Deep quantum neural networks equipped with backpropagation on a superconducting processor

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report the first experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results explicitly showcase the advantages of deep quantum neural networks, including quantum analogue of the backpropagation algorithm and less stringent coherence-time requirement for their constituting physical qubits, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices.Comment: 7 pages (main text) + 11 pages (Supplementary Information), 10 figure

Tubeimoside 1 Acts as a Chemotherapeutic Synergist via Stimulating Macropinocytosis

Macropinocytosis is a highly conserved endocytic process which characterizes the engulfment of extracellular fluid and its contents into cells via large, heterogeneous vacuoles known as macropinosomes. Tubeimoside-1 (TBM1) is a low toxic triterpenoid saponin extracted from a traditional Chinese herb Bolbostemma paniculatum (Maxim.). TBM1 stimulates a quick accumulation of numerous phase-lucent cytoplasmic vacuoles in multiple colorectal cancer (CRC) cell lines. These vacuoles can be termed as macropinosomes that efficiently engulf lucifer yellow. These vesicles are not overlaps with endocytic organelle tracers, such as ERTracker, LysoTracker and mitoTracker. These vacuoles induced by TBM1 partially incorporate into lysosomes. Transmission electron microscope indicates membrane ruffling to form lamellipodia. Protrusions collapse onto and then fuse back with the plasma membrane to generate these large endocytic vacuoles. Notably, TBM1 efficiently trafficks dextrans into heterotopic xenografts in vivo, thus provide consolidated evidence that the vacuolization can be mainly defined as macropinocytosis. TBM1 downregulates cell viability and increases PI-positive, but not highlighted Hoechst 33342-positive cells. TBM1 induced cell death can be ascribed as methuosis by hyperstimulation of macropinocytosis which can be compromised by amiloride derivative 5-(Nethyl-N-isopropyl). Light chain 3 II is recruited to these vesicles to stimulate macropinocytosis. The cell death and vacuoles can be significantly neutralized by chloroquine, but can not be the inhibited by 3-methyladenine. TBM1 can coordinate with 5-FU to exert toxicity reducing and efficacy enhancing effects in vivo by increasing the uptake of the latter without hepatic injury. In conclusion, TBM1 effectively induces in vitro and in vivo macropinocytosis which can traffick small molecules into CRC cells. It is an attractive drug transporter and can be harnessed as a chemotherapeutic synergist with translational potential

MiR-218 Inhibits Invasion and Metastasis of Gastric Cancer by Targeting the Robo1 Receptor

MicroRNAs play key roles in tumor metastasis. Here, we describe the regulation and function of miR-218 in gastric cancer (GC) metastasis. miR-218 expression is decreased along with the expression of one of its host genes, Slit3 in metastatic GC. However, Robo1, one of several Slit receptors, is negatively regulated by miR-218, thus establishing a negative feedback loop. Decreased miR-218 levels eliminate Robo1 repression, which activates the Slit-Robo1 pathway through the interaction between Robo1 and Slit2, thus triggering tumor metastasis. The restoration of miR-218 suppresses Robo1 expression and inhibits tumor cell invasion and metastasis in vitro and in vivo. Taken together, our results describe a Slit-miR-218-Robo1 regulatory circuit whose disruption may contribute to GC metastasis. Targeting miR-218 may provide a strategy for blocking tumor metastasis

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

On signed distance-kk-domination in graphs

summary:The signed distance-$k$-domination number of a graph is a certain variant of the signed domination number. If $v$ is a vertex of a graph $G$, the open $k$-neighborhood of $v$, denoted by $N_k(v)$, is the set $N_k(v)=\lbrace u\mid u\ne v$ and $d(u,v)\le k\rbrace$. $N_k[v]=N_k(v)\cup \lbrace v\rbrace$ is the closed $k$-neighborhood of $v$. A function $f\: V\rightarrow \lbrace -1,1\rbrace$ is a signed distance-$k$-dominating function of $G$, if for every vertex $v\in V$, $f(N_k[v])=\sum _{u\in N_k[v]}f(u)\ge 1$. The signed distance-$k$-domination number, denoted by $\gamma _{k,s}(G)$, is the minimum weight of a signed distance-$k$-dominating function on $G$. The values of $\gamma _{2,s}(G)$ are found for graphs with small diameter, paths, circuits. At the end it is proved that $\gamma _{2,s}(T)$ is not bounded from below in general for any tree $T$