Systemic similarity analysis of compatibility drug-induced multiple pathway patterns _in vivo_

A major challenge in post-genomic research is to understand how physiological and pathological phenotypes arise from the networks of expressed genes and to develop powerful tools for translating the information exchanged between gene and the organ system networks. Although different expression modules may contribute independently to different phenotypes, it is difficult to interpret microarray experimental results at the level of single gene associations. The global effects and response pathways of small molecules in cells have been investigated, but the quantitative details of the activation mechanisms of multiple pathways _in vivo_ are not well understood. Similar response networks indicate similar modes of action, and gene networks may appear to be similar despite differences in the behaviour of individual gene groups. Here we establish the method for assessing global effect spectra of the complex signaling forms using Global Similarity Index (GSI) in cosines vector included angle. Our approach provides quantitative multidimensional measures of genes expression profile based on drug-dependent phenotypic alteration _in vivo_. These results make a starting point for identifying relationships between GSI at the molecular level and a step toward phenotypic outcomes at a system level to predict action of unknown compounds and any combination therapy

Shifting from Population-wide to Personalized Cancer Prognosis with Microarrays

The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment

SpacePulse: Combining Parameterized Pulses and Contextual Subspace for More Practical VQE

In this paper, we explore the integration of parameterized quantum pulses with the contextual subspace method. The advent of parameterized quantum pulses marks a transition from traditional quantum gates to a more flexible and efficient approach to quantum computing. Working with pulses allows us to potentially access areas of the Hilbert space that are inaccessible with a CNOT-based circuit decomposition. Compared to solving the complete Hamiltonian via the traditional Variational Quantum Eigensolver (VQE), the computation of the contextual correction generally requires fewer qubits and measurements, thus improving computational efficiency. Plus a Pauli grouping strategy, our framework, SpacePulse, can minimize the quantum resource cost for the VQE and enhance the potential for processing larger molecular structures

Photonic link from single flux quantum circuits to room temperature

Broadband, energy-efficient signal transfer between cryogenic and room-temperature environment has been a major bottleneck for superconducting quantum and classical logic circuits. Photonic links promise to overcome this challenge by offering simultaneous high bandwidth and low thermal load. However, the development of cryogenic electro-optic modulators -- a key component for photonic readout of electrical signals -- has been stifled by the stringent requirements of superconducting circuits. Rapid single flux quantum circuits (RSFQ), for example, operate with a tiny signal amplitude of only a few millivolts (mV), far below the volt-level signal used in conventional circuits. Here, we demonstrate the first direct optical readout of an RSFQ circuit without additional electrical amplification enabled by a novel superconducting electro-optic modulator (SEOM) featuring a record-low half-wave voltage V{\pi} of 42 mV on a 1 m-long SEOM. Leveraging the low ohmic loss of superconductors, we break the fundamental V{\pi}-bandwidth trade-off and demonstrate electro-optic bandwidth up to 17 GHz on a 0.2 m-long SEOM at cryogenic temperatures. Our work presents a viable solution toward high-bandwidth signal transfer between future large-scale superconducting circuits and room-temperature electronics

Targeting the differential addiction to anti-apoptotic BCL-2 family for cancer therapy

AbstractBCL-2 family proteins are central regulators of mitochondrial apoptosis and validated anti-cancer targets. Using small cell lung cancer (SCLC) as a model, we demonstrated the presence of differential addiction of cancer cells to anti-apoptotic BCL-2, BCL-XL or MCL-1, which correlated with the respective protein expression ratio. ABT-263 (navitoclax), a BCL-2/BCL-XL inhibitor, prevented BCL-XL from sequestering activator BH3-only molecules (BH3s) and BAX but not BAK. Consequently, ABT-263 failed to kill BCL-XL-addicted cells with low activator BH3s and BCL-XL overabundance conferred resistance to ABT-263. High-throughput screening identified anthracyclines including doxorubicin and CDK9 inhibitors including dinaciclib that synergized with ABT-263 through downregulation of MCL-1. As doxorubicin and dinaciclib also reduced BCL-XL, the combinations of BCL-2 inhibitor ABT-199 (venetoclax) with doxorubicin or dinaciclib provided effective therapeutic strategies for SCLC. Altogether, our study highlights the need for mechanism-guided targeting of anti-apoptotic BCL-2 proteins to effectively activate the mitochondrial cell death programme to kill cancer cells.</jats:p

PAN: Pulse Ansatz on NISQ Machines

Variational quantum algorithms (VQAs) have demonstrated great potentials in the NISQ era. In the workflow of VQA, the parameters of ansatz are iteratively updated to approximate the desired quantum states. We have seen various efforts to draft better ansatz with less gates. In quantum computers, the gate ansatz will eventually be transformed into control signals such as microwave pulses on transmons. And the control pulses need elaborate calibration to minimize the errors such as over-rotation and under-rotation. In the case of VQAs, this procedure will introduce redundancy, but the variational properties of VQAs can naturally handle problems of over-rotation and under-rotation by updating the amplitude and frequency parameters. Therefore, we propose PAN, a native-pulse ansatz generator framework for VQAs. We generate native-pulse ansatz with trainable parameters for amplitudes and frequencies. In our proposed PAN, we are tuning parametric pulses, which are natively supported on NISQ computers. Considering that parameter-shift rules do not hold for native-pulse ansatz, we need to deploy non-gradient optimizers. To constrain the number of parameters sent to the optimizer, we adopt a progressive way to generate our native-pulse ansatz. Experiments are conducted on both simulators and quantum devices to validate our methods. When adopted on NISQ machines, PAN obtained improved the performance with decreased latency by an average of 86%. PAN is able to achieve 99.336% and 96.482% accuracy for VQE tasks on H2 and HeH+ respectively, even with considerable noises in NISQ machines.Comment: 13 pages, 13 figure