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The effects of aging on myelinated nerve fibers of the central nervous system are complex. Many myelinated nerve fibers in white matter degenerate and are lost, leading to some disconnections between various parts of the central nervous system. Other myelinated nerve fibers are affected differently, because only their sheaths degenerate, leaving the axons intact. Such axons are remyelinated by a series of internodes that are much shorter than the original ones and are composed of thinner sheaths. Thus the myelin-forming cells of the central nervous system, the oligodendrocytes, remain active during aging. Indeed, not only do these neuroglial cell remyelinate axons, with age they also continue to add lamellae to the myelin sheaths of intact nerve fibers, so that sheaths become thicker. It is presumed that the degeneration of myelin sheaths is due to the degeneration of the parent oligodendrocyte, and that the production of increased numbers of internodes as a consequence of remyelination requires additional oligodendrocytes. Whether there is a turnover of oligodendrocytes during life has not been studied in primates, but it has been established that over the life span of the monkey, there is a substantial increase in the numbers of oligodendrocytes. While the loss of some myelinated nerve fibers leads to some disconnections, the degeneration of other myelin sheaths and the subsequent remyelination of axons by shorter internodes slow down the rate conduction along nerve fibers. These changes affect the integrity and timing in neuronal circuits, and there is evidence that they contribute to cognitive decline.

There is no consensus on a comprehensive classification for green infrastructure (GI). This is a consequence of the diversity of disciplines, application contexts, methods, terminologies, purposes and valuation criteria for which a GI typology is required. The aim of this systematic literature review is to evaluate the existing evidence on how GI is being categorised and characterised worldwide. We reviewed a total of 85 studies from 15 countries that were analysed for contextual trends, methods, parameters and typologies. Results show that relevant literature lacks a common terminology and that a universal typology for all scenarios is impractical. Analysis reveals that GI can be organised into four main GI categories: (a) tree canopy, (b) green open spaces, (c) green roofs and (d) vertical greenery systems (facades/walls). Green open spaces and tree canopy attracted the attention of researchers due to their complexity, variability and important roles in GI planning. Evidence suggests that a ternary approach in terms of the functional (purpose, use, services), structural (morphology) and configurational (spatial arrangements) attributes of GI should be applied for a more comprehensive classification. Although this approximation is inherently generic, since it can be used across different research disciplines, it is also sufficiently specific to be implemented for individual scopes, scenarios and settings. Further research is needed to develop a typology capable of responding to particular research aims and performance analyses based upon the findings discussed in this paper.

To examine the impact of the partial volume effect (PVE) on the imaging of spherical objects depending on their size, density and center voxel position. We developed an algorithm for calculating the volume of a sphere wrapped by voxels. The algorithm measured the internal volume of each voxel cut by the sphere and automatically attributed the average voxel density. The sphere volume was simulated by the sum of voxels with an average density above the Hounsfield Unit (HU) cutoff level for that object. Various sphere sizes, densities and positions in the voxel grid were examined. The two clinical settings used were nodules (0 HU) in the lung (-1000 HU) and kidney stones (1000 HU) embedded in the renal parenchyma (30 HU). Small kidney stones appeared magnified by the PVE when a stone cutoff level of 130 HU was used: the smallest stone simulated with a diameter of 1.4 mm demonstrated a volume that was 231% the size of the ground truth (sphere volume as measured with the classical formula). A hypothetical stone of 10 cm would still have a PVE of 2%. The PVE did not affect lung nodules if the cutoff level for the nodule fraction was set to the exact mean of both the internal and external density (-500 HU). Lung nodules were more affected by the geometrical effect, where tiny nodules appeared smaller because of the greater curvature of smaller spheres, often cutting less than 50% of the volume of a surface voxel. This study highlights the potential risks associated with inaccurate raw data postprocessing of CT images with objects that are particularly sensitive to the PVE, such as kidney stones and high-density calcifications (Agatston score).

Reptiles show varying degrees of facultative parthenogenesis. Here we use genetic methods to determine that an isolated, captive female Asian water dragon produced at least nine offspring via parthenogenesis. We identified microsatellites for the species from shotgun genomic sequences, selected and optimized primer sets, and tested all of the offspring for a set of seven microsatellites that were heterozygous in the mother. We verified that the seven loci showed high levels of polymorphism in four wild Asian water dragons from Vietnam. In all cases, the offspring (unhatched, but developed eggs, or hatched young) had only a single allele at each locus, and contained only alleles present in the mother's genotype (i.e., were homozygous or hemizygous). The probability that our findings resulted from the female mating with one or more males is extremely small, indicating that the offspring were derived from a single female gamete (either alone or via duplication and/or fusion) and implicating parthenogenesis. This is the first documented case of parthenogenesis in the Squamate family Agamidae.

Objectives This study was conducted to evaluate the effect of dose reduction on the performance of a deep learning (DL)–based computer-aided diagnosis (CAD) system regarding pulmonary nodule detection in a virtual screening scenario. Methods Sixty-eight anthropomorphic chest phantoms were equipped with 329 nodules (150 ground glass, 179 solid) with four sizes (5 mm, 8 mm, 10 mm, 12 mm) and scanned with nine tube voltage/current combinations. The examinations were analyzed by a commercially available DL-based CAD system. The results were compared by a comparison of proportions. Logistic regression was performed to evaluate the impact of tube voltage, tube current, nodule size, nodule density, and nodule location. Results The combination with the lowest effective dose ( E ) and unimpaired detection rate was 80 kV/50 mAs (sensitivity: 97.9%, mean false-positive rate (FPR): 1.9, mean CTDIvol: 1.2 ± 0.4 mGy, mean E : 0.66 mSv). Logistic regression revealed that tube voltage and current had the greatest impact on the detection rate, while nodule size and density had no significant influence. Conclusions The optimal tube voltage/current combination proposed in this study (80 kV/50 mAs) is comparable to the proposed combinations in similar studies, which mostly dealt with conventional CAD software. Modification of tube voltage and tube current has a significant impact on the performance of DL-based CAD software in pulmonary nodule detection regardless of their size and composition. Key Points • Modification of tube voltage and tube current has a significant impact on the performance of deep learning–based CAD software. • Nodule size and composition have no significant impact on the software’s performance. • The optimal tube voltage/current combination for the examined software is 80 kV/50 mAs.

Objectives To evaluate and compare the measurement accuracy of two different computer-aided diagnosis (CAD) systems regarding artificial pulmonary nodules and assess the clinical impact of volumetric inaccuracies in a phantom study. Methods In this phantom study, 59 different phantom arrangements with 326 artificial nodules (178 solid, 148 ground-glass) were scanned at 80 kV, 100 kV, and 120 kV. Four different nodule diameters were used: 5 mm, 8 mm, 10 mm, and 12 mm. Scans were analyzed by a deep-learning (DL)–based CAD and a standard CAD system. Relative volumetric errors (RVE) of each system vs. ground truth and the relative volume difference (RVD) DL–based vs. standard CAD were calculated. The Bland–Altman method was used to define the limits of agreement (LOA). The hypothetical impact on LungRADS classification was assessed for both systems. Results There was no difference between the three voltage groups regarding nodule volumetry. Regarding the solid nodules, the RVE of the 5-mm-, 8-mm-, 10-mm-, and 12-mm-size groups for the DL CAD/standard CAD were 12.2/2.8%, 1.3/ − 2.8%, − 3.6/1.5%, and − 12.2/ − 0.3%, respectively. The corresponding values for the ground-glass nodules (GGN) were 25.6%/81.0%, 9.0%/28.0%, 7.6/20.6%, and 6.8/21.2%. The mean RVD for solid nodules/GGN was 1.3/ − 15.2%. Regarding the LungRADS classification, 88.5% and 79.8% of all solid nodules were correctly assigned by the DL CAD and the standard CAD, respectively. 14.9% of the nodules were assigned differently between the systems. Conclusions Patient management may be affected by the volumetric inaccuracy of the CAD systems and hence demands supervision and/or manual correction by a radiologist. Key Points • The DL-based CAD system was more accurate in the volumetry of GGN and less accurate regarding solid nodules than the standard CAD system. • Nodule size and attenuation have an effect on the measurement accuracy of both systems; tube voltage has no effect on measurement accuracy. • Measurement inaccuracies of CAD systems can have an impact on patient management, which demands supervision by radiologists.

Objectives The purpose of this study was to determine the influence of dose reduction on a commercially available lung cancer prediction convolutional neuronal network (LCP-CNN). Methods CT scans from a cohort provided by the local lung cancer center ( n  = 218) with confirmed pulmonary malignancies and their corresponding reduced dose simulations (25% and 5% dose) were subjected to the LCP-CNN. The resulting LCP scores (scale 1–10, increasing malignancy risk) and the proportion of correctly classified nodules were compared. The cohort was divided into a low-, medium-, and high-risk group based on the respective LCP scores; shifts between the groups were studied to evaluate the potential impact on nodule management. Two different malignancy risk score thresholds were analyzed: a higher threshold of ≥ 9 (“rule-in” approach) and a lower threshold of > 4 (“rule-out” approach). Results In total, 169 patients with 196 nodules could be included (mean age ± SD, 64.5 ± 9.2 year; 49% females). Mean LCP scores for original, 25% and 5% dose levels were 8.5 ± 1.7, 8.4 ± 1.7 ( p  > 0.05 vs. original dose) and 8.2 ± 1.9 ( p  < 0.05 vs. original dose), respectively. The proportion of correctly classified nodules with the “rule-in” approach decreased with simulated dose reduction from 58.2 to 56.1% ( p  = 0.34) and to 52.0% for the respective dose levels ( p  = 0.01). For the “rule-out” approach the respective values were 95.9%, 96.4%, and 94.4% ( p  = 0.12). When reducing the original dose to 25%/5%, eight/twenty-two nodules shifted to a lower, five/seven nodules to a higher malignancy risk group. Conclusion CT dose reduction may affect the analyzed LCP-CNN regarding the classification of pulmonary malignancies and potentially alter pulmonary nodule management. Clinical relevance statement Utilization of a “rule-out” approach with a lower malignancy risk threshold prevents underestimation of the nodule malignancy risk for the analyzed software, especially in high-risk cohorts. Key Points • LCP-CNN may be affected by CT image parameters such as noise resulting from low-dose CT acquisitions. • CT dose reduction can alter pulmonary nodule management recommendations by affecting the outcome of the LCP-CNN. • Utilization of a lower malignancy risk threshold prevents underestimation of pulmonary malignancies in high-risk cohorts.

Background and Objectives: Osteoarthritis (OA) of the knee is a degenerative disorder characterized by damage to the joint cartilage, pain, swelling, and walking disability. The purpose of this study was to assess whether demographic and radiologic parameters (knee diameters and knee cross-sectional area from magnetic resonance (MR) images) could be used as surrogate biomarkers for the prediction of OA. Materials and Methods: The knee diameters and cross-sectional areas of 481 patients were measured on knee MR images, and the corresponding demographic parameters were extracted from the patients’ clinical records. The images were graded based on the modified Outerbridge arthroscopic classification that was used as ground truth. Receiver-operating characteristic (ROC) analysis was performed on the collected data. Results: ROC analysis established that age was the most accurate predictor of severe knee cartilage degeneration (corresponding to Outerbridge grades 3 and 4) with an area under the curve (AUC) of the specificity–sensitivity plot of 0.865 ± 0.02. An age over 41 years was associated with a sensitivity and specificity for severe degeneration of 82.8% (CI: 77.5–87.3%), and 76.4% (CI: 70.4–81.6%), respectively. The second-best degeneration predictor was the normalized knee cross-sectional area, with an AUC of 0.767 ± 0.04), followed by BMI (AUC = 0.739 ± 0.02), and normalized knee maximal diameter (AUC = 0.724 ± 0.05), meaning that knee degeneration increases with increasing knee diameter. Conclusions: Age is the best predictor of knee damage progression in OA and can be used as surrogate marker for knee degeneration. Knee diameters and cross-sectional area also correlate with the extent of cartilage lesions. Though less-accurate predictors of damage progression than age, they have predictive value and are therefore easily available surrogate markers of OA that can be used also by general practitioners and orthopedic surgeons.