Chiral Ionic Liquids: Synthesis, Properties, and Enantiomeric Recognition

We have synthesized a series of structurally novel chiral ionic liquids which have a either chiral cation, chiral anion, or both. Cations are an imidazolium group, while anions are based on a borate ion with spiral structure and chiral substituents. Both (or all) stereoisomeric forms of each compound in the series can be readily synthesized in optically pure form by a simple one-step process from commercially available reagents. In addition to the ease of preparation, most of the chiral ILs in this series are liquid at room temperature with a solid to liquid transformation temperature as low as −70 °C and have relatively high thermal stability (up to at least 300 °C). Circular dichroism and X-ray crystallographic results confirm that the reaction to form the chiral spiral borate anion is stereospecific, namely, only one of two possible spiral stereoisomers was formed. Results of NMR studies including 1H{15N} heteronuclear single quantum coherence (HSQC) show that these chiral ILs exhibit intramolecular as well as intermolecular enantiomeric recognition. Intramolecularly, the chiral anion of an IL was found to exhibit chiral recognition toward the cation. Specifically, for a chiral IL composing with a chiral anion and a racemic cation, enantiomeric recognition of the chiral anion toward both enantiomers of the cation lead to pronounced differences in the NMR bands of the cation enantiomers. The chiral recognition was found to be dependent on solvent dielectric constant, concentration, and structure of the ILs. Stronger enantiomeric recognition was found in solvent with relatively lower dielectric constants (CDCl3 compared to CD3CN) and at higher concentration of ILs. Also, stronger chiral recognition was found for anions with a relatively larger substituent group (e.g., chiral anion with a phenylmethyl group exhibits stronger chiral recognition compared to that with a phenyl group, and an anion with an isobutyl group has the weakest chiral recognition). Chiral anions were also found to exhibit intermolecular chiral recognition. Enantiomeric discrimination was found for a chiral IL composed of a chiral anion and achiral cation toward another chiral molecule such as a quinine derivative

Aubry-Mather measures in the non convex setting

The adjoint method, introduced in [L. C. Evans, Arch. Ration. Mech. Anal., 197 (2010), pp. 1053–1088] and [H. V. Tran, Calc. Var. Partial Differential Equations, 41 (2011), pp. 301–319], is used to construct analogues to the Aubry–Mather measures for nonconvex Hamiltonians. More precisely, a general construction of probability measures, which in the convex setting agree with Mather measures, is provided. These measures may fail to be invariant under the Hamiltonian flow and a dissipation arises, which is described by a positive semidefinite matrix of Borel measures. However, in the case of uniformly quasiconvex Hamiltonians the dissipation vanishes, and as a consequence the invariance is guaranteed. Copyright © 2011 Society for Industrial and Applied Mathematic

Towards Explainability of UAV-Based Convolutional Neural Networks for Object Classification

f autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautical Solutions (CAS) Project. One critical research element of ATTRACTOR is explainability of the decision-making across relevant subsystems of an autonomous system. The ability to explain why an autonomous system makes a decision is needed to establish a basis of trustworthiness to safely complete a mission. Convolutional Neural Networks (CNNs) are popular visual object classifiers that have achieved high levels of classification performances without clear insight into the mechanisms of the internal layers and features. To explore the explainability of the internal components of CNNs, we reviewed three feature visualization methods in a layer-by-layer approach using aviation related images as inputs. Our approach to this is to analyze the key components of a classification event in order to generate component labels for features of the classified image at different layers of depths. For example, an airplane has wings, engines, and landing gear. These could possibly be identified somewhere in the hidden layers from the classification and these descriptive labels could be provided to a human or machine teammate while conducting a shared mission and to engender trust. Each descriptive feature may also be decomposed to a combination of primitives such as shapes and lines. We expect that knowing the combination of shapes and parts that create a classification will enable trust in the system and insight into creating better structures for the CNN

A baseband residual vector quantization algorithm for voiceband data signals

Journal ArticleAbstract-In this paper, we present a new approach to the digitization and compression of a class of voiceband modem signals. Our approach, which we call baseband residual vector quantization (BRVQ), relies heavily upon the simple structure present in a modem signal. After the signal is converted to baseband, the magnitude sequence and the sequence of residuals obtained when the phase within each baud of the baseband signal is modeled by a straight line are separately vector quantized. In order to carry out these operations, we developed the new carrier-frequency estimation and baud-rate classification schemes described in the paper. Experimental results show that the performance of the BRVQ system at and below 16 kbits/s is better than that of a previously developed vector quantization scheme that has itself been shown to outperform traditional speech-compression techniques such as adaptive predictive coding, adaptive transform coding, and subband coding when these techniques are used to compress modem signals

Chemical pre-processing of cluster galaxies over the past 10 billion years in the IllustrisTNG simulations

We use the IllustrisTNG simulations to investigate the evolution of the mass-metallicity relation (MZR) for star-forming cluster galaxies as a function of the formation history of their cluster host. The simulations predict an enhancement in the gas-phase metallicities of star-forming cluster galaxies (10^9< M_star<10^10 M_sun) at z<1.0 in comparisons to field galaxies. This is qualitatively consistent with observations. We find that the metallicity enhancement of cluster galaxies appears prior to their infall into the central cluster potential, indicating for the first time a systematic "chemical pre-processing" signature for {\it infalling} cluster galaxies. Namely, galaxies which will fall into a cluster by z=0 show a ~0.05 dex enhancement in the MZR compared to field galaxies at z<0.5. Based on the inflow rate of gas into cluster galaxies and its metallicity, we identify that the accretion of pre-enriched gas is the key driver of the chemical evolution of such galaxies, particularly in the stellar mass range (10^9< M_star<10^10 M_sun). We see signatures of an environmental dependence of the ambient/inflowing gas metallicity which extends well outside the nominal virial radius of clusters. Our results motivate future observations looking for pre-enrichment signatures in dense environments.Comment: 5 pages, 4 figures, accepted for publication in MNRAS Letter

Thermogravimetry and neutron thermodiffractometry studies of the H-YBa2Cu3O7 system.

The high Tc superconducting oxide YBa2Cu3O7¿x reacts with hydrogen gas. Thermogravimetric, X-ray and neutron scattering experiments allow us to propose a two-step type of hydrogen bonding. Firstly, a few hydrogen atoms fill some oxygen vacancies and may favourably modify the electron state, giving rise to a slight increase in the critical temperature. Secondly, after a prolonged heating period, the collapse of the YBa2Cu3O7¿x type framework and of superconductivity were observed, and a new, highly hydrogenated material appeared

Coxiella burnetii Blocks Intracellular Interleukin-17 Signaling in Macrophages

Coxiella burnetii is an obligate intracellular bacterium and the etiological agent of Q fever. Successful host cell infection requires the Coxiella type IVB secretion system (T4BSS), which translocates bacterial effector proteins across the vacuole membrane into the host cytoplasm, where they manipulate a variety of cell processes. To identify host cell targets of Coxiella T4BSS effector proteins, we determined the transcriptome of murine alveolar macrophages infected with a Coxiella T4BSS effector mutant. We identified a set of inflammatory genes that are significantly upregulated in T4BSS mutant-infected cells compared to mock-infected cells or cells infected with wild-type (WT) bacteria, suggesting that Coxiella T4BSS effector proteins downregulate the expression of these genes. In addition, the interleukin-17 (IL-17) signaling pathway was identified as one of the top pathways affected by the bacteria. While previous studies demonstrated that IL-17 plays a protective role against several pathogens, the role of IL-17 during Coxiella infection is unknown. We found that IL-17 kills intracellular Coxiella in a dose-dependent manner, with the T4BSS mutant exhibiting significantly more sensitivity to IL-17 than WT bacteria. In addition, quantitative PCR confirmed the increased expression of IL-17 downstream signaling genes in T4BSS mutant-infected cells compared to WT- or mock-infected cells, including the proinflammatory cytokine genes Il1a, Il1b, and Tnfa, the chemokine genes Cxcl2 and Ccl5, and the antimicrobial protein gene Lcn2 We further confirmed that the Coxiella T4BSS downregulates macrophage CXCL2/macrophage inflammatory protein 2 and CCL5/RANTES protein levels following IL-17 stimulation. Together, these data suggest that Coxiella downregulates IL-17 signaling in a T4BSS-dependent manner in order to escape the macrophage immune response