Induced log-concavity of equivariant matroid invariants

Inspired by the notion of equivariant log-concavity, we introduce the concept of induced log-concavity for a sequence of representations of a finite group. For an equivariant matroid equipped with a symmetric group action or a finite general linear group action, we transform the problem of proving the induced log-concavity of matroid invariants to that of proving the Schur positivity of symmetric functions. We prove the induced log-concavity of the equivariant Kazhdan-Lusztig polynomials of $q$-niform matroids equipped with the action of a finite general linear group, as well as that of the equivariant Kazhdan-Lusztig polynomials of uniform matroids equipped with the action of a symmetric group. As a consequence of the former, we obtain the log-concavity of Kazhdan-Lusztig polynomials of $q$-niform matroids, thus providing further positive evidence for Elias, Proudfoot and Wakefield's log-concavity conjecture on the matroid Kazhdan-Lusztig polynomials. From the latter we obtain the log-concavity of Kazhdan-Lusztig polynomials of uniform matroids, which was recently proved by Xie and Zhang by using a computer algebra approach. We also establish the induced log-concavity of the equivariant characteristic polynomials and the equivariant inverse Kazhdan-Lusztig polynomials for $q$-niform matroids and uniform matroids.Comment: 36 page

Designing all-graphene nanojunctions by covalent functionalization

We investigated theoretically the effect of covalent edge functionalization, with organic functional groups, on the electronic properties of graphene nanostructures and nano-junctions. Our analysis shows that functionalization can be designed to tune electron affinities and ionization potentials of graphene flakes, and to control the energy alignment of frontier orbitals in nanometer-wide graphene junctions. The stability of the proposed mechanism is discussed with respect to the functional groups, their number as well as the width of graphene nanostructures. The results of our work indicate that different level alignments can be obtained and engineered in order to realize stable all-graphene nanodevices

The solvation and dissociation of 4-benzylaniline hydrochloride in chlorobenzene

A reaction scheme is proposed to account for the liberation of 4-benzylaniline from 4-benzylaniline hydrochloride, using chlorobenzene as a solvent at a temperature of 373 K. Two operational regimes are explored: “closed” reaction conditions correspond to the retention of evolved hydrogen chloride gas within the reaction medium, whereas an “open” system permits gaseous hydrogen chloride to be released from the reaction medium. The solution phase chemistry is analyzed by 1H NMR spectroscopy. Complete liberation of solvated 4-benzylaniline from solid 4-benzylaniline hydrochloride is possible under “open” conditions, with the entropically favored conversion of solvated hydrogen chloride to the gaseous phase thought to be the thermodynamic driver that effectively controls a series of interconnecting equilibria. A kinetic model is proposed to account for the observations of the open system

Optical properties and charge-transfer excitations in edge-functionalized all-graphene nanojunctions

We investigate the optical properties of edge-functionalized graphene nanosystems, focusing on the formation of junctions and charge transfer excitons. We consider a class of graphene structures which combine the main electronic features of graphene with the wide tunability of large polycyclic aromatic hydrocarbons. By investigating prototypical ribbon-like systems, we show that, upon convenient choice of functional groups, low energy excitations with remarkable charge transfer character and large oscillator strength are obtained. These properties can be further modulated through an appropriate width variation, thus spanning a wide range in the low-energy region of the UV-Vis spectra. Our results are relevant in view of designing all-graphene optoelectronic nanodevices, which take advantage of the versatility of molecular functionalization, together with the stability and the electronic properties of graphene nanostructures.Comment: J. Phys. Chem. Lett. (2011), in pres

MARCKS phosphorylation is modulated by a peptide mimetic of MARCKS effector domain leading to increased radiation sensitivity in lung cancer cell lines

Lung cancer is the leading cause of cancer-associated mortality in the United States. Kinase hyperactivation is a known mechanism of tumorigenesis. The phosphorylation status of the plasma membrane-associated protein myristoylated alanine rich C-kinase substrate (MARCKS) effector domain (ED) was previously established as being important in the sensitivity of lung cancer to radiation. Specifically, when MARCKS ED was in a non-phosphorylated state, lung cancer cells were more susceptible to ionizing radiation and experienced prolonged double-strand DNA breaks. Additional studies demonstrated that the phosphorylation status of MARCKS ED is important for gene expression and in vivo tumor growth. The present study used a peptide mimetic of MARCKS ED as a therapeutic intervention to modulate MARCKS phosphorylation. Culturing A549, H1792 and H1975 lung cancer cell lines with the MARCKS ED peptide led to reduced levels of phosphorylated MARCKS and phosphorylated Akt serine/threonine kinase 1. Further investigation demonstrated that the peptide therapy was able to reduce lung cancer cell proliferation and increase radiation sensitivity. In addition, the MARCKS peptide therapy was able to prolong double-strand DNA breaks following ionizing radiation exposure. The results of the present study demonstrate that a peptide mimetic of MARCKS ED is able to modulate MARCKS phosphorylation, leading to an increase in sensitivity to radiation. Keywords: lung cancer, myristoylated alanine rich C-kinase substrate, radiation sensitivity, effector domain, peptide mimeti

Measurement of pion, kaon and proton production in proton-proton collisions at s=7\sqrt{s}=7 TeV

The measurement of primary $\pi^{\pm}$, K$^{\pm}$, p and $\overline{p}$ production at mid-rapidity ($|y| <$ 0.5) in proton-proton collisions at $\sqrt{s} = 7$ TeV performed with ALICE (A Large Ion Collider Experiment) at the Large Hadron Collider (LHC) is reported. Particle identification is performed using the specific ionization energy loss and time-of-flight information, the ring-imaging Cherenkov technique and the kink-topology identification of weak decays of charged kaons. Transverse momentum spectra are measured from 0.1 up to 3 GeV/$c$ for pions, from 0.2 up to 6 GeV/$c$ for kaons and from 0.3 up to 6 GeV/$c$ for protons. The measured spectra and particle ratios are compared with QCD-inspired models, tuned to reproduce also the earlier measurements performed at the LHC. Furthermore, the integrated particle yields and ratios as well as the average transverse momenta are compared with results at lower collision energies.Comment: 33 pages, 19 captioned figures, 3 tables, authors from page 28, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/156

Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science

This paper describes a machine learning and data science pipeline for structured information extraction from documents, implemented as a suite of open-source tools and extensions to existing tools. It centers around a methodology for extracting procedural information in the form of recipes, stepwise procedures for creating an artifact (in this case synthesizing a nanomaterial), from published scientific literature. From our overall goal of producing recipes from free text, we derive the technical objectives of a system consisting of pipeline stages: document acquisition and filtering, payload extraction, recipe step extraction as a relationship extraction task, recipe assembly, and presentation through an information retrieval interface with question answering (QA) functionality. This system meets computational information and knowledge management (CIKM) requirements of metadata-driven payload extraction, named entity extraction, and relationship extraction from text. Functional contributions described in this paper include semi-supervised machine learning methods for PDF filtering and payload extraction tasks, followed by structured extraction and data transformation tasks beginning with section extraction, recipe steps as information tuples, and finally assembled recipes. Measurable objective criteria for extraction quality include precision and recall of recipe steps, ordering constraints, and QA accuracy, precision, and recall. Results, key novel contributions, and significant open problems derived from this work center around the attribution of these holistic quality measures to specific machine learning and inference stages of the pipeline, each with their performance measures. The desired recipes contain identified preconditions, material inputs, and operations, and constitute the overall output generated by our computational information and knowledge management (CIKM) system.Comment: 15th International Conference on Document Analysis and Recognition Workshops (ICDARW 2019