Vector-based feedback of continuous wave radiofrequency compression cavity for ultrafast electron diffraction.

The temporal resolution of ultrafast electron diffraction at weakly relativistic beam energies (≲100 keV) suffers from space-charge induced electron pulse broadening. We describe the implementation of a radio frequency (RF) cavity operating in the continuous wave regime to compress high repetition rate electron bunches from a 40.4 kV DC photoinjector for ultrafast electron diffraction applications. Active stabilization of the RF amplitude and phase through a feedback loop based on the demodulated in-phase and quadrature components of the RF signal is demonstrated. This scheme yields 144 ± 19 fs RMS temporal resolution in pump-probe studies.

Hierarchical Organization of Single Crystal Polymorphs of Azobenzene Chromophore in Anisotropic Media Subjected to Local Thermal Gradients upon Cold Spraying.

The present article entails the emergence of diverse crystal polymorphs following thermal quenching into various coexistence regions of binary azobenzene chromophore (ACh)/diacrylate (DA) solution and of azobenzene/nematic liquid crystal (E7) mixture. Development of various crystal topologies encompassing rhomboidal and hexagonal shapes can be witnessed in a manner dependent on thermal quenched depths into the crystal + liquid coexistence region of ACh/DA system. Upon spraying with compressed carbon dioxide (CO2 ) fluid, the local temperature gradient is generated resulting in spherulitic morphology showing discrete lamellae undergoing twisting locally in some regions and branched dendrites or seaweeds in another. When ACh/E7 blend is sprayed using compressed CO2 fluid, hierarchical organization of various discrete faceted single crystals including needle, rectangular, rhombus, and truncated hexagonal crystals radiating from the spherulite core can be discerned in a brighter region (off cross-polarization) polarized optical microscopy (POM) and nematic disclination in a darker cross-polarized region. Of particular interest is that the observed faceted single-crystal polymorphs in ACh/E7 may be contrasted to the lamellar twisting and branching observed in the ACh/DA system and plausible mechanisms of polymer spherulitic growth are discussed.

Machine Learning Algorithms Evaluate Immune Response to Novel Mycobacterium tuberculosis Antigens for Diagnosis of Tuberculosis.

Rationale: Tuberculosis diagnosis in children remains challenging. Microbiological confirmation of tuberculosis disease is often lacking, and standard immunodiagnostic including the tuberculin skin test and interferon-γ release assay for tuberculosis infection has limited sensitivity. Recent research suggests that inclusion of novel Mycobacterium tuberculosis antigens has the potential to improve standard immunodiagnostic tests for tuberculosis. Objective: To identify optimal antigen-cytokine combinations using novel Mycobacterium tuberculosis antigens and cytokine read-outs by machine learning algorithms to improve immunodiagnostic assays for tuberculosis. Methods: A total of 80 children undergoing investigation of tuberculosis were included (15 confirmed tuberculosis disease, five unconfirmed tuberculosis disease, 28 tuberculosis infection and 32 unlikely tuberculosis). Whole blood was stimulated with 10 novel Mycobacterium tuberculosis antigens and a fusion protein of early secretory antigenic target (ESAT)-6 and culture filtrate protein (CFP) 10. Cytokines were measured using xMAP multiplex assays. Machine learning algorithms defined a discriminative classifier with performance measured using area under the receiver operating characteristics. Measurements and main results: We found the following four antigen-cytokine pairs had a higher weight in the discriminative classifier compared to the standard ESAT-6/CFP-10-induced interferon-γ: Rv2346/47c- and Rv3614/15c-induced interferon-gamma inducible protein-10; Rv2031c-induced granulocyte-macrophage colony-stimulating factor and ESAT-6/CFP-10-induced tumor necrosis factor-α. A combination of the 10 best antigen-cytokine pairs resulted in area under the curve of 0.92 ± 0.04. Conclusion: We exploited the use of machine learning algorithms as a key tool to evaluate large immunological datasets. This identified several antigen-cytokine pairs with the potential to improve immunodiagnostic tests for tuberculosis in children.