Topological phases and edge modes of an uneven ladder

We investigate the topological properties of a two-chain quantum ladder with uneven legs, i.e. the two chains differ in their periods by a factor of two. Such an uneven ladder presents rich band structures classified by the closure of either direct or indirect bandgaps. It also provides opportunities to explore fundamental concepts concerning band topology and edge modes, including the difference of intracellular and intercellular Zak phases, and the role of the inversion symmetry (IS). We calculate the Zak phases of the two kinds and find excellent agreement with the dipole moment and extra charge accumulation, respectively. We also find that configurations with IS feature a pair of degenerate two-side edge modes emerging as the closure of the direct bandgap, while configurations without IS feature one-side edge modes emerging as not only the closure of both direct and indirect bandgap but also within the band continuum. Furthermore, by projecting to the two sublattices, we find that the effective Bloch Hamiltonian corresponds to that of a generalized Su-Schrieffer-Heeger model or Rice-Mele model whose hopping amplitudes depend on the quasimomentum. In this way, the topological phases can be efficiently extracted through winding numbers. We propose that uneven ladders can be realized by spin-dependent optical lattices and their rich topological characteristics can be examined by near future experiments.Comment: 17 pages with 15 figure

Electroacupuncture of 2 Hz Has a Rewarding Effect: Evidence from a Conditioned Place Preference Study in Rats

Electroacupuncture (EA) has been used to suppress heroin craving in addicts and the conditioned place preference (CPP) for morphine in the rat. The question remained whether EA by itself will produce some rewarding effect. This was investigated using the CPP procedure in the present study. The results indicated that rats showed a significant preference to the 2 Hz EA-paired compartment. This rewarding effect of EA was prevented by pre-treatment with the opioid receptor antagonist naloxone [2 mg kg−1, intraperitoneally (i.p.)], CB1 cannabinoid antagonist AM251 (3 μg per rat, intracerebroventricularly) or D1 dopamine receptor antagonist SCH23390 (0.1 mg kg−1, i.p.), respectively. TempspacetempspaceIt is concluded that 2 Hz EA is capable of inducing CPP in the rat via the activation of the endogenous opioid-, cannabinoid- and dopamine-systems

Video-assisted thoracic bronchial sleeve lobectomy with bronchoplasty for treatment of lung cancer confined to a single lung lobe: a case series of Chinese patients

BACKGROUND: The outcomes of video-assisted thoracic bronchial sleeve lobectomy (VABSL), a minimally invasive video-assisted thoracoscopic (VATS) lobectomy, are mostly unknown in Chinese patients. OBJECTIVES: To investigate operative and postoperative outcomes of VABSL in a cases series of Chinese patients with lung cancer. METHODS: Retrospective study of 9 patients (male:female 8:1; mean age 59.4 ± 17.6 years, ranging 21–79 years) diagnosed with lung cancer of a single lobe, treated with VABSL between March 2009 and November 2011, and followed up for at least 2 months (mean follow-up: 14.17 ± 12.91 months). Operative outcomes (tumor size, operation time, estimated blood loss and blood transfusion), postoperative outcomes (intensive care unit [ICU] stay, hospitalization length and pathological tumor stage), death, tumor recurrence and safety were assessed. RESULTS: Patients were diagnosed with carcinoid cancer (11.1%), squamous carcinoma (66.7%) or small cell carcinoma (22.2%), affecting the right (77.8%) or left (22.2%) lung lobes in the upper (55.6%), middle (11.1%) or lower (33.3%) regions. TNM stages were T2 (88.9%) or T3 (11.1%); N0 (66.7%), N1 (11.1%) or N2 (22.2%); and M0 (100%). No patient required conversion to thoracotomy. Mean tumor size, operation time and blood loss were 2.50 ± 0.75 cm, 203 ± 20 min and 390 ± 206 ml, respectively. Patients were treated in the ICU for 18.7 ± 0.7 hours, and overall hospitalization duration was 20.8 ± 2.0 days. No deaths, recurrences or severe complications were reported. CONCLUSIONS: VABSL surgery is safe and effective for treatment of lung cancer by experienced physicians, warranting wider implementation of VABSL and VATS training in China

GeneAlign: a coding exon prediction tool based on phylogenetical comparisons

GeneAlign is a coding exon prediction tool for predicting protein coding genes by measuring the homologies between a sequence of a genome and related sequences, which have been annotated, of other genomes. Identifying protein coding genes is one of most important tasks in newly sequenced genomes. With increasing numbers of gene annotations verified by experiments, it is feasible to identify genes in the newly sequenced genomes by comparing to annotated genes of phylogenetically close organisms. GeneAlign applies CORAL, a heuristic linear time alignment tool, to determine if regions flanked by the candidate signals (initiation codon-GT, AG-GT and AG-STOP codon) are similar to annotated coding exons. Employing the conservation of gene structures and sequence homologies between protein coding regions increases the prediction accuracy. GeneAlign was tested on Projector dataset of 491 human–mouse homologous sequence pairs. At the gene level, both the average sensitivity and the average specificity of GeneAlign are 81%, and they are larger than 96% at the exon level. The rates of missing exons and wrong exons are smaller than 1%. GeneAlign is a free tool available at

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in one shot. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads to enable the generation of realistic synthetic load profiles in large quantity for meeting the emerging need in distribution system planning. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, it generates a group of load profiles bearing realistic spatial-temporal correlations in one shot. Second, two complementary metrics for evaluating realisticness of generated load profiles are developed: statistics metrics based on domain knowledge and a deep-learning classifier for comparing high-level features. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms state-of-the-art approaches in realisticness, computational efficiency, and robustness. With little finetuning, the MultiLoad-GAN approach can be readily extended to generate a group of load or PV profiles for a feeder, a substation, or a service area.Comment: Submitted to IEEE Transactions on Smart Gri