Unidirectional scattering with spatial homogeneity using photonic time disorder

The temporal degree of freedom in photonics has been a recent research hotspot due to its analogy with spatial axes, causality, and open-system characteristics. In particular, the temporal analogues of photonic crystals have stimulated the design of momentum gaps and their extension to topological and non-Hermitian photonics. Although recent studies have also revealed the effect of broken discrete time-translational symmetry in view of the temporal analogy of spatial Anderson localization, the broad intermediate regime between time order and time uncorrelated disorder has not been examined. Here we investigate the inverse design of photonic time disorder to achieve optical functionalities in spatially homogeneous platforms. By developing the structure factor and order metric using causal Green's functions for the domain of time disorder, we demonstrate engineered time scatterer, which provides unidirectional scattering with controlled scattering amplitudes. We also reveal that the order-to-disorder transition in the time domain allows for the manipulation of scattering bandwidths, which inspires resonance-free temporal colour filtering. Our work will pave the way for advancing optical functionalities without spatial patterning.Comment: 22 pages, 4 figure

Transformation of (allo)securinine to (allo)norsecurinine via a molecular editing strategy

Securinega alkaloids have intrigued chemists since the isolation of securinine in 1956. This family of natural products comprises a securinane subfamily with a piperidine substructure and norsecurinane alkaloids featuring a pyrrolidine core. From a biosynthetic perspective, the piperidine moiety in securinane alkaloids derives from lysine, whereas the pyrrolidine moiety in norsecurinane natural products originates from ornithine, marking an early biogenetic divergence. Herein, we introduce a single-atom deletion strategy that enables the late-stage conversion of securinane to norsecurinane alkaloids. Notably, for the first time, this method enabled the transformation of piperidine-based (allo)securinine into pyrrolidine-based (allo)norsecurinine. Straightforward access to norsecurinine from securinine, which can be readily extracted from the plant Flueggea suffruticosa, abundant across the Korean peninsula, holds promise for synthetic studies of norsecurinine-based oligomeric securinega alkaloids

Chiral symmetry and taste symmetry from the eigenvalue spectrum of staggered Dirac operators

We investigate general properties of the eigenvalue spectrum for improved staggered quarks. We introduce a new chirality operator $[\gamma_5 \otimes 1]$ and a new shift operator $[1 \otimes \xi_5]$, which respect the same recursion relation as the $\gamma_5$ operator in the continuum. Then we show that matrix elements of the chirality operator sandwiched between two eigenstates of the staggered Dirac operator are related to those of the shift operator by the Ward identity of the conserved $U(1)_A$ symmetry of staggered fermion actions. We perform a numerical study in quenched QCD using HYP staggered quarks to demonstrate the Ward identity. We introduce a new concept of leakage patterns which collectively represent the matrix elements of the chirality operator and the shift operator sandwiched between two eigenstates of the staggered Dirac operator. The leakage pattern provides a new method to identify zero modes and non-zero modes in the Dirac eigenvalue spectrum. This method is as robust as the spectral flow method but requires much less computing power. Analysis using a machine learning technique confirms that the leakage pattern is universal, since the staggered Dirac eigenmodes on normal gauge configurations respect it. In addition, the leakage pattern can be used to determine a ratio of renormalization factors as a by-product. We conclude that it might be possible and realistic to measure the topological charge $Q$ using the Atiya-Singer index theorem and the leakage pattern of the chirality operator in the staggered fermion formalism.Comment: 27 pages, 78 figures, 10 tables, references updated, more explanation adde

MicroNano2008-70266 FABRICATION OF PHASE REFLECTION SAWTOOTH GRATINGS OPTIMIZED BY SCALAR AND VECTOR WAY BY USING DIAMOND CUTTING

ABSTRACT This paper presents optimization of phase reflection sawtooth gratings with a period of 2.0 ㎛ and a depth of 0.2 ㎛ based on the Fourier transformation (FT) and the rigorous coupled wave analysis (RCWA). And its fabrication on oxygen free Cu and electroless Ni-coated surfaces by using diamond cutting in a shaping process whose toolpath is interfered to provide smaller period. The diffraction efficiencies were estimated 100% for FT, 83.0% and 79.0% for TE and TM polarization of the incident light at a depth of 0.2 ㎛ It was found that electroless Ni-coated surface had better performance in terms of machining and optical functionality. From optical testing, the diffraction efficiencies were measured 84.0% and 84.4% for TE and TM polarization, respectively

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.</p

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome

Molecular basis of USP7 inhibition by selective small-molecule inhibitors

Ubiquitination controls the stability of most cellular proteins, and its deregulation contributes to human diseases including cancer. Deubiquitinases remove ubiquitin from proteins, and their inhibition can induce the degradation of selected proteins, potentially including otherwise 'undruggable' targets. For example, the inhibition of ubiquitin-specific protease 7 (USP7) results in the degradation of the oncogenic E3 ligase MDM2, and leads to re-activation of the tumour suppressor p53 in various cancers. Here we report that two compounds, FT671 and FT827, inhibit USP7 with high affinity and specificity in vitro and within human cells. Co-crystal structures reveal that both compounds target a dynamic pocket near the catalytic centre of the auto-inhibited apo form of USP7, which differs from other USP deubiquitinases. Consistent with USP7 target engagement in cells, FT671 destabilizes USP7 substrates including MDM2, increases levels of p53, and results in the transcription of p53 target genes, induction of the tumour suppressor p21, and inhibition of tumour growth in mice