Accurate harmonic vibrational frequencies for diatomic molecules via quantum computing

During the noisy intermediate-scale quantum (NISQ) era, quantum computational approaches refined to overcome the challenge of limited quantum resources are highly valuable. However, the accuracy of the molecular properties predicted by most of the quantum computations nowadays is still far off (not within chemical accuracy) compared to their corresponding experimental data. Here, we propose a promising qubit-efficient quantum computational approach to calculate the harmonic vibrational frequencies of a large set of neutral closed-shell diatomic molecules with results in great agreement with their experimental data. To this end, we construct the accurate Hamiltonian using molecular orbitals, derived from density functional theory to account for the electron correlation and expanded in the Daubechies wavelet basis set to allow an accurate representation in real space grid points, where an optimized compact active space is further selected so that only a reduced small number of qubits is sufficient to yield an accurate result. To justify the approach, we benchmark the performance of the Hamiltonians spanned by the selected molecular orbitals by first transforming the molecular Hamiltonians into qubit Hamiltonians and then using the exact diagonalization method to calculate the results, regarded as the best results achievable by quantum computation. Furthermore, we show that the variational quantum circuit with the chemistry-inspired UCCSD ansatz can achieve the same accuracy as the exact diagonalization method except for systems whose Mayer bond order indices are larger than 2. For those systems, we demonstrate that the heuristic hardware-efficient RealAmplitudes ansatz, even with a shorter circuit depth, can provide a significant improvement over the UCCSD ansatz, verifying that the harmonic vibrational frequencies could be calculated accurately by quantum computation in the NISQ era.Comment: 17 pages, 5 figures plus 11-page supplemental materia

Security weaknesses in two multi-server password based authentication protocols

In 2004 and 2005, Tsaur et al. proposed a smart card based password authentication schemes for multi-server environments, respectively. They claimed that their protocols are safe and can withstand various kinds of attacks. However, after analysis, we found their schemes each have some secure loopholes. In this article, we will show the security flaws in these two protocols

Rainfall variations in central Indo-Pacific over the past 2,700 y

Tropical rainfall variability is closely linked to meridional shifts of the Intertropical Convergence Zone (ITCZ) and zonal movements of the Walker circulation. The characteristics and mechanisms of tropical rainfall variations on centennial to decadal scales are, however, still unclear. Here, we reconstruct a replicated stalagmite-based 2,700-y-long, continuous record of rainfall for the deeply convective northern central Indo-Pacific (NCIP) region. Our record reveals decreasing rainfall in the NCIP over the past 2,700 y, similar to other records from the northern tropics. Notable centennial- to decadal-scale dry climate episodes occurred in both the NCIP and the southern central Indo-Pacific (SCIP) during the 20th century [Current Warm Period (CWP)] and the Medieval Warm Period (MWP), resembling enhanced El Niño-like conditions. Further, we developed a 2,000-y-long ITCZ shift index record that supports an overall southward ITCZ shift in the central Indo-Pacific and indicates southward mean ITCZ positions during the early MWP and the CWP. As a result, the drying trend since the 20th century in the northern tropics is similar to that observed during the past warm period, suggesting that a possible anthropogenic forcing of rainfall remains indistinguishable from natural variability

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features

Ultrasound sonication with microbubbles disrupts blood vessels and enhances tumor treatments of anticancer nanodrug

Ultrasound (US) sonication with microbubbles (MBs) has the potential to disrupt blood vessels and enhance the delivery of drugs into the sonicated tissues. In this study, mouse ear tumors were employed to investigate the therapeutic effects of US, MBs, and pegylated liposomal doxorubicin (PLD) on tumors. Tumors started to receive treatments when they grew up to about 15 mm3 (early stage) with injection of PLD 10 mg/kg, or up to 50 mm3 (medium stage) with PLD 6 (or 4) mg/kg. Experiments included the control, PLD alone, PLD + MBs + US, US alone, and MBs + US groups. The procedure for the PLD + MBs + US group was that PLD was injected first, MB (SonoVue) injection followed, and then US was immediately sonicated on the tumor. The results showed that: (1) US sonication with MBs was always able to produce a further hindrance to tumor growth for both early and medium-stage tumors; (2) for the medium-stage tumors, 6 mg/kg PLD alone was able to inhibit their growth, while it did not work for 4 mg/kg PLD alone; (3) with the application of MBs + US, 4 mg/kg PLD was able to inhibit the growth of medium-stage tumors; (4) for early stage tumors after the first treatment with a high dose of PLD alone (10 mg/kg), the tumor size still increased for several days and then decreased (a biphasic pattern); (5) MBs + US alone was able to hinder the growth of early stage tumors, but unable to hinder that of medium stage tumors. The results of histological examinations and blood perfusion measurements indicated that the application of MBs + US disrupts the tumor blood vessels and enhances the delivery of PLD into tumors to significantly inhibit tumor growth

Genetic analysis of fish iridoviruses isolated in Taiwan during 2001–2009

To investigate the genetic relationships between field strains of iridoviruses gathered from various fish species in Taiwan, viruses that were collected from 2001 to 2009 were analyzed. Open reading frames encoding the viral major capsid protein (MCP) and adenosine triphosphatase (ATPase) were sequenced for phylogenetic analysis. Our results indicated that iridoviruses from Taiwan aquaculture fishes could be classified into two groups: prior to 2005, the viruses were closely related to members of the genus Ranavirus; and after 2005, they were similar to members of the genus Megalocytivirus. Based on the analysis of MCP amino acid sequences, virus isolates were divided into 4 major genotypes that were related to ISKNV, RSIV, FLIV, and GIV, respectively. Pairwise comparisons of MCP genes showed that the ranavirus was an epidemic pathogen for economically important species in the major production regions and cultured marine fish, while the megalocytivirus isolates were sensitive to host range. In addition, the distribution of synonymous and non-synonymous changes in the MCP gene revealed that the iridoviruses were evolving slowly, and most of the variations were synonymous mutations. The Ka/Ks values were lower than one, and hence, the viruses were under negative selection

A multivariate prediction model for microarray cross-hybridization

BACKGROUND: Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization. RESULTS: We propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multivariate models agree that the 'most contiguous base pairs between probe and target sequences,' rather than percent identity, is the best univariate predictor. The predictive power is improved by inclusion of additional nonlinear effects, in particular target GC content, when regression trees or ANNs are used. CONCLUSION: A systematic multivariate approach is provided to assess the importance of multiple sequence features for hybridization and of relationships among these features. This approach can easily be applied to larger datasets. This will allow future developments of generalized hybridization models that will be able to correct for false-positive cross-hybridization signals in expression experiments

Design of microarray probes for virus identification and detection of emerging viruses at the genus level

BACKGROUND: Most virus detection methods are geared towards the detection of specific single viruses or just a few known targets, and lack the capability to uncover the novel viruses that cause emerging viral infections. To address this issue, we developed a computational method that identifies the conserved viral sequences at the genus level for all viral genomes available in GenBank, and established a virus probe library. The virus probes are used not only to identify known viruses but also for discerning the genera of emerging or uncharacterized ones. RESULTS: Using the microarray approach, the identity of the virus in a test sample is determined by the signals of both genus and species-specific probes. The genera of emerging and uncharacterized viruses are determined based on hybridization of the viral sequences to the conserved probes for the existing viral genera. A detection and classification procedure to determine the identity of a virus directly from detection signals results in the rapid identification of the virus. CONCLUSION: We have demonstrated the validity and feasibility of the above strategy with a small number of viral samples. The probe design algorithm can be applied to any publicly available viral sequence database. The strategy of using separate genus and species probe sets enables the use of a straightforward virus identity calculation directly based on the hybridization signals. Our virus identification strategy has great potential in the diagnosis of viral infections. The virus genus and specific probe database and the associated summary tables are available a