The PyPES library of high quality semi‐global potential energy surfaces

In this article, we present a Python‐based library of high quality semi‐global potential energy surfaces for 50 polyatomic molecules with up to six atoms. We anticipate that these surfaces will find widespread application in the testing of new potential energy surface construction algorithms and nuclear ro‐vibrational structure theories. To this end, we provide the ability to generate the energy derivatives required for Taylor series expansions to sixth order about any point on the potential energy surface in a range of common coordinate systems, including curvilinear internal, Cartesian, and normal mode coordinates. The PyPES package, along with FORTRAN, C, MATLAB and Mathematica wrappers, is available at http://sourceforge.net/projects/pypes-lib . © 2015 Wiley Periodicals, Inc.     

A Universal DNA Aptamer that Recognizes Spike Proteins of Diverse SARS-CoV-2 Variants of Concern

We report on a unique DNA aptamer, denoted MSA52, that displays universally high affinity for the spike proteins of wildtype SARS-CoV-2 as well as the Alpha, Beta, Gamma, Epsilon, Kappa, Delta and Omicron variants. Using an aptamer pool produced from round 13 of selection against the S1 domain of the wildtype spike protein, we carried out one-round SELEX experiments using five different trimeric spike proteins from variants, followed by high-throughput sequencing and sequence alignment analysis of aptamers that formed complexes with all proteins. A previously unidentified aptamer, MSA52, showed K_d values ranging from 2 to 10 nM for all variant spike proteins, and also bound similarly to variants not present in the reselection experiments. This aptamer also recognized pseudotyped lentiviruses (PL) expressing eight different spike proteins of SARS-CoV-2 with K_d values between 20 and 50 pM, and was integrated into a simple colorimetric assay for detection of multiple PL variants. This discovery provides evidence that aptamers can be generated with high affinity to multiple variants of a single protein, including emerging variants, making it well-suited for molecular recognition of rapidly evolving targets such as those found in SARS-CoV-2.     

The Cancer Genetics Network: recruitment results and pilot studies

OBJECTIVE: The National Cancer Institute established the Cancer Genetics Network (CGN) to support collaborative investigations into the genetic basis of cancer susceptibility, explore mechanisms to integrate this new knowledge into medical practice, and identify ways of addressing the associated psychosocial, ethical, legal, and public health issues. SUBJECTS AND METHODS: The CGN has developed the complex infrastructure required to support the projects, including the establishment of guidelines and policies, uniform methods, standard questionnaires to be used by all of the centers, and a standard format for submission of data to the Informatics Center. Cancer patients and their family members have been invited to enroll and be included in a pool of potential study participants. The Information Technology Group is responsible for support of the design, implementation, and maintenance of the multicenter Network-wide research protocols. RESULTS: As of January 2004, the CGN contained data on 23,995 probands (participants) and 425,798 family members. As a resource for cancer genetic studies, the CGN has a large number of probands and first-degree relatives with and without cancer and with multiple ethnicities. Different study designs can be used including case-control, case-case, and family studies. CONCLUSIONS: The unique resources of the CGN are available for studies on cancer genetic susceptibility, translational research, and behavioral research. The CGN is now at a point where approved collaborators may have access to enrolled patients and their families for special studies, as well as to the clinical, environmental and family cancer history data banked in the Informatics Center.     

Predicting the Outcome of Photocyclisation Reactions: A Joint Experimental and Computational Investigation

Photochemical oxidative cyclodehydrogenation reactions are a versatile class of aromatic ring‐forming reactions. They are tolerant to functional group substitution and heteroatom inclusion, so can be used to form a diverse range of extended polyaromatic systems by fusing existing ring substituents. However, despite their undoubted synthetic utility, there are no existing models—computational or heuristic—that predict the outcome of photocyclisation reactions across all possible classes of reactants. This can be traced back to the fact that “negative” results are rarely published in the synthetic literature and the lack of a general conceptual framework for understanding how photoexcitation affects reactivity. In this work, we address both of these issues. We present experimental data for a series of aromatically substituted pyrroles and indoles, and show that quantifying induced atomic forces upon photoexcitation provides a powerful predictive model for determining whether a given reactant will photoplanarise and hence proceed to photocyclised product under appropriate reaction conditions. The propensity of a molecule to photoplanarise is related to localised changes in charge distribution around the putative forming ring upon photoexcitation. This is promoted by asymmetry in molecular structures and/or charge distributions, inclusion of heteroatoms and ethylene bridging and well‐separated or isolated photocyclisation sites.     

Synthesis of High χ–Low N Diblock Copolymers by Polymerization-Induced Self-Assembly

Polymerization-induced self-assembly (PISA) enables the scalable synthesis of functional block copolymer nanoparticles with various morphologies. Herein we exploit this versatile technique to produce so-called “high χ–low N” diblock copolymers that undergo nanoscale phase separation in the solid state to produce sub-10 nm surface features. By varying the degree of polymerization of the stabilizer and core-forming blocks, PISA provides rapid access to a wide range of diblock copolymers, and enables fundamental thermodynamic parameters to be determined. In addition, the pre-organization of copolymer chains within sterically-stabilized nanoparticles that occurs during PISA leads to enhanced phase separation relative to that achieved using solution-cast molecularly-dissolved copolymer chains.     

Re-casting traditional organic experiments into green guided-inquiry based experiments: student perceptions

ABSTRACT

Green Chemistry principles can be used to re-cast traditional Organic chemistry experiments into more guided-inquiry based experiments. Inquiry questions related to green chemistry principles and metrics have been incorporated into our laboratory for the development of more guided-inquiry based experiments. Re-casting traditional experiments provides time for guided-inquiry by allowing students to evaluate reaction conditions and wastefulness of reactions. This includes evaluating solvent choices, heating methods, use of renewal materials, and contemplating reactants and products impacts on human health and environment. Students examine the changes as it pertains to green chemistry, the success of the reaction and the potential impacts on the mechanism. Involving students in these discoveries rooted in a guiding question made the Organic experiments guided-inquiry. Students were surveyed about their exposure to green chemistry and guided-inquiry based labs. Examples of some of the re-casted experiments, excerpts from student reports, and student impressions of the theme are presented.