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A time resolved experiment to investigate the ultrafast dynamics following an ion impact onto a solid surface requires an ultrashort ion pump pulse in combination with a properly synchronized and time resolved probe. In order to realize such an experiment, we have investigated a strategy to use femtosecond laser photoionization of atoms entrained in a pulsed supersonic jet for the production of sufficiently short ion pulses. While the generation of Ar q+ ions was targeted in previous work, it has in the meantime been demonstrated that argon is not suitable due to extensive cluster formation in the supersonic expansion. Here, we therefore present numerical simulations investigating the use of neon as a precursor gas and show the feasibility of pulses containing up to ∼1000 Ne + ions at keV energies and picosecond duration. In the process, we demonstrate that space charge broadening can be significantly reduced by detuning the flight time focusing conditions of an ion bunching system. Moreover, the results show that a controlled variation of the buncher geometry and potentials permits the generation of picosecond pulses at variable ion energy between 1 and 5 keV.

The impact of an energetic particle onto a solid surface generates a strongly perturbed and extremely localized non-equilibrium state, which relaxes on extremely fast time scales. In order to facilitate a time-resolved observation of the relaxation dynamics using established ultrafast pump-probe techniques, it is necessary to pinpoint the projectile impact in time with sufficient accuracy. In this paper, we propose a concept to generate ultrashort ion pulses via femtosecond photoionization of rare gas atoms entrained in a supersonic jet, combined with ion optical bunching of the resulting ion package. We calculate the photoion cloud generated by an intense focused laser pulse and show that Arq+ ions with q = 1-5 can be generated with a standard table-top laser system, which are then accelerated to energies in the keV range over a very short distance and bunched to impinge onto the target surface in a time-focused manner. Detailed ion trajectory simulations show that single ion pulses of sub-picosecond duration can be generated this way. The influence of space charge broadening is included in the simulations, which reveal that flight time broadening is insignificant for pulses containing up to 10-20 ions and starts to increase the pulse width above ∼50 ions/pulse.

Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Rather than evaluate the predictions of a single simulation approach, the Sandia Fracture Challenge relies on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014 ) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in ∼ 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. Additional shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.

This work investigates the effects of semicrystaline poly(1-butene) on amorphous polylactide in relation to mechanical, thermal and rheological properties. The blends were prepared by melt mixing, and poly(1-butene) content ranged from 5 to 40 wt %. It was found that 5 wt % poly(1-butene) heightened impact strength by more than double, however, other mechanical properties decreased. Poly(1-butene) significantly affected the glass transition temperature of polylactide, while the melting temperature of blends with 5 and 15 wt % poly(1-butene) slightly decreased. Thermogravimetric measurements revealed that the thermal stability of polylactide improved if poly(1-butene) was present, and rheological properties exhibited enhanced complex viscosity in combination with dynamic moduli.

The aim of this study was elimination of false positive results obtained by the Chlamylege kit. Two serological kits (IgM ELISA L. pneumophila sgs1-7; ImmuView(TM) L. pneumophila sg1/sg3) and pre-absorption tests (with L. pneumophila sg1 and sg3 reference strains antigens) were used. 153 sera (79 patients) were examined. The high correlations were found between the results by both tests. Positive results by ELISA (sgs1-7) were found in 19/79 patients; by ImmuView(TM) (sg1+sg3) in 16/63. In 8 patients, the dynamics of the IgM in pairs of sera was high (ratio ≥2). In 5/8 of those patients seroconversion was determined. Selected pre-absorbed sera (15 pairs) were tested simultaneously by the same tests. In 8/15 pairs of sera, the reduction of IgM levels in pre-absorbed sera was higher than 10. The reduction of IgM differed in sg1 and sg3 tests. The probability of infections due to L. pneumophila sg3 (7 patients) and L. pneumophila sg1 (5 patients) was based on the results of pre-absorption tests. The correlation between ELISA and ImmuView(TM) tests of pre-absorbed sera was statistically significant (Po=0.0389). Moreover, genotyping of L. pneumophila (SBT) directly in the sera of selected 15 patients (high IgM reduction) was carried out. Completed 7 alleles profile (ST36) was determined in one patient. However, a second patient had the same profile of 5 alleles, and similar reactions in pre-absorption tests. At least 4 sources of infections were suggested on the base of genotyping and pre-absorption results. Positive results obtained by molecular techniques (eg.PCR) in the diagnosis of Legionella infections should be supplemented by other tests for confirmation of legionellosis. The sequence based typing carried out directly in clinical specimens seems to be a promising method.

A time resolved experiment to investigate the ultrafast dynamics following an ion impact onto a solid surface requires an ultrashort ion pump pulse in combination with a properly synchronized and time resolved probe. In order to realize such an experiment, we have investigated a strategy to use femtosecond laser photoionization of atoms entrained in a pulsed supersonic jet for the production of sufficiently short ion pulses. While the generation of Ar\\(^{q+}\\) ions was targeted in previous work, it has in the meantime been demonstrated that argon is not suitable due to extensive cluster formation in the supersonic expansion. Here, we therefore present numerical simulations investigating the use of neon as a precursor gas and show the feasibility of pulses containing up to ~1000 Ne\\(^{q+}\\) ions at keV energies and picosecond duration. In the process, we demonstrate that space charge broadening can be significantly reduced by detuning the flight time focusing conditions of an ion bunching system. Moreover, the results show that a controlled variation of the buncher geometry and potentials permits the generation of picosecond pulses at variable ion energy between 1 keV and 5 keV.

Commonly used for Parkinson’s disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here, we examine whether functional magnetic resonance imaging (fMRI) can be used to predict optimal stimulation settings for individual patients. We analyze 3 T fMRI data prospectively acquired as part of an observational trial in 67 PD patients using optimal and non-optimal stimulation settings. Clinically optimal stimulation produces a characteristic fMRI brain response pattern marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen datasets: a priori clinically optimized and stimulation-naïve PD patients. We propose that fMRI brain responses to DBS stimulation in PD patients could represent an objective biomarker of clinical response. Upon further validation with additional studies, these findings may open the door to functional imaging-assisted DBS programming. Deep brain stimulation programming for Parkinson’s disease entails the assessment of a large number of possible simulation settings, requiring numerous clinic visits after surgery. Here, the authors show that patterns of functional MRI can predict the optimal stimulation settings.

Background Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world – lung cancer, their interrelations are not well understood. Digital pathology data provides a unique insight into the spatial composition of the TME. Various spatial metrics and machine learning approaches were proposed for prediction of either patient survival or gene mutations from this data. Still, these approaches are limited in the scope of analyzed features and in their explainability, and as such fail to transfer to clinical practice. Methods Here, we generated 23,199 image patches from 26 hematoxylin-and-eosin (H&E)-stained lung cancer tissue sections and annotated them into 9 different tissue classes. Using this dataset, we trained a deep neural network ARA-CNN. Next, we applied the trained network to segment 467 lung cancer H&E images from The Cancer Genome Atlas (TCGA) database. We used the segmented images to compute human-interpretable features reflecting the heterogeneous composition of the TME, and successfully utilized them to predict patient survival and cancer gene mutations. Results We achieved per-class AUC ranging from 0.72 to 0.99 for classifying tissue types in lung cancer with ARA-CNN. Machine learning models trained on the proposed human-interpretable features achieved a c-index of 0.723 in the task of survival prediction and AUC up to 73.5% for PDGFRB in the task of mutation classification. Conclusions We presented a framework that accurately predicted survival and gene mutations in lung adenocarcinoma patients based on human-interpretable features extracted from H&E slides. Our approach can provide important insights for designing novel cancer treatments, by linking the spatial structure of the TME in lung adenocarcinoma to gene mutations and patient survival. It can also expand our understanding of the effects that the TME has on tumor evolutionary processes. Our approach can be generalized to different cancer types to inform precision medicine strategies.

The LHCb experiment has been taking data at the Large Hadron Collider (LHC) at CERN since the end of 2009. One of its key detector components is the Ring-Imaging Cherenkov (RICH) system. This provides charged particle identification over a wide momentum range, from 2–100 GeV/ c . The operation and control, software, and online monitoring of the RICH system are described. The particle identification performance is presented, as measured using data from the LHC. Excellent separation of hadronic particle types ( π , K, p) is achieved.

Background/Aims: The permeability transition pore (PTP) is an unselective, Ca 2+ -dependent high conductance channel of the inner mitochondrial membrane whose molecular identity has long remained a mystery. The most recent hypothesis is that pore formation involves the F-ATP synthase, which consistently generates Ca 2+ -activated channels. Available structures do not display obvious features that can accommodate a channel; thus, how the pore can form and whether its activity can be entirely assigned to F-ATP synthase is the matter of debate. In this study, we investigated the role of F-ATP synthase subunits e, g and b in PTP formation. Methods: Yeast null mutants for e, g and the first transmembrane (TM) α-helix of subunit b were generated and evaluated for mitochondrial morphology (electron microscopy), membrane potential (Rhodamine123 fluorescence) and respiration (Clark electrode). Homoplasmic C23S mutant of subunit a was generated by in vitro mutagenesis followed by biolistic transformation. F-ATP synthase assembly was evaluated by BN-PAGE analysis. Cu 2+ treatment was used to induce the formation of F-ATP synthase dimers in the absence of e and g subunits. The electrophysiological properties of F-ATP synthase were assessed in planar lipid bilayers. Results: Null mutants for the subunits e and g display dimer formation upon Cu 2+ treatment and show PTP-dependent mitochondrial Ca 2+ release but not swelling. Cu 2+ treatment causes formation of disulfide bridges between Cys23 of subunits a that stabilize dimers in absence of e and g subunits and favors the open state of wild-type F-ATP synthase channels. Absence of e and g subunits decreases conductance of the F-ATP synthase channel about tenfold. Ablation of the first TM of subunit b, which creates a distinct lateral domain with e and g, further affected channel activity. Conclusion: F-ATP synthase e, g and b subunits create a domain within the membrane that is critical for the generation of the high-conductance channel, thus is a prime candidate for PTP formation. Subunits e and g are only present in eukaryotes and may have evolved to confer this novel function to F-ATP synthase.