Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials

The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material space dwarfs current synthesis throughput. While the computational crystal structure prediction is expected to mitigate this frustration, the NP-hardness and steep costs of density functional theory (DFT) calculations prohibit material exploration at scale. Herein, we introduce SPINNER, a highly efficient and reliable structure-prediction framework based on exhaustive random searches and evolutionary algorithms, which is completely free from empiricism. Empowered by accurate neural network potentials, the program can navigate the configuration space faster than DFT by more than 10$^{2}$-fold. In blind tests on 60 ternary compositions diversely selected from the experimental database, SPINNER successfully identifies experimental (or theoretically more stable) phases for ~80% of materials within 5000 generations, entailing up to half a million structure evaluations for each composition. When benchmarked against previous data mining or DFT-based evolutionary predictions, SPINNER identifies more stable phases in the majority of cases. By developing a reliable and fast structure-prediction framework, this work opens the door to large-scale, unbounded computational exploration of undiscovered inorganic crystals.Comment: 3 figure

Stimulated penetrating keratoplasty using real-time virtual intraoperative surgical optical coherence tomography

An intraoperative surgical microscope is an essential tool in a neuro-or ophthalmological surgical environment. Yet, it has an inherent limitation to classify subsurface information because it only provides the surface images. To compensate for and assist in this problem, combining the surgical microscope with optical coherence tomography (OCT) has been adapted. We developed a real-time virtual intraoperative surgical OCT (VISOCT) system by adapting a spectral-domain OCT scanner with a commercial surgical microscope. Thanks to our custommade beam splitting and image display subsystems, the OCT images and microscopic images are simultaneously visualized through an ocular lens or the eyepiece of the microscope. This improvement helps surgeons to focus on the operation without distraction to view OCT images on another separate display. Moreover, displaying the OCT live images on the eyepiece helps surgeon's depth perception during the surgeries. Finally, we successfully processed stimulated penetrating keratoplasty in live rabbits. We believe that these technical achievements are crucial to enhance the usability of the VISOCT system in a real surgical operating condition.open0

Comprehensive genomic analyses associate UGT8 variants with musical ability in a Mongolian population

Background: Musical abilities such as recognising music and singing performance serve as means for communication and are instruments in sexual selection. Specific regions of the brain have been found to be activated by musical stimuli, but these have rarely been extended to the discovery of genes and molecules associated with musical ability. Methods: A total of 1008 individuals from 73 families were enrolled and a pitch-production accuracy test was applied to determine musical ability. To identify genetic loci and variants that contribute to musical ability, we conducted family-based linkage and association analyses, and incorporated the results with data from exome sequencing and array comparative genomic hybridisation analyses. Results: We found significant evidence of linkage at 4q23 with the nearest marker D4S2986 (LOD=3.1), whose supporting interval overlaps a previous study in Finnish families, and identified an intergenic single nucleotide polymorphism (SNP) $(rs1251078, p=8.4×10^{−17})$ near UGT8, a gene highly expressed in the central nervous system and known to act in brain organisation. In addition, a non-synonymous SNP in UGT8 was revealed to be highly associated with musical ability $(rs4148254, p=8.0×10^{−17})$, and a 6.2 kb copy number loss near UGT8 showed a plausible association with musical ability $(p=2.9×10^{−6})$. Conclusions: This study provides new insight into the genetics of musical ability, exemplifying a methodology to assign functional significance to synonymous and non-coding alleles by integrating multiple experimental methods

A Symmetric Key Based Deduplicatable Proof of Storage for Encrypted Data in Cloud Storage Environments

Over the recent years, cloud storage services have become increasingly popular, where users can outsource data and access the outsourced data anywhere, anytime. Accordingly, the data in the cloud is growing explosively. Among the outsourced data, most of them are duplicated. Cloud storage service providers can save huge amounts of resources via client-side deduplication. On the other hand, for safe outsourcing, clients who use the cloud storage service desire data integrity and confidentiality of the outsourced data. However, ensuring confidentiality and integrity in the cloud storage environment can be difficult. Recently, in order to achieve integrity with deduplication, the notion of deduplicatable proof of storage has emerged, and various schemes have been proposed. However, previous schemes are still inefficient and insecure. In this paper, we propose a symmetric key based deduplicatable proof of storage scheme, which ensures confidentiality with dictionary attack resilience and supports integrity auditing based on symmetric key cryptography. In our proposal, we introduce a bit-level challenge in a deduplicatable proof of storage protocol to minimize data access. In addition, we prove the security of our proposal in the random oracle model with information theory. Implementation results show that our scheme has the best performance

Efficient synthesis of frutinone A and its derivatives through palladium-catalyzed C-H activation/carbonylation

Frutinone A, a biologically active ingredient of an antimicrobial herbal extract, demonstrates potent inhibitory activity towards the CYP1A2 enzyme. A three-step total synthesis of frutinone A with an overall yield of 44 is presented. The construction of the chromone-annelated coumarin core was achieved through palladium-catalyzed C-H carbonylation of 2-phenolchromones. The straightforward synthetic route allowed facile substitutions around the frutinone A core and thus rapid exploration of the structure-activity relationship (SAR) profile of the derivatives. The inhibitory activity of the synthesized frutinone A derivatives were determined for CYP1A2, and ten compounds exhibited one-to-two digit nanomolar inhibitory activity towards the CYP1A2 enzyme. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim114151sciescopu

Efficient Synthesis of Frutinone A and Its Derivatives through Palladium-Catalyzed C-H Activation/Carbonylation

FrutinoneA, a biologically active ingredient of an antimicrobial herbal extract, demonstrates potent inhibitory activity towards the CYP1A2 enzyme. A three-step total synthesis of frutinoneA with an overall yield of 44% is presented. The construction of the chromone-annelated coumarin core was achieved through palladium-catalyzed CH carbonylation of 2-phenolchromones. The straightforward synthetic route allowed facile substitutions around the frutinoneA core and thus rapid exploration of the structure-activity relationship (SAR) profile of the derivatives. The inhibitory activity of the synthesized frutinoneA derivatives were determined for CYP1A2, and ten compounds exhibited one-to-two digit nanomolar inhibitory activity towards the CYP1A2 enzyme

Neural Cryptography Based on Generalized Tree Parity Machine for Real-Life Systems

Traditional public key exchange protocols are based on algebraic number theory. In another perspective, neural cryptography, which is based on neural networks, has been emerging. It has been reported that two parties can exchange secret key pairs with the synchronization phenomenon in neural networks. Although there are various models of neural cryptography, called Tree Parity Machine (TPM), many of them are not suitable for practical use, considering efficiency and security. In this paper, we propose a Vector-Valued Tree Parity Machine (VVTPM), which is a generalized architecture of TPM models and can be more efficient and secure for real-life systems. In terms of efficiency and security, we show that the synchronization time of the VVTPM has the same order as the basic TPM model, and it can be more secure than previous results with the same synaptic depth