestrial ecosystem's productivity and carbon budget. The circadian clock, a system of genetic oscillators, acts as 'Master of Ceremony' during plant physiological processes. The mechanism is particularly elusive in trees despite its relevance. The primary and secondary tree growth, leaf senescence, bud set, and bud burst timing were investigated in 68 constructs transformed into Populus hybrids and compared with untransformed or transformed controls grown in natural or controlled conditions. The results were analyzed using generalized additive models with ordered-factor-smooth interaction smoothers. This meta-analysis shows that several genetic components are associated with the clock. Especially core clock-regulated genes affected tree growth and phenology in both controlled and field conditions. Our results highlight the importance of field trials and the potential of using the clock to generate trees with improved characteristics for sustainable silviculture (e.g., reprogrammed to new photoperiodic regimes and increased growth).

• Understanding isotope fractionation mechanisms is fundamental for analyses of plant ecophysiology and paleoclimate based on tree-ring isotope data.
• To gain new insights into isotope fractionation, we analysed intramolecular ¹³C discrimination in tree-ring glucose (Δ_i', i = C-1 to C-6) and metabolic deuterium fractionation at H¹ and H² (ε_met) combinedly. This dual-isotope approach was used for isotope-signal deconvolution.
• We found evidence for metabolic processes affecting Δ₁' and Δ₃', which respond to air vapour pressure deficit (VPD), and processes affecting Δ₁' and Δ₃', and ε_met, which respond to precipitation but not VPD. These relationships exhibit change points dividing a period of homeostasis (1961–1980) from a period of metabolic adjustment (1983–1995). Homeostasis may result from sufficient groundwater availability. Additionally, we found Δ₅' and Δ₆' relationships with radiation and temperature, which are temporally stable and consistent with previously proposed isotope fractionation mechanisms.
• Based on the multitude of climate covariables, intramolecular carbon isotope analysis has a remarkable potential for climate reconstruction. While isotope fractionation beyond leaves is currently considered to be constant, we propose significant parts of the carbon and hydrogen isotope variation in tree-ring glucose originate in stems (precipitation-dependent signals). As basis for follow-up studies, we propose mechanisms introducing Δ₁', Δ₂', Δ₃', and ε_met variability.

This paper introduces a novel ridgelet transform-based method for Poisson image denoising. Our work focuses on harnessing the Poisson noise's unique non-additive and signal-dependent properties, distinguishing it from Gaussian noise. The core of our approach is a new thresholding scheme informed by theoretical insights into the ridgelet coefficients of Poisson-distributed images and adaptive thresholding guided by Stein's method. We verify our theoretical model through numerical experiments and demonstrate the potential of ridgelet thresholding across assorted scenarios. Our findings represent a significant step in enhancing the understanding of Poisson noise and offer an effective denoising method for images corrupted with it.

Low-count positron emission tomography (PET) data suffer from high noise levels, leading topoor image quality and reduced diagnostic accuracy. Compressed sensing (CS) based denoisingmethods have shown potential in medical imaging. This study investigates the performance ofCS-based denoising methods on PET sinograms.Three simulated datasets were used in this study, including circular phantom, patient pelvisphantom, and patient brain phantom. Ten sampling levels were employed to investigate the effect of data reduction on diagnostic accuracy. CS-based denoising methods were applied prereconstruction, and a conventional Gaussian post-filter was used for comparison. Performancemeasures included rRMSE, SSIM, SNR, line profiles, and FWHM.Overall, the proposed CS-based denoising methods performed similarly to the benchmark interms of lesion contrast, spatial resolution, and noise texture. The proposed methods outperformed the benchmark in low-count situations by suppressing background noise and preservingcontrast better.The results of this study demonstrate that CS-based denoising methods in the sinogram domain can improve the quality of low-count PET images, particularly in suppressing backgroundnoise and preserving contrast. These findings suggest that CS-based denoising could be apromising solution for improving the diagnostic accuracy of low-count PET data.

This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains. Inhomogeneous Markov chain model and stratified Cox model were adopted to account for the time-varying risks of train delays. The inhomogeneous Markov chain modelling used covariates weather variables, train operational direction, and findings from the primary delay analysis through stratified Cox model. The results showed that temperature, snow depth, ice/snow precipitation, and train operational direction significantly impacted the arrival delay. Further, by partitioning the train line into three segments as per transition intensity, the model identified that the middle segment had the highest chance of a transfer from punctuality to delay, and the last segment had the lowest probability of recovering from delayed state. The performance of the fitted inhomogeneous Markov chain model was evaluated by the walk-forward validation method, which indicated that approximately 9% of trains may be misclassified as having arrival delays by the fitted model at a measuring point on the train line. With the model performance, the fitted model could be beneficial for both travellers to plan their trips reasonably and railway operators to design more efficient and wiser train schedules as per weather condition.

Objective: In this study, the aim was to compare the performance of four spatiotemporal decomposition algorithms (stICA, stJADE, stSOBI, and sPCA) and parameters for identifying single motor units in human skeletal muscle under voluntary isometric contractions in ultrafast ultrasound image sequences as an extension of a previous study. The performance was quantifed using two measures: (1) the similarity of components’ temporal characteristics against gold standard needle electromyography recordings and (2) the agreement of detected sets of components between the diferent algorithms.

Results: We found that out of these four algorithms, no algorithm signifcantly improved the motor unit identifcation success compared to stICA using spatial information, which was the best together with stSOBI using either spatialor temporal information. Moreover, there was a strong agreement of detected sets of components between the different algorithms. However, stJADE (using temporal information) provided with complementary successful detections. These results suggest that the choice of decomposition algorithm is not critical, but there may be a methodological improvement potential to detect more motor units

Explicitly using the block structure of the unknown signal can achieve better reconstruction performance in compressive sensing. An unknown signal with block structure can be accurately recovered from under-determined linear measurements provided that it is sufficiently block sparse. However, in practice, the block sparsity level is typically unknown. In this paper, we propose a soft measure of block sparsity k_α(x) = (||x||_2,α/||x||_2,1) ^α/(1−α) with α ∈ [0,∞], and present a procedure to estimate it by using multivariate centered isotropic symmetric α-stable random projections. The limiting distribution of the estimator is given. Simulations are conducted to illustrate our theoretical results.

• Stable isotope abundances convey valuable information about plant physiological processes and underlying environmental controls. Central gaps in our mechanistic understanding of hydrogen isotope abundances impede their widespread application within the plant and biogeosciences.
• To address these gaps, we analysed intramolecular deuterium abundances in glucose of Pinus nigra extracted from an annually resolved tree-ring series (1961–1995).
• We found fractionation signals (i.e. temporal variability in deuterium abundance) at glucose H¹ and H² introduced by closely related metabolic processes. Regression analysis indicates that these signals (and thus metabolism) respond to drought and atmospheric CO₂ concentration beyond a response change point. They explain ≈ 60% of the whole-molecule deuterium variability. Altered metabolism is associated with below-average yet not exceptionally low growth.
• We propose the signals are introduced at the leaf level by changes in sucrose-to-starch carbon partitioning and anaplerotic carbon flux into the Calvin–Benson cycle. In conclusion, metabolism can be the main driver of hydrogen isotope variation in plant glucose.

The purpose of this work is to investigate spatial statistical modelling approaches to improve contrast agent quantification in dynamic contrast enhanced MRI, by utilising the spatial dependence among image voxels. Bayesian hierarchical models (BHMs), such as Besag model and Leroux model, were studied using simulated MRI data. The models were built on smaller images where spatial dependence can be incorporated, and then extended to larger images using the maximum a posteriori (MAP) method. Notable improvements on contrast agent concentration estimation were obtained for both smaller and larger images. For smaller images: the BHMs provided substantial improved estimates in terms of the root mean squared error (rMSE), compared to the estimates from the existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperformed Besag models with two different dependence structures. Specifically, the Besag models increased the estimation precision by 27% around the peak of the dynamic curve, while the Leroux model improved the estimation by 40% at the peak, compared with the existing estimation method. For larger images: the proposed MAP estimators showed clear improvements on rMSE for vessels, tumor rim and white matter.

Harsh winter climate can cause various problems for both public and private sectors in Sweden, especially in the northern part for railway industry. To have a better understanding of winter climate impacts, this study investigates effects of the winter climate including ice/snow precipitation on the performance of high speed passenger trains in the Botnia-Atlantica region. The investigation is done with train operational data together with simulated weather data fromthe Weather Research and Forecast model over January–February 2017.

Two different measurements of the train performance are analysed. One is primary delay which measures the increment in delay in terms of running time within two consecutive measuring spots, the other is arrival delay which is the delay in terms of arrival time at each measuring spot compared to the schedule. Primary delay is investigated through a Cox model and the arrival delay is studied using a Markov chain model.

The results show that the weather variables have impacts on the train performance. Therein temperature and humidity have significant impacts on both the occurrence of primary delay and the transition intensities between arrival delay and non-delay.