Background Ablative tumor surgery often results in continuity defects of the mandible. When an immediate reconstruction using autologous bone grafts is not possible the bridging of the defects with a variety of bridging plates might be achieved. However, those bridging plates have the risk of plate fractures or exposure. Customized titanium implants manufactured using CAD/CAM and the LaserCUSING® technique might be an alternative. Methods In the present study, computed tomographies (CT) of porcine cadaver mandibles were generated and transferred into DICOM data. Following, different continuity defects were surgically created in the mandibles. Based on the DICOM data customized titanium implants were manufactured using CAD/CAM procedures and the LaserCUSING® technique. The implants were fixed to the remaining stumps with screws. Subsequently, the accuracy of the reconstructed mandibles was tested using plaster casts. Results The workflow from the CT to the application of the customized implants was proved to be practicable. Furthermore, a stable fixation of the customized implant to the remaining stumps could be achieved. The control of the accuracy showed no frictions or obstacles. Conclusion The customized titanium implant seems to be a promising approach to bridge continuity defects of the mandible whenever an immediate reconstruction with autologous bone is not possible.

Thermal oxidation of silicon belongs to the most decisive steps in microelectronic fabrication because it allows creating electrically insulating areas which enclose electrically conductive devices and device areas, respectively. Deal and Grove developed the first model (DG-model) for the thermal oxidation of silicon describing the oxide thickness versus oxidation time relationship with very good agreement for oxide thicknesses of more than 23 nm. Their approach named as general relationship is the basis of many similar investigations. However, measurement results show that the DG-model does not apply to very thin oxides in the range of a few nm. Additionally, it is inherently not self-consistent. The aim of this paper is to develop a self-consistent model that is based on the continuity equation instead of Fick’s law as the DG-model is. As literature data show, the relationship between silicon oxide thickness and oxidation time is governed—down to oxide thicknesses of just a few nm—by a power-of-time law. Given by the time-independent surface concentration of oxidants at the oxide surface, Fickian diffusion seems to be neglectable for oxidant migration. The oxidant flux has been revealed to be carried by non-Fickian flux processes depending on sites being able to lodge dopants (oxidants), the so-called DOCC-sites, as well as on the dopant jump rate.

Genetic and environmental protective factors and risks modulate brain structure and function in neurodegenerative diseases and their preclinical stages. We wanted to investigate whether the years of formal education, a proxy measure for cognitive reserve, would influence hippocampal structure in Alzheimer’s disease patients, and whether apolipoprotein Eε4 (APOE4) carrier status and a first-degree family history of the disease would change a possible association. Fifty-eight Alzheimer’s disease patients underwent 3T magnetic resonance imaging. We applied a cortical unfolding approach to investigate individual subregions of the medial temporal lobe. Among patients homozygous for the APOE4 genotype or carrying both APOE4 and family history risks, lower education was associated with a thinner cortex in multiple medial temporal regions, including the hippocampus. Our data suggest that the years of formal education and genetic risks interact in their influence on hippocampal structure in Alzheimer’s disease patients.

BACKGROUND: DNA damage occurs as a consequence of both direct and indirect effects of ionizing radiation. The severity of DNA damage depends on the physical characteristics of the radiation quality, e.g., the linear energy transfer (LET). There are still contrary findings regarding direct or indirect interactions of high-LET emitters with DNA. Our aim is to determine DNA damage and the effect on cellular survival induced by (223)Ra compared to (188)Re and (99m)Tc modulated by the radical scavenger dimethyl sulfoxide (DMSO). METHODS: Radioactive solutions of (223)Ra, (188)Re, or (99m)Tc were added to either plasmid DNA or to PC Cl3 cells in the absence or presence of DMSO. Following irradiation, single strand breaks (SSB) and double strand breaks (DSB) in plasmid DNA were analyzed by gel electrophoresis. To determine the radiosensitivity of the rat thyroid cell line (PC Cl3), survival curves were performed using the colony formation assay. RESULTS: Exposure to 120 Gy of (223)Ra, (188)Re, or (99m)Tc leads to maximal yields of SSB (80 %) in plasmid DNA. Irradiation with 540 Gy (223)Ra and 500 Gy (188)Re or (99m)Tc induced 40, 28, and 64 % linear plasmid conformations, respectively. DMSO prevented the SSB and DSB in a similar way for all radionuclides. However, with the α-emitter (223)Ra, a low level of DSB could not be prevented by DMSO. Irradiation of PC Cl3 cells with (223)Ra, (188)Re, and (99m)Tc pre-incubated with DMSO revealed enhanced survival fractions (SF) in comparison to treatment without DMSO. Protection factors (PF) were calculated using the fitted survival curves. These factors are 1.23 ± 0.04, 1.20 ± 0.19, and 1.34 ± 0.05 for (223)Ra, (188)Re, and (99m)Tc, respectively. CONCLUSIONS: For (223)Ra, as well as for (188)Re and (99m)Tc, dose-dependent radiation effects were found applicable for plasmid DNA and PC Cl3 cells. The radioprotection by DMSO was in the same range for high- and low-LET emitter. Overall, the results indicate the contribution of mainly indirect radiation effects for each of the radionuclides regarding DNA damage and cell survival. In summary, our findings may contribute to fundamental knowledge about the α-particle induced DNA damage.

Spin-polarized two-dimensional electron states (2DESs) at surfaces and interfaces of magnetically active materials attract immense interest because of the idea of exploiting fermion spins rather than charge in next generation electronics. Applying angle-resolved photoelectron spectroscopy, we show that the silicon surface of GdRh2Si2 bears two distinct 2DESs, one being a Shockley surface state, and the other a Dirac surface resonance. Both are subject to strong exchange interaction with the ordered 4f-moments lying underneath the Si-Rh-Si trilayer. The spin degeneracy of the Shockley state breaks down below ~90 K, and the splitting of the resulting subbands saturates upon cooling at values as high as ~185 meV. The spin splitting of the Dirac state becomes clearly visible around ~60 K, reaching a maximum of ~70 meV. An abrupt increase of surface magnetization at around the same temperature suggests that the Dirac state contributes significantly to the magnetic properties at the Si surface. We also show the possibility to tune the properties of 2DESs by depositing alkali metal atoms. The unique temperature-dependent ferromagnetic properties of the Si-terminated surface in GdRh2Si2 could be exploited when combined with functional adlayers deposited on top for which novel phenomena related to magnetism can be anticipated.

Within the field of functional magnetic resonance imaging (fMRI) neurofeedback, most studies provide subjects with instructions or suggest strategies to regulate a particular brain area, while other neuro-/biofeedback approaches often do not. This study is the first to investigate the hypothesis that subjects are able to utilize fMRI neurofeedback to learn to differentially modulate the fMRI signal from the bilateral amygdala congruent with the prescribed regulation direction without an instructed or suggested strategy and apply what they learned even when feedback is no longer available. Thirty-two subjects were included in the analysis. Data were collected at 3 Tesla using blood oxygenation level dependent (BOLD)-sensitivity optimized multi-echo EPI. Based on the mean contrast between up- and down-regulation in the amygdala in a post-training scan without feedback following three neurofeedback sessions, subjects were able to regulate their amygdala congruent with the prescribed directions with a moderate effect size of Cohen’s d = 0.43 (95% conf. int. 0.23–0.64). This effect size would be reduced, however, through stricter exclusion criteria for subjects that show alterations in respiration. Regulation capacity was positively correlated with subjective arousal ratings and negatively correlated with agreeableness and susceptibility to anger. A learning effect over the training sessions was only observed with end-of-block feedback (EoBF) but not with continuous feedback (trend). The results confirm the above hypothesis. Further studies are needed to compare effect sizes of regulation capacity for approaches with and without instructed strategies.

Reaction times (RTs) are a valuable measure for assessing cognitive processes. However, RTs are susceptible to confounds and therefore variable. Exposure to threat, for example, speeds up or slows down responses. Distinct task types to some extent account for differential effects of threat on RTs. But also do inter-individual differences like trait anxiety. In this functional magnetic resonance imaging (fMRI) study, we investigated whether activation within the amygdala, a brain region closely linked to the processing of threat, may also function as a predictor of RTs, similar to trait anxiety scores. After threat conditioning by means of aversive electric shocks, 45 participants performed a choice RT task during alternating 30 s blocks in the presence of the threat conditioned stimulus [CS+] or of the safe control stimulus [CS-]. Trait anxiety was assessed with the State-Trait Anxiety Inventory and participants were median split into a high- and a low-anxiety subgroup. We tested three hypotheses: (1) RTs will be faster during the exposure to threat compared to the safe condition in individuals with high trait anxiety. (2) The amygdala fMRI signal will be higher in the threat condition compared to the safe condition. (3) Amygdala fMRI signal prior to a RT trial will be correlated with the corresponding RT. We found that, the high-anxious subgroup showed faster responses in the threat condition compared to the safe condition, while the low-anxious subgroup showed no significant difference in RTs in the threat condition compared to the safe condition. Though the fMRI analysis did not reveal an effect of condition on amygdala activity, we found a trial-by-trial correlation between blood-oxygen-level-dependent signal within the right amygdala prior to the CRT task and the subsequent RT. Taken together, the results of this study showed that exposure to threat modulates task performance. This modulation is influenced by personality trait. Additionally and most importantly, activation in the amygdala predicts behavior in a simple task that is performed during the exposure to threat. This finding is in line with “attentional capture by threat”—a model that includes the amygdala as a key brain region for the process that causes the response slowing.

Graphs are used to model a wide range of systems from different disciplines including social network analysis, biology, and big data processing. When analyzing these constantly changing dynamic graphs at a high frequency, performance is the main concern. Depending on the graph size and structure, update frequency, and read accesses of the analysis, the use of different data structures can yield great performance variations. Even for expert programmers, it is not always obvious, which data structure is the best choice for a given scenario. In previous work, we presented an approach for handling the selection of the most efficient data structures automatically using a compile-time approach well-suited for constant workloads. We extend this work with a measurement study of seven data structures and use the results to fit actual cost estimation functions. In addition, we evaluate our approach for the computations of seven different graph metrics. In analyses of real-world dynamic graphs with a constant workload, our approach achieves a speedup of up to 5.4× compared to basic data structure configurations. Such a compile-time based approach cannot yield optimal results when the behavior of the system changes later and the workload becomes non-constant. To close this gap we present a run-time approach which provides live profiling and facilitates automatic exchanges of data structures during execution. We analyze the performance of this approach using an artificial, non-constant workload where our approach achieves speedups of up to 7.3× compared to basic configurations.

Chitosan is known for its hemostatic and antimicrobial properties and might be useful for temporary coating of removable dentures or intraoral splints to control bleeding after oral surgery or as a supportive treatment in denture stomatitis. This study investigated a new method to adhere chitosan to polymethyl methacrylate (PMMA) and polyethylene terephthalate (PET). There were 70 cylindrical specimens made from PMMA and 70 from PET (13 mm diameter, 6 mm thickness). The materials with ten specimens each were sandblasted at 2.8 or 4.0 bar with aluminum oxide 110 μm or/and aluminum oxide coated with silica. After sandblasting, all specimens were coated with a 2% or 4% acetic chitosan solution with a thickness of 1 mm. Then the specimens were dried for 120 min at 45 °C. The precipitated chitosan was neutralized with 1 mol NaOH. After neutralization, all specimens underwent abrasion tests using the tooth-brushing simulator with soft brushes (load 2N, 2 cycles/s, 32 °C, 3000 and 30,000 cycles). After each run, the specimen surfaces were analyzed for areas of remaining chitosan by digital planimetry under a light microscope. The best chitosan adhesion was found after sandblasting with aluminum oxide coated with silica (U-Test, p < 0.05) in both the PMMA and the PET groups. Hence, with relatively simple technology, a reliable bond of chitosan to PMMA and PET could be achieved.

Objectives: The inverse relationship between QT interval variability (QTV) and T wave amplitude potentially confounds QT variability assessment. We quantified the influence of the T wave amplitude on QTV in a comprehensive dataset and devised a correction formula. Methods: Three ECG datasets of healthy subjects were analyzed to model the relationship between T wave amplitude and QTV. To derive a generally valid correction formula, linear regression analysis was used. The proposed correction formula was applied to patients enrolled in the Evaluation of Defibrillator in Non-Ischemic Cardiomyopathy Treatment Evaluation trial (DEFINITE) to assess the prognostic significance of QTV for all-cause mortality in patients with non-ischemic dilated cardiomyopathy. Results: A strong inverse relationship between T wave amplitude and QTV was demonstrated, both in healthy subjects (R2 = 0.68, p < 0.001) and DEFINITE patients (R2 = 0.20, p < 0.001). Applying the T wave amplitude correction to QTV achieved 2.5-times better group discrimination between patients enrolled in the DEFINITE study and healthy subjects. Kaplan-Meier estimator analysis showed that T wave amplitude corrected QTVi is inversely related to survival (p < 0.01) and a significant predictor of all-cause mortality. Conclusion: We have proposed a simple correction formula for improved QTV assessment. Using this correction, predictive value of QTV for all-cause mortality in patients with non-ischemic cardiomyopathy has been demonstrated.