Resonant hyper-Raman scattering in spherical quantum dots

A theoretical model of resonant hyper-Raman scattering by an ensemble of spherical semiconductor quantum dots has been developed. The electronic intermediate states are described as Wannier-Mott excitons in the framework of the envelope function approximation. The optical polar vibrational modes of the nanocrystallites (vibrons) and their interaction with the electronic system are analized with the help of a continuum model satisfying both the mechanical and electrostatic matching conditions at the interface. An explicit expression for the hyper-Raman scattering efficiency is derived, which is valid for incident two-photon energy close to the exciton resonances. The dipole selection rules for optical transitions and Fr\"ohlich-like exciton-lattice interaction are derived: It is shown that only exciton states with total angular momentum $L=0,1$ and vibrational modes with angular momentum $l_p=1$ contribute to the hyper-Raman scattering process. The associated exciton energies, wavefunctions, and vibron frequencies have been obtained for spherical CdSe zincblende-type nanocrystals, and the corresponding hyper-Raman scattering spectrum and resonance profile are calculated. Their dependence on the dot radius and the influence of the size distribution on them are also discussed.Comment: 12 pages REVTeX (two columns), 2 tables, 8 figure

Programmable antivirals targeting critical conserved viral RNA secondary structures from influenza A virus and SARS-CoV-2

Influenza A virus’s (IAV’s) frequent genetic changes challenge vaccine strategies and engender resistance to current drugs. We sought to identify conserved and essential RNA secondary structures within IAV’s genome that are predicted to have greater constraints on mutation in response to therapeutic targeting. We identified and genetically validated an RNA structure (packaging stem–loop 2 (PSL2)) that mediates in vitro packaging and in vivo disease and is conserved across all known IAV isolates. A PSL2-targeting locked nucleic acid (LNA), administered 3 d after, or 14 d before, a lethal IAV inoculum provided 100% survival in mice, led to the development of strong immunity to rechallenge with a tenfold lethal inoculum, evaded attempts to select for resistance and retained full potency against neuraminidase inhibitor-resistant virus. Use of an analogous approach to target SARS-CoV-2, prophylactic administration of LNAs specific for highly conserved RNA structures in the viral genome, protected hamsters from efficient transmission of the SARS-CoV-2 USA_WA1/2020 variant. These findings highlight the potential applicability of this approach to any virus of interest via a process we term ‘programmable antivirals’, with implications for antiviral prophylaxis and post-exposure therapy

Facile discovery of surrogate cytokine agonists

Cytokines are powerful immune modulators that initiate signaling through receptor dimerization, but natural cytokines have structural limitations as therapeutics. We present a strategy to discover cytokine surrogate agonists by using modular ligands that exploit induced proximity and receptor dimer geometry as pharmacological metrics amenable to high-throughput screening. Using VHH and scFv to human interleukin-2/15, type-I interferon, and interleukin-10 receptors, we generated combinatorial matrices of single-chain bispecific ligands that exhibited diverse spectrums of functional activities, including potent inhibition of SARS-CoV-2 by surrogate interferons. Crystal structures of IL-2R:VHH complexes revealed that variation in receptor dimer geometries resulted in functionally diverse signaling outputs. This modular platform enabled engineering of surrogate ligands that compelled assembly of an IL-2R/IL-10R heterodimer, which does not naturally exist, that signaled through pSTAT5 on T and natural killer (NK) cells. This “cytokine med-chem” approach, rooted in principles of induced proximity, is generalizable for discovery of diversified agonists for many ligand-receptor systems

Lattice Dynamics of II-VI materials using adiabatic bond charge model

We extend the adiabatic bond charge model, originally developed for group IV semiconductors and III-V compounds, to study phonons in more ionic II-VI compounds with a zincblende structure. Phonon spectra, density of states and specific heats are calculated for six II-VI compounds and compared with both experimental data and the results of other models. We show that the 6-parameter bond charge model gives a good description of the lattice dynamics of these materials. We also discuss trends in the parameters with respect to the ionicity and metallicity of these compounds.Comment: 16 pages of RevTex with 3 figures submitted as a uuencode compressed tar fil

Reciprocity and the tragedies of maintaining and providing the commons

Social cooperation often requires collectively beneficial but individually costly restraint to maintain a public good, or it needs costly generosity to create one. Status quo effects predict that maintaining a public good is easier than providing a new one. Here, we show experimentally and with simulations that even under identical incentives, low levels of cooperation (the ‘tragedy of the commons’) are systematically more likely in maintenance than provision. Across three series of experiments, we find that strong and weak positive reciprocity, known to be fundamental tendencies underpinning human cooperation, are substantially diminished under maintenance compared with provision. As we show in a fourth experiment, the opposite holds for negative reciprocity (‘punishment’). Our findings suggest that incentives to avoid the ‘tragedy of the commons’ need to contend with dilemma specific reciprocity

Broad-spectrum CRISPR-mediated inhibition of SARS-CoV-2 variants and endemic coronaviruses in vitro

A major challenge in coronavirus vaccination and treatment is to counteract rapid viral evolution and mutations. Here we demonstrate that CRISPR-Cas13d offers a broad-spectrum antiviral (BSA) to inhibit many SARS-CoV-2 variants and diverse human coronavirus strains with >99% reduction of the viral titer. We show that Cas13d-mediated coronavirus inhibition is dependent on the crRNA cellular spatial colocalization with Cas13d and target viral RNA. Cas13d can significantly enhance the therapeutic effects of diverse small molecule drugs against coronaviruses for prophylaxis or treatment purposes, and the best combination reduced viral titer by over four orders of magnitude. Using lipid nanoparticle-mediated RNA delivery, we demonstrate that the Cas13d system can effectively treat infection from multiple variants of coronavirus, including Omicron SARS-CoV-2, in human primary airway epithelium air-liquid interface (ALI) cultures. Our study establishes CRISPR-Cas13 as a BSA which is highly complementary to existing vaccination and antiviral treatment strategies

Efficient Learning and Feature Selection in High Dimensional Regression

We present a novel algorithm for efficient learning and feature selection in high-dimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a probabilistic version of the well-known statistical regression technique of backfitting. Using the expectation-maximization algorithm, along with variational approximation methods to overcome intractability, we extend our algorithm to include automatic relevance detection of the input features. This variational Bayesian least squares (VBLS) approach retains its simplicity as a linear model, but offers a novel statistically robust black-box approach to generalized linear regression with high-dimensional inputs. It can be easily extended to nonlinear regression and classification problems. In particular, we derive the framework of sparse Bayesian learning, the relevance vector machine, with VBLS at its core, offering significant computational and robustness advantages for this class of methods. The iterative nature of VBLS makes it most suitable for real-time incremental learning, which is crucial especially in the application domain of robotics, brain-machine interfaces, and neural prosthetics, where real-time learning of models for control is needed. We evaluate our algorithm on synthetic and neurophysiological data sets, as well as on standard regression and classification benchmark data sets, comparing it with other competitive statistical approaches and demonstrating its suitability as a drop-in replacement for other generalized linear regression techniques