Chiral metallohelices enantioselectively target hybrid human telomeric G-quadruplex DNA

The design and synthesis of metal complexes that can specifically target DNA secondary structure has attracted considerable attention. Chiral metallosupramolecular complexes (e.g. helicates) in particular display unique DNA-binding behavior, however until recently few examples which are both water-compatible and enantiomerically pure have been reported. Herein we report that one metallohelix enantiomer , available from a diastereoselective synthesis with no need for resolution, can enantioselectively stabilize human telomeric hybrid G-quadruplex and strongly inhibit telomerase activity with IC 50 of 600 nM. In contrast, no such a preference is observed for the mirror image complex . More intriguingly, neither of the two enantiomers binds specifically to human telomeric antiparallel G-quadruplex. To the best of our knowledge, this is the first example of one pair of enantiomers with contrasting selectivity for human telomeric hybrid G-quadruplex. Further studies show that can discriminate human telomeric G-quadruplex from other telomeric G-quadruplexes

Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks

Achieving efficient and robust multi-channel data learning is a challenging task in data science. By exploiting low-rankness in the transformed domain, i.e., transformed low-rankness, tensor Singular Value Decomposition (t-SVD) has achieved extensive success in multi-channel data representation and has recently been extended to function representation such as Neural Networks with t-product layers (t-NNs). However, it still remains unclear how t-SVD theoretically affects the learning behavior of t-NNs. This paper is the first to answer this question by deriving the upper bounds of the generalization error of both standard and adversarially trained t-NNs. It reveals that the t-NNs compressed by exact transformed low-rank parameterization can achieve a sharper adversarial generalization bound. In practice, although t-NNs rarely have exactly transformed low-rank weights, our analysis further shows that by adversarial training with gradient flow (GF), the over-parameterized t-NNs with ReLU activations are trained with implicit regularization towards transformed low-rank parameterization under certain conditions. We also establish adversarial generalization bounds for t-NNs with approximately transformed low-rank weights. Our analysis indicates that the transformed low-rank parameterization can promisingly enhance robust generalization for t-NNs.Comment: 46 pages, accepted to NeurIPS 2023. We have corrected several typos in the first version (arXiv:2303.00196

Mirror-image dependence : targeting enantiomeric G-quadruplex DNA using triplex metallohelices

Natural d‐DNA and l‐DNA are mirror‐image counterparts. However, because of the inherent flexibility and conformation diversity of DNA, it is still not clear how enantiomeric compounds recognize d‐DNA and l‐DNA. Herein, taking G‐quadruplex (G4) DNA as an example that has diverse conformations and distinct biofunctions, the binding of ten pairs of iron triplex metallohelices to d‐ and l‐G4 DNA were evaluated. The Δ‐enantiomer binds to d‐DNA and the Λ‐enantiomer binds to l‐DNA, exhibiting almost the same stabilization effect and binding affinity. The binding affinity of the Δ‐metallohelix with d‐G4 is nearly 70‐fold higher than that of Λ‐metallohelix binding d‐G4. Δ‐Metallohelix binding to d‐G4 follows a two‐step binding process driven by a favorable enthalpy contribution to compensate for the associated unfavorable entropy

Advanced machine learning optimized by the genetic algorithm in ionospheric models using long-term multi-instrument observations

The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%-35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly andWeddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data

<em>Fusarium graminearum</em> Species Complex and Trichothecene Genotype

The fungal phytopathogen in Fusarium species can cause Fusarium head blight of wheat, barley, oats, and other small cereal grain crops worldwide. Most importantly, these fungi can produce different kinds of mycoxins, and they are harmful to humans and animal health. FAO reported that approximately 25% of the world’s grains were contaminated by mycotoxins annually. This chapter will focus on several topics as below: (1) composition of Fusarium graminearum species complex; (2) genotype determination of Fusarium graminearum species complex strains from different hosts and their population structure changes; (3) genetic approaches to genotype determination in type B-trichothecene producing Fusaria fungi; and (4) some newly identified trichothecene mycotoxins, their toxicity, and distribution of the producers

Protection Efficacy of the Extract of Ginkgo biloba

Repeated high sustained positive Gz (+Gz) exposures are known for the harmful pathophysiological impact on the brain of rats, which is reflected as the interruption of normal performance of learning and memory. Interestingly, extract of Ginkgo biloba (EGb) has been reported to have neuroprotective effects and cognition-enhancing effects. In this study, we are interested in evaluating the protective effects of EGb toward the learning and memory abilities. Morris Water Maze Test (MWM) was used to evaluate the cognitive function, and the physiological status of the key components in central cholinergic system was also investigated. Our animal behavioral tests indicated that EGb can release the learning and memory impairment caused by repeated high sustained +Gz. Administration of EGb to rats can diminish some of the harmful physiological effects caused by repeated +Gz exposures. Moreover, EGb administration can increase the biological activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) but reduce the production of malondialdehyde (MDA). Taken together, our study showed that EGb can ameliorate the impairment of learning and memory abilities of rats induced by repeated high sustained +Gz exposure; the underlying mechanisms appeared to be related to the signal regulation on the cholinergic system and antioxidant enzymes system

Preparation of Monolayer MoS\u3csub\u3e2\u3c/sub\u3e Quantum Dots using Temporally Shaped Femtosecond Laser Ablation of Bulk MoS\u3csub\u3e2\u3c/sub\u3e Targets in Water

Zero-dimensional MoS2 quantum dots (QDs) possess distinct physical and chemical properties, which have garnered them considerable attention and facilitates their use in a broad range of applications. In this study, we prepared monolayer MoS2 QDs using temporally shaped femtosecond laser ablation of bulk MoS2 targets in water. The morphology, crystal structures, chemical, and optical properties of the MoS2 QDs were characterized by transmission electron microscopy, X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, UV–vis absorption spectra, and photoluminescence spectra. The analysis results show that highly pure, uniform, and monolayer MoS2 QDs can be successfully prepared. Moreover, by temporally shaping a conventional single pulse into a two-subpulse train, the production rate of MoS2 nanomaterials (including nanosheets, nanoparticles, and QDs) and the ratio of small size MoS2 QDs can be substantially improved. The underlying mechanism is a combination of multilevel photoexfoliation of monolayer MoS2 and water photoionization–enhanced light absorption. The as-prepared MoS2 QDs exhibit excellent electrocatalytic activity for hydrogen evolution reactions because of the abundant active edge sites, high specific surface area, and excellent electrical conductivity. Thus, this study provides a simple and green alternative strategy for the preparation of monolayer QDs of transition metal dichalcogenides or other layered materials

Lightweight Knowledge Representations for Automating Data Analysis

The principal goal of data science is to derive meaningful information from data. To do this, data scientists develop a space of analytic possibilities and from it reach their information goals by using their knowledge of the domain, the available data, the operations that can be performed on those data, the algorithms/models that are fed the data, and how all of these facets interweave. In this work, we take the first steps towards automating a key aspect of the data science pipeline: data analysis. We present an extensible taxonomy of data analytic operations that scopes across domains and data, as well as a method for codifying domain-specific knowledge that links this analytics taxonomy to actual data. We validate the functionality of our analytics taxonomy by implementing a system that leverages it, alongside domain labelings for 8 distinct domains, to automatically generate a space of answerable questions and associated analytic plans. In this way, we produce information spaces over data that enable complex analyses and search over this data and pave the way for fully automated data analysis

A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network

There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde's observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde's data in the polar regions. The differences between ANN-based maps and references show that the d

Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde

The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal-vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space-borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY-3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three-dimensional electron density model based on an artificial neural network, namely ANN-TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root-mean-square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN-TDD is 30%–60% higher than the IRI-2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN-TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI-2016 with the STORM option activated. Additionally, the ANN-TDD successfully reproduces the large-scale horizontal-v