Existence of Universal Entangler

A gate is called entangler if it transforms some (pure) product states to entangled states. A universal entangler is a gate which transforms all product states to entangled states. In practice, a universal entangler is a very powerful device for generating entanglements, and thus provides important physical resources for accomplishing many tasks in quantum computing and quantum information. This Letter demonstrates that a universal entangler always exists except for a degenerated case. Nevertheless, the problem how to find a universal entangler remains open.Comment: 4 page

2-Chloro-N-{5-[(4R,5R,10S)-dehydro­abiet-4-yl]-1,3,4-thia­diazol-2-yl}benzamide

There are two independent mol­ecules in the asymmetric unit of the title compound, C28H32ClN3OS (systematic name: 2-chloro-N-{5-[(1R,4aS,10aR)-7-isopropyl-1,4a-dimethyl-1,2,3,4,4a,9,10,10a-octa­hydro­phenanthren-1-yl]-1,3,4-thia­diazol-2-yl}benzamide). In each mol­ecule, the cyclo­hexyl ring attached to the thia­diazole fragment adopts a classic chair conformation with two of its two methyl groups in the axial positions. In the crystal, pairs of inter­molecular N—H⋯N hydrogen bonds link the mol­ecules into centrosymmetric dimers, which are further linked via C—H⋯π inter­actions

No-go Theorem for One-way Quantum Computing on Naturally Occurring Two-level Systems

One-way quantum computing achieves the full power of quantum computation by performing single particle measurements on some many-body entangled state, known as the resource state. As single particle measurements are relatively easy to implement, the preparation of the resource state becomes a crucial task. An appealing approach is simply to cool a strongly correlated quantum many-body system to its ground state. In addition to requiring the ground state of the system to be universal for one-way quantum computing, we also want the Hamiltonian to have non-degenerate ground state protected by a fixed energy gap, to involve only two-body interactions, and to be frustration-free so that measurements in the course of the computation leave the remaining particles in the ground space. Recently, significant efforts have been made to the search of resource states that appear naturally as ground states in spin lattice systems. The approach is proved to be successful in spin-5/2 and spin-3/2 systems. Yet, it remains an open question whether there could be such a natural resource state in a spin-1/2, i.e., qubit system. Here, we give a negative answer to this question by proving that it is impossible for a genuinely entangled qubit states to be a non-degenerate ground state of any two-body frustration-free Hamiltonian. What is more, we prove that every spin-1/2 frustration-free Hamiltonian with two-body interaction always has a ground state that is a product of single- or two-qubit states, a stronger result that is interesting independent of the context of one-way quantum computing.Comment: 5 pages, 1 figur

Comment on some results of Erdahl and the convex structure of reduced density matrices

In J. Math. Phys. 13, 1608-1621 (1972), Erdahl considered the convex structure of the set of $N$-representable 2-body reduced density matrices in the case of fermions. Some of these results have a straightforward extension to the $m$-body setting and to the more general quantum marginal problem. We describe these extensions, but can not resolve a problem in the proof of Erdahl's claim that every extreme point is exposed in finite dimensions. Nevertheless, we can show that when $2m \geq N$ every extreme point of the set of $N$-representable $m$-body reduced density matrices has a unique pre-image in both the symmetric and anti-symmetric setting. Moreover, this extends to the quantum marginal setting for a pair of complementary $m$-body and $(N-m)$-body reduced density matrices.Comment: 19 pages, 0 figures. Comments are welcom

ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis

Background: Drug repositioning is considered a promising drug development strategy with the goal of discovering new uses for existing drugs. Compared with the experimental screening for drug discovery, computational drug repositioning offers lower cost and higher efficiency and, hence, has become a hot issue in bioinformatics. However, there are sparse samples, multi-source information, and even some noises, which makes it difficult to accurately identify potential drug-associated indications.Methods: In this article, we propose a new scheme with improved tensor robust principal component analysis (ITRPCA) in multi-source data to predict promising drug–disease associations. First, we use a weighted k-nearest neighbor (WKNN) approach to increase the overall density of the drug–disease association matrix that will assist in prediction. Second, a drug tensor with five frontal slices and a disease tensor with two frontal slices are constructed using multi-similarity matrices and an updated association matrix. The two target tensors naturally integrate multiple sources of data from the drug-side aspect and the disease-side aspect, respectively. Third, ITRPCA is employed to isolate the low-rank tensor and noise information in the tensor. In this step, an additional range constraint is incorporated to ensure that all the predicted entry values of a low-rank tensor are within the specific interval. Finally, we focus on identifying promising drug indications by analyzing drug–disease association pairs derived from the low-rank drug and low-rank disease tensors.Results: We evaluate the effectiveness of the ITRPCA method by comparing it with five prominent existing drug repositioning methods. This evaluation is carried out using 10-fold cross-validation and independent testing experiments. Our numerical results show that ITRPCA not only yields higher prediction accuracy but also exhibits remarkable computational efficiency. Furthermore, case studies demonstrate the practical effectiveness of our method

Characterization of Human Coronavirus Etiology in Chinese Adults with Acute Upper Respiratory Tract Infection by Real-Time RT-PCR Assays

BACKGROUND: In addition to SARS associated coronaviruses, 4 non-SARS related human coronaviruses (HCoVs) are recognized as common respiratory pathogens. The etiology and clinical impact of HCoVs in Chinese adults with acute upper respiratory tract infection (URTI) needs to be characterized systematically by molecular detection with excellent sensitivity. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we detected 4 non-SARS related HCoV species by real-time RT-PCR in 981 nasopharyngeal swabs collected from March 2009 to February 2011. All specimens were also tested for the presence of other common respiratory viruses and newly identified viruses, human metapneumovirus (hMPV) and human bocavirus (HBoV). 157 of the 981 (16.0%) nasopharyngeal swabs were positive for HCoVs. The species detected were 229E (96 cases, 9.8%), OC43 (42 cases, 4.3%), HKU1 (16 cases, 1.6%) and NL63 (11 cases, 1.1%). HCoV-229E was circulated in 21 of the 24 months of surveillance. The detection rates for both OC43 and NL63 were showed significantly year-to-year variation between 2009/10 and 2010/11, respectively (P<0.001 and P = 0.003), and there was a higher detection frequency of HKU1 in patients aged over 60 years (P = 0.03). 48 of 157(30.57%) HCoV positive patients were co-infected. Undifferentiated human rhinoviruses and influenza (Flu) A were the most common viruses detected (more than 35%) in HCoV co-infections. Respiratory syncytial virus (RSV), human parainfluenza virus (PIV) and HBoV were detected in very low rate (less than 1%) among adult patients with URTI. CONCLUSIONS/SIGNIFICANCE: All 4 non-SARS-associated HCoVs were more frequently detected by real-time RT-PCR assay in adults with URTI in Beijing and HCoV-229E led to the most prevalent infection. Our study also suggested that all non-SARS-associated HCoVs contribute significantly to URTI in adult patients in China

Winner\u27s Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner’s-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner’s curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25–50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner’s curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10−5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure

Generation of specific inhibitors of SUMO-1– and SUMO-2/3–mediated protein-protein interactions using Affimer (Adhiron) technology

Because protein-protein interactions underpin most biological processes, developing tools that target them to understand their function or to inform the development of therapeutics is an important task. SUMOylation is the posttranslational covalent attachment of proteins in the SUMO family (SUMO-1, SUMO-2, or SUMO-3), and it regulates numerous cellular pathways. SUMOylated proteins are recognized by proteins with SUMO-interaction motifs (SIMs) that facilitate noncovalent interactions with SUMO. We describe the use of the Affimer system of peptide display for the rapid isolation of synthetic binding proteins that inhibit SUMO-dependent protein-protein interactions mediated by SIMs both in vitro and in cells. Crucially, these synthetic proteins did not prevent SUMO conjugation either in vitro or in cell-based systems, enabling the specific analysis of SUMO-mediated protein-protein interactions. Furthermore, through structural analysis and molecular modeling, we explored the molecular mechanisms that may underlie their specificity in interfering with either SUMO-1–mediated interactions or interactions mediated by either SUMO-2 or SUMO-3. Not only will these reagents enable investigation of the biological roles of SUMOylation, but the Affimer technology used to generate these synthetic binding proteins could also be exploited to design or validate reagents or therapeutics that target other protein-protein interactions