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A mobile sensor suite was developed and evaluated to predict plant water status by measuring the leaf temperature and microclimatic variables in nut crop trees and grapevines. The sensor suite consists of an infrared thermometer to measure leaf temperature along with other relevant sensors to measure microclimatic variables. The sensor suite was successfully evaluated in commercial orchards in the Sacramento Valley of California on three orchard crops, i.e., almond (Prunus dulcis), walnut (Juglans regia), and grape (Vitis vinifera), for both sunlit and shaded leaves. Stepwise linear regression models developed for shaded leaf temperature yielded coefficient of multiple determination values of 0.90, 0.86, and 0.86 for almond, walnut, and grape crops, respectively. Stem water potential (SWP) was found to be a significant variable in all models. The regression models were used to classify trees into water stressed and unstressed categories. Critical misclassification errors (classifying a water stressed tree as unstressed) for sunlit and shaded leaf models were 8.8% and 5.2% for almond, 5.4% and 6.9% for walnut, and 12.9% and 8.1% for grapevine, respectively. Canonical discriminant analyses were also conducted using the sensor suite data to classify trees into water stressed and unstressed trees with critical misclassification errors for sunlit and shaded leaves of 9.3% and 7.8% for almond, 2.0% and 4.1% for walnut, and 9.6% and 1.6% for grapevine, respectively. These results show the feasibility of the sensor suite to determine plant water status for irrigation management of nut and vineyard crops. [ABSTRACT FROM AUTHOR]

The period from full bloom (FB) to fruit maturity for individual cultivars of peach, nectarine, plum, and prune is influenced by daily temperatures between the start of FB and 30 d after FB (DAFB). Typically, warm Springs accelerate fruit development. Almond is closely-related to peach, but the date of fruit maturity is not always closely- related to the date of harvest. Normally the date of "hull-split" (HS) signals the beginning of fruit maturity. The aim of this study was to determine if the length of the period between FB and HS in several important Californian almond cultivars was related to temperatures shortly after the start of FB. Data on the dates of FB and HS from three locations in the Central Valleys of California (North, Central, and South) were analysed over 8 years to determine the effect of Spring temperatures on the duration of fruit development. Data on 28 cultivars were evaluated, but only the results for 12 of the most important cultivars are reported here. The length of the period of fruit development from FB to HS was negatively correlated with the accumulation of degree-days between FB and 90 DAFB (mean R2 = 0.51 ± 0.3), with generally poorer correlations with degree-days to 30 or 50 DAFB (mean R2 = 0.31 ± 0.02 and 0.36 ± 0.3, respectively). These results suggest that temperatures in the first 90 DAFB are the primary factor influencing the time of nut maturity in almond cultivars in California. This information will be used to develop a harvest prediction model to assist growers in planning harvest dates. To facilitate this, we are in the process of developing a webpage on the UC Davis Fruit and Research Information Website similar to the one for peach and plum growers. [ABSTRACT FROM AUTHOR]

Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T 2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts., Competing Interests: Declaration of Competing Interest Markus Nilsson and Filip Szczepankiewicz declare ownership interests in Random Walk Imaging, which holds patents pertaining to the methods presented herein. Remaining authors declare no conflict of interest., (Copyright © 2023. Published by Elsevier Inc.)

Background: Chemo- and radiotherapy (RT) is standard treatment for patients with high-grade glioma, but may cause side-effects on the patient's cognitive function., Aim: Use of diffusion tensor imaging (DTI) to investigate the longitudinal changes in normal-appearing brain tissue in glioblastoma patients undergoing modern arc-based RT with volumetric modulated arc therapy (VMAT) or helical tomotherapy., Materials and Methods: The study included 27 patients newly diagnosed with glioblastoma and planned for VMAT or tomotherapy. All subjects underwent magnetic resonance imaging at the start of RT and at week 3, 6, 15, and 26. Fourteen subjects were additionally imaged at week 52. The DTI data were co-registered to the dose distribution maps. Longitudinal changes in fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were assessed in the corpus callosum, the centrum semiovale, the hippocampus, and the amygdala., Results: Significant longitudinal changes in FA, MD, and RD were mainly found in the corpus callosum. In the other examined brain structures, only sparse and transient changes were seen. No consistent correlations were found between biodose, age, or gender and changes in DTI parameters., Conclusion: Longitudinal changes in MD, FA, and RD were observed but only in a limited number of brain structures and the changes were smaller than expected from literature. The results suggest that modern, arc-based RT may have less negative effect on normal-appearing parts of the brain tissue up to 12 months after radiotherapy.

Background: Tumor-related hyperintensities in high b -value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation., Purpose: To investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter., Materials and Methods: Twenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b -value of 2,000 s/mm 2 , were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNR eff )., Results: The spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 - 2.1) for STE and 1.4 (1.3 - 1.7) for LTE, with a significant difference of 0.4 (0.3 -0.5) ( p < 10 -4 , paired U -test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNR eff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 - 3.5) vs. 2.3 (1.7 - 3.1), with a significant difference of 0.4 (-0.1 -0.6) ( p < 10 -3 , paired U -test). The STE improved CNR eff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected., Conclusion: The contrast mechanism of high b -value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities., Competing Interests: MN declares ownership interests in Random Walk Imaging, and patent applications in Sweden (1250453-6 and 1250452-8), in the United States (61/642 594 and 61/642 589), and via the Patent Cooperation Treaty (SE2013/050492 and SE2013/050493). JB, MN, FS, and PS are inventors on pending patents pertaining to the methods presented herein in the United States (63/293 098). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Brabec, Durmo, Szczepankiewicz, Brynolfsson, Lampinen, Rydelius, Knutsson, Westin, Sundgren and Nilsson.)

Specific features of white matter microstructure can be investigated by using biophysical models to interpret relaxation-diffusion MRI brain data. Although more intricate models have the potential to reveal more details of the tissue, they also incur time-consuming parameter estimation that may converge to inaccurate solutions due to a prevalence of local minima in a degenerate fitting landscape. Machine-learning fitting algorithms have been proposed to accelerate the parameter estimation and increase the robustness of the attained estimates. So far, learning-based fitting approaches have been restricted to microstructural models with a reduced number of independent model parameters where dense sets of training data are easy to generate. Moreover, the degree to which machine learning can alleviate the degeneracy problem is poorly understood. For conventional least-squares solvers, it has been shown that degeneracy can be avoided by acquisition with optimized relaxation-diffusion-correlation protocols that include tensor-valued diffusion encoding. Whether machine-learning techniques can offset these acquisition requirements remains to be tested. In this work, we employ artificial neural networks to vastly accelerate the parameter estimation for a recently introduced relaxation-diffusion model of white matter microstructure. We also develop strategies for assessing the accuracy and sensitivity of function fitting networks and use those strategies to explore the impact of the acquisition protocol. The developed learning-based fitting pipelines were tested on relaxation-diffusion data acquired with optimal and sub-optimal acquisition protocols. Networks trained with an optimized protocol were observed to provide accurate parameter estimates within short computational times. Comparing neural networks and least-squares solvers, we found the performance of the former to be less affected by sub-optimal protocols; however, model fitting networks were still susceptible to degeneracy issues and their use could not fully replace a careful design of the acquisition protocol., (Copyright © 2021. Published by Elsevier Inc.)

Purpose: Reperfusion therapy enables effective treatment of ischemic stroke presenting within 4-6 hours. However, tissue progression from ischemia to infarction is variable, and some patients benefit from treatment up until 24 hours. Improved imaging techniques are needed to identify these patients. Here, it was hypothesized that time dependence in diffusion MRI may predict tissue outcome in ischemic stroke., Methods: Diffusion MRI data were acquired with multiple diffusion times in five non-reperfused patients at 2, 9, and 100 days after stroke onset. Maps of "rate of kurtosis change" (k), mean kurtosis, ADC, and fractional anisotropy were derived. The ADC maps defined lesions, normal-appearing tissue, and the lesion tissue that would either be infarcted or remain viable by day 100. Diffusion parameters were compared (1) between lesions and normal-appearing tissue, and (2) between lesion tissue that would be infarcted or remain viable., Results: Positive values of k were observed within stroke lesions on day 2 (P = .001) and on day 9 (P = .023), indicating diffusional exchange. On day 100, high ADC values indicated infarction of 50 ± 20% of the lesion volumes. Tissue infarction was predicted by high k values both on day 2 (P = .026) and on day 9 (P = .046), by low mean kurtosis values on day 2 (P = .043), and by low fractional anisotropy values on day 9 (P = .029), but not by low ADC values., Conclusions: Diffusion time dependence predicted tissue outcome in ischemic stroke more accurately than the ADC, and may be useful for predicting reperfusion benefit., (© 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Cerebrospinal fluid (CSF) neurofilament light (NfL) concentration has reproducibly been shown to reflect neurodegeneration in brain disorders, including Alzheimer's disease (AD). NfL concentration in blood correlates with the corresponding CSF levels, but few studies have directly compared the reliability of these 2 markers in sporadic AD. Herein, we measured plasma and CSF concentrations of NfL in 478 cognitively unimpaired (CU) subjects, 227 patients with mild cognitive impairment, and 113 patients with AD dementia. We found that the concentration of NfL in CSF, but not in plasma, was increased in response to Aβ pathology in CU subjects. Both CSF and plasma NfL concentrations were increased in patients with mild cognitive impairment and AD dementia. Furthermore, only NfL in CSF was associated with reduced white matter microstructure in CU subjects. Finally, in a transgenic mouse model of AD, CSF NfL increased before serum NfL in response to the development of Aβ pathology. In conclusion, NfL in CSF may be a more reliable biomarker of neurodegeneration than NfL in blood in preclinical sporadic AD., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T 2 values within a two-compartment model of white matter, and to explore the approach in vivo., Methods: Sampling protocols featuring different b-values (b), b-tensor shapes (b Δ ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB). Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T 2 values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin.", Results: Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/b Δ /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm 2 /ms) but higher T 2 values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm 2 /ms) and T 2 values (150 ms)., Conclusions: Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities., (© 2020 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

Objective: Delineation of malformations of cortical development (MCD) is central in presurgical evaluation of drug-resistant epilepsy. Delineation using magnetic resonance imaging (MRI) can be ambiguous, however, because the conventional T 1 - and T 2 -weighted contrasts depend strongly on myelin for differentiation of cortical tissue and white matter. Variations in myelin content within both cortex and white matter may cause MCD findings on MRI to change size, become undetectable, or disagree with histopathology. The novel tensor-valued diffusion MRI (dMRI) technique maps microscopic diffusion anisotropy, which is sensitive to axons rather than myelin. This work investigated whether tensor-valued dMRI may improve differentiation of cortex and white matter in the delineation of MCD., Methods: Tensor-valued dMRI was performed on a 7 T MRI scanner in 13 MCD patients (age = 32 ± 13 years) featuring periventricular heterotopia, subcortical heterotopia, focal cortical dysplasia, and polymicrogyria. Data analysis yielded maps of microscopic anisotropy that were compared with T 1 -weighted and T 2 -fluid-attenuated inversion recovery images and with the fractional anisotropy from diffusion tensor imaging., Results: Maps of microscopic anisotropy revealed large white matter-like regions within MCD that were uniformly cortex-like in the conventional MRI contrasts. These regions were seen particularly in the deep white matter parts of subcortical heterotopias and near the gray-white boundaries of focal cortical dysplasias and polymicrogyrias., Significance: By being sensitive to axons rather than myelin, mapping of microscopic anisotropy may yield a more robust differentiation of cortex and white matter and improve MCD delineation in presurgical evaluation of epilepsy., (© 2020 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

A growing body of evidence suggests that the dysregulation of neuronal iron may play a critical role in Alzheimer's disease. Recent MRI studies have established a relationship between iron accumulation and amyloid-β aggregation. The present study provides further insight demonstrating a relationship between iron and tau accumulation using magnetic resonance-based quantitative susceptibility mapping and tau-PET in n = 236 subjects with amyloid-β pathology (from the Swedish BioFINDER-2 study). Both voxel-wise and regional analyses showed a consistent association between differences in bulk magnetic susceptibility, which can be primarily ascribed to an increase in iron content, and tau-PET signal in regions known to be affected in Alzheimer's disease. Subsequent analyses revealed that quantitative susceptibility specifically mediates the relationship between tau-PET and cortical atrophy measures, thus suggesting a modulatory effect of iron burden on the disease process. We also found evidence suggesting the relationship between quantitative susceptibility and tau-PET is stronger in younger participants (age ≤ 65). Together, these results provide in vivo evidence of an association between iron deposition and both tau aggregation and neurodegeneration, which help advance our understanding of the role of iron dysregulation in the Alzheimer's disease aetiology., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.)

In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic ("stick-like") diffusion. Second, the "density" of tissue components may be confounded by non-diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with "b-tensor encoding" and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b-tensor data is associated with myelinated axons but not with dendrites. Furthermore, b-tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data-driven estimates of compartment-specific T2 values. Finally, the "stick" fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment-specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained "index" parameters could be valid within limited domains that should be delineated by future studies., (© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Apolipoprotein (APOE) ε4 is a major genetic risk factor for Alzheimer's disease (AD), but its importance for the clinical and biological heterogeneity in AD is unclear, particularly at the prodromal stage. We analyzed 151 prodromal AD patients (44 APOE ε4-negative and 107 APOE ε4-positive) from the BioFINDER study. We tested cognition, 18F-flutemetamol β-amyloid (Aβ) positron emission tomography, cerebrospinal fluid biomarkers of Aβ, tau and neurodegeneration, and magnetic resonance imaging of white matter pathology and brain structure. Despite having similar cortical Aβ-load and baseline global cognition (mini mental state examination), APOE ε4-negative prodromal AD had more nonamnestic cognitive impairment, higher cerebrospinal fluid levels of Aβ-peptides and neuronal injury biomarkers, more white matter pathology, more cortical atrophy, and faster decline of mini mental state examination, compared to APOE ε4-positive prodromal AD. The absence of APOE ε4 is associated with an atypical phenotype of prodromal AD. This suggests that APOE ε4 may impact both the diagnostics of AD in early stages and potentially also effects of disease-modifying treatments., (Copyright © 2018 Elsevier Inc. All rights reserved.)