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In the cell cycle, there are two checkpoint arrests that allow cells to repair damaged DNA in order to maintain genomic integrity. Many cancer cells have defective G1 checkpoint mechanisms, thus depending on the G2 checkpoint far more than normal cells. G2 checkpoint abrogation is therefore a promising concept to preferably damage cancerous cells over normal cells. The main factor influencing the decision to enter mitosis is a complex composed of Cdk1 and cyclin B. Cdk1/CycB is regulated by various feedback mechanisms, in particular inhibitory phosphorylations at Thr14 and Tyr15 of Cdk1. In fact, Cdk1/CycB activity is restricted by the balance between WEE family kinases and Cdc25 phosphatases. The WEE kinase family consists of three proteins: WEE1, PKMYT1, and the less important WEE1B. WEE1 exclusively mediates phosphorylation at Tyr15, whereas PKMYT1 is dual-specific for Tyr15 as well as Thr14. Inhibition by a small molecule inhibitor is therefore proposed to be a promising option since WEE kinases bind Cdk1, altering equilibria and thus affecting G2/M transition.

Earth-fill structures such as embankments, which are constructed for the preservation of land and infrastructure, show significant amount of settlement during and after construction in lowland areas with soft grounds. Settlements are often still predicted with large uncertainty and frequently observational methods are applied using settlement monitoring results in the early stage after construction to predict the long term settlement. Most of these methods require a significant amount of measurements to enable accurate predictions. In this paper, an artificial neural network model for settlement prediction is evaluated and improved using measurement records from a test embankment in The Netherlands. Based on a learning pattern that focuses on convergence of the settlement rate, the basic model predicted settlements which were in good agreement with the measurements, when the amount of measured data used as teach data for the model exceeded a degree of consolidation of 69 %. For lower amounts of teach data the accuracy of settlement prediction was limited. To improve the accuracy of settlement prediction, it is proposed to add short-term predicted values that satisfy predefined statistical criteria of low coefficient of variance or low standard deviation to the teach data, after which the model is allowed to relearn and repredict the settlement. This procedure is repeated until all predicted values satisfy the criterion. Using the improved network model resulted in significantly better predictions. Predicted settlements were in good agreement with the measurements, even when only the measurements up to a consolidation stage of 35 % were used as initial teach data.

This research presents a hybrid approach to generate sample data for future machine learning applications for the prediction of mechanical properties in directed energy deposition-arc (DED-Arc) using the GMAW process. DED-Arc is an additive manufacturing process which offers a cost-effective way to generate 3D metal parts, due to its high deposition rate of up to 8 kg/h. The mechanical properties additively manufactured wall structures made of the filler material G4Si1 (ER70 S-6) are shown in dependency of the t8/5 cooling time. The numerical simulation is used to link the process parameters and geometrical features to a specific t8/5 cooling time. With an input of average welding power, welding speed and geometrical features such as wall thickness, layer height and heat source size a specific temperature field can be calculated for each iteration in the simulated welding process. This novel approach allows to generate large, artificial data sets as training data for machine learning methods by combining experimental results to generate a regression equation based on the experimentally measured t8/5 cooling time. Therefore, using the regression equations in combination with numerically calculated t8/5 cooling times an accurate prediction of the mechanical properties was possible in this research with an error of only 2.6%. Thus, a small set of experimentally generated data set allows to achieve regression equations which enable a precise prediction of mechanical properties. Moreover, the validated numerical welding simulation model was suitable to achieve an accurate calculation of the t8/5 cooling time, with an error of only 0.3%.

PurposeTo investigate whether (1 → 3)-β-d-Glucan (BDG)-guidance shortens time to antifungal therapy and thereby reduces mortality of sepsis patients with high risk of invasive Candida infection (ICI).MethodsMulticenter, randomized, controlled trial carried out between September 2016 and September 2019 in 18 intensive care units enrolling adult sepsis patients at high risk for ICI. Patients in the control group received targeted antifungal therapy driven by culture results. In addition to targeted therapy, patients in the BDG group received antifungals if at least one of two consecutive BDG samples taken during the first two study days was ≥ 80 pg/mL. Empirical antifungal therapy was discouraged in both groups. The primary endpoint was 28-day-mortality.Results339 patients were enrolled. ICI was diagnosed in 48 patients (14.2%) within the first 96 h after enrollment. In the BDG-group, 48.8% (84/172) patients received antifungals during the first 96 h after enrollment and 6% (10/167) patients in the control group. Death until day 28 occurred in 58 of 172 patients (33.7%) in the BDG group and 51 of 167 patients (30.5%) in the control group (relative risk 1.10; 95% confidence interval, 0.80–1.51; p = 0.53). Median time to antifungal therapy was 1.1 [interquartile range (IQR) 1.0–2.2] days in the BDG group and 4.4 (IQR 2.0–9.1, p < 0.01) days in the control group.ConclusionsSerum BDG guided antifungal treatment did not improve 28-day mortality among sepsis patients with risk factors for but unexpected low rate of IC. This study cannot comment on the potential benefit of BDG-guidance in a more selected at-risk population.

This study investigates the advantages and disadvantages of cone-beam-based navigated standardized posterior lumbar interbody fusion surgery (PLIF), regarding the radiation exposure and perioperative time management, compared to the use of fluoroscopy. Patients treated receiving an elective one- to three-level PLIF were retrospectively enrolled in the study. The surgery time, preparation time, operation room time, and effective dose (mSv) were analyzed for comparison of the radiation exposure and time consumption between cone-beam and fluoroscopy; Results: 214 patients were included (108 cone-beam navigated, and 106 traditional fluoroscopies). Using cone-beam navigation, reductions in the effective dose (2.23 ± 1.96 mSv vs. 3.39 ± 2.32 mSv, p = 0.002) and mean surgery time of 30 min (143.62 ± 43.87 min vs. 171.10 ± 48.91 min, p < 0.001) were demonstrated, which leveled out the extended preparation time of 7–8 min (37.25 ± 9.99 min vs. 29.65 ± 7.69 min, p < 0.001). These effects were fusion length dependent and demonstrated additional benefits in multisegmental surgeries. The cone-beam navigation system led to a reduction in the perioperative time requirements and radiation exposure. Furthermore, the controversially discussed longer preparation time when using cone-beam navigation was amortized by a shortened surgery time, especially in multilevel surgery.

When humans listen to popular music, they often feel ‘the groove’, which is a pleasurable urge to move along with music. Previous studies suggest that groove arises from the brain’s entrainment to rhythmical patterns of music in the delta-theta and beta bands, which enables the brain to predict temporal structures. However, this notion has only been tested using simplified or artificial acoustic stimuli and melodies, rather than naturalistic popular music. In this study, we combined electroencephalography (EEG) measurements with two measures of neural entrainment to test whether neural entrainment in the delta, theta, and beta bands predicts individuals’ groove ratings of musical and clapping stimuli, which served as a control. We presented nine pop music songs and twelve MIDI-based clapping rhythms, which participants rated for their groove perception on a 5-point scale. Our results show that music songs received very variable groove ratings while ratings of clapping stimuli showed less variability. Univariate analyses demonstrated that both music and clapping stimuli consistently entrained neural oscillations in the delta–theta bands, but not beta bands, as measured by intertrial phase coherence (ITPC) and stimulus-brain coherence (SBC). ITPC and SBC in the theta band weakly, but significantly correlated with groove ratings in fronto-central clusters. Decoding of groove ratings from multivariate spatiotemporal-spectral EEG patterns of entrainment with curve-fitting and machine-learning models predicted groove perception with high accuracy, up to 0.8 Pearson correlation, where combined patterns of inter-trial phase coherence appeared more informative than patterns of stimulus-brain coherence. Our results support the theory that the perception of groove is rooted in the brain’s ability to entrain to ongoing dynamic temporal structures of music in the theta band. They further show that multivariate decoding models can be leveraged to predict subjective groove online from neural entrainment with high accuracy and temporal resolution. While participants (n = 30) rated their subjective groove perception of 9 popular songs and 12 MIDI clapping rhythms, we measured how their brains entrained to these musical stimuli using stimulus-brain coherence (SBC) and inter-trial phase coherence (ITPC) measures. Both stimulus types strongly entrained delta–theta (1–7 Hz) oscillations. In univariate analyses, also both stimulus types at fronto-central, beat-related frequencies significantly predicted groove ratings. Multivariate decoding using EEG entrainment patterns predicted individual groove ratings far better (up to Pearson r = 0.8), with combined ITPC patterns providing the most information.

PINK1 gene mutations are a cause of recessively inherited, early-onset Parkinson's disease. In some patients, a single heterozygous mutation has been identified, including the recurrent c.1366C>T transition. The interpretation of this finding remains controversial. Furthermore, the c.1366C>T mutation is associated with lower levels of PINK1 transcript, raising the question of whether mRNA levels correlate with the clinical status. We sequenced genomic DNA and copy DNA (cDNA) from 20 subjects carrying the c.1366C>T mutation in the homozygous (n = 5) or heterozygous (n = 15) state. In 17 mutation carriers, messenger RNA (mRNA) was quantified by real-time PCR using four different assays (PINK1 exon 5-6 or exon 7-8 relative to control genes SDHA or YWHAZ). Genomic sequencing confirmed the presence and zygosity of PINK1 mutations. cDNA sequencing in heterozygous mutation carriers revealed a strong wild-type and a much weaker or almost absent mutant signal, whereas in the homozygous patients, only the mutant signal was detected. Homozygous and heterozygous carriers showed PINK1 mRNA levels relative to a reference gene in the range of 0.1-0.2 and 0.5-0.6, respectively, compared with values of 0.9-1.0 in mutation-negative individuals. Treatment of lymphoblasts from a heterozygous mutation carrier with cycloheximide markedly increased the mutant transcript signal. We conclude that the recurrent PINK1 c.1366C>T mutation exerts a major effect at the mRNA level (80-90% reduction), most likely via nonsense-mediated mRNA decay. The absence of correlation between PINK1 mRNA levels and clinical status in heterozygous mutation carriers suggests that other genetic or environmental factors play a role in determining the phenotypic variability associated with the c.1366C>T mutation.

First data on coherent threshold \\pi^0 electroproduction from the deuteron taken by the A1 Collaboration at the Mainz Microtron MAMI are presented. At a four-momentum transfer of q^2=-0.1 GeV^2/c^2 the full solid angle was covered up to a center-of-mass energy of 4 MeV above threshold. By means of a Rosenbluth separation the longitudinal threshold s wave multipole and an upper limit for the transverse threshold s wave multipole could be extracted and compared to predictions of Heavy Baryon Chiral Perturbation Theory.