Fano Effect through Parallel-coupled Double Coulomb Islands

By means of the non-equilibrium Green function and equation of motion method, the electronic transport is theoretically studied through a parallel-coupled double quantum dots(DQD) in the presence of the on-dot Coulomb correlation, with an emphasis put on the quantum interference. It has been found that in the Coulomb blockage regime, the quantum interference between the bonding and antiboding DQD states or that between their Coulomb blockade counterparts may result in the Fano resonance in the conductance spectra, and the Fano peak doublet may be observed under certain non-equilibrium condition. The possibility of manipulating the Fano lineshape is predicted by tuning the dot-lead coupling and magnetic flux threading the ring connecting the dots and leads. Similar to the case without Coulomb interaction, the direction of the asymmetric tail of Fano lineshape can be flipped by the external field. Most importantly, by tuning the magnetic flux, the function of four relevant states can be interchanged, giving rise to the swap effect, which might play a key role as a qubit in the quantum computation.Comment: 7 pages, 5 figure

A Semi-automatic Approach to Identifying and Unifying Ambiguously Encoded Arabic-Based Characters

In this study, we outline a potential problem in normalising texts that are based on a modified version of the Arabic alphabet. One of the main resources available for processing resource-scarce languages is raw text collected from the Internet. Many less-resourced languages, such as Kurdish, Farsi, Urdu, Pashtu, etc., use a modified version of the Arabic writing system. Many characters in harvested data from the Internet may have exactly the same form but encoded with different Unicode values (ambiguous characters). The existence of ambiguous characters in words leads to word duplication, thus it is important to identify and unify ambiguous characters during the normalisation stage. Here, we demonstrate cases related to ambiguous Kurdish and Farsi characters and propose a semi-automatic approach to identifying and unifying them

Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers

In this paper, we investigate a range of strategies for combining multiple machine learning techniques for recognizing Arabic characters, where we are faced with imperfect and dimensionally variable input characters. Experimental results show that combined confidence-based backoff strategies can produce more accurate results than each technique produces by itself and even the ones exhibited by the majority voting combination

Experimental and Simulation Identification of Xanthohumol as an Inhibitor and Substrate of ABCB1

Xanthohumol (XN) is a well-known prenylated flavonoid found in Humulus lupulus L. It is involved in several pharmacological activities, including the sensitization of doxorubicin-resistant breast cancer (MCF-7/ADR) cells to doxorubicin (DOX) through a reduction in cell viability and stemness. In the present study, we revealed another mechanism to further explain the reverse of the drug resistance of XN. In the MCF-7/ADR cell line, we found that XN inhibited the efflux functions of ATP-binding cassette subfamily B member 1 (ABCB1). We also observed that XN was a substrate of ABCB1 and stimulated its ATPase activity. Moreover, our results revealed that XN showed a synergic effect with the ABCB1 substrate colchicine (COL) in the MCF-7/ADR cell line. Further, we showed that XN bound to the central transmembrane domain (TMD) site, overlapping with the DOX binding site. This mechanism was supported by molecular modeling and simulation data, which revealed that XN bound to the ABCB1 transmembrane domain, where doxorubicin also binds, and its binding affinity was stronger than that of doxorubicin, resulting in less protein and ligand position fluctuation. These results support the XN-induced reversal of drug resistance via the inhibition of ABCB1-mediated transport of doxorubicin, stimulating ABCB1 ATPase activity and acting as a substrate of ABCB1

Experimental and Simulation Identification of Xanthohumol as an Inhibitor and Substrate of ABCB1

Xanthohumol (XN) is a well-known prenylated flavonoid found in Humulus lupulus L. It is involved in several pharmacological activities, including the sensitization of doxorubicin-resistant breast cancer (MCF-7/ADR) cells to doxorubicin (DOX) through a reduction in cell viability and stemness. In the present study, we revealed another mechanism to further explain the reverse of the drug resistance of XN. In the MCF-7/ADR cell line, we found that XN inhibited the efflux functions of ATP-binding cassette subfamily B member 1 (ABCB1). We also observed that XN was a substrate of ABCB1 and stimulated its ATPase activity. Moreover, our results revealed that XN showed a synergic effect with the ABCB1 substrate colchicine (COL) in the MCF-7/ADR cell line. Further, we showed that XN bound to the central transmembrane domain (TMD) site, overlapping with the DOX binding site. This mechanism was supported by molecular modeling and simulation data, which revealed that XN bound to the ABCB1 transmembrane domain, where doxorubicin also binds, and its binding affinity was stronger than that of doxorubicin, resulting in less protein and ligand position fluctuation. These results support the XN-induced reversal of drug resistance via the inhibition of ABCB1-mediated transport of doxorubicin, stimulating ABCB1 ATPase activity and acting as a substrate of ABCB1

A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms

We describe a genetic variation map for the chicken genome containing 2.8 million single-nucleotide polymorphisms ( SNPs). This map is based on a comparison of the sequences of three domestic chicken breeds ( a broiler, a layer and a Chinese silkie) with that of their wild ancestor, red jungle fowl. Subsequent experiments indicate that at least 90% of the variant sites are true SNPs, and at least 70% are common SNPs that segregate in many domestic breeds. Mean nucleotide diversity is about five SNPs per kilobase for almost every possible comparison between red jungle fowl and domestic lines, between two different domestic lines, and within domestic lines - in contrast to the notion that domestic animals are highly inbred relative to their wild ancestors. In fact, most of the SNPs originated before domestication, and there is little evidence of selective sweeps for adaptive alleles on length scales greater than 100 kilobases

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense