Pseudogap in BaPbxBi1‒xO3(x = 0.7, 0.75 and 1.0).

In this work, we have investigated the crystal and electronic structure of the orthorhombic phase of BaPbxBi1-xO3(BPBO) forx = 0.7 (BPBO70), 0.75 (BPBO75) and 1.0 (BPO), using temperature dependent x-ray diffraction measurements, photoemission spectroscopy, and electronic structure calculations. Our results show the importance of particle size and strain in governing superconductivity. Interestingly, the temperature evolution of the structural parameters in the case of BPBO70 is similar to that of BPBO75 but the magnitude of the change is diminished. The BPBO75 and BPO compounds exhibit metallic nature, which is corroborated by the core level studies. The electronic structure calculations in conjunction with the core level studies suggest that oxygen vacancies play an important role for metallicity observed in the end compound. The exponent to the spectral line shape close to the Fermi level suggests the origin of pseudogap to be due to other contributions in addition to disorder in the case of BPBO70 and BPO. The core level studies also show that as one goes fromx = 0.70 to 1.0, there occurs chemical potential shift towards the valence band suggesting hole doping. Our results open the venue to further study these compounds as a function of particle size, nature of carriers for its transport behaviour, electronic structure belowTC, composition at the grain boundaries and microscopic origin of pseudogap in the non-superconducting phase. We believe that our results call for a revision of the temperature-doping phase diagram of BPBO to include the pseudogap phase.

Evidence of lattice strain as a precursor to superconductivity in BaPb0.75Bi0.25O3.

In this work, we have investigated the precursor effects to superconductivity in BaPb0.75Bi0.25O3using temperature dependent resistivity, x-ray diffraction technique and photoemission spectroscopy. The present compound exhibits superconductivity around 11 K (TC). The synthesis procedure adopted is much simpler as compared to the procedure available in the literature. In the temperature range (10 K-25 K) i.e. aboveTC, our results show an increase in both the orthorhombic and tetragonal strain. The well screened features observed in Bi and Pb 4f7/2core levels are indicative of the metallic nature of the sample. The compound exhibits finite intensity at the Fermi level at 300 K and this intensity decreases with decrease in temperature and develops into a pseudogap; the energy dependence of the spectral density of states suggests disordered metallic state. Furthermore, our band structure calculations reveal that the structural transition upon Pb doping results in the closing of the band gap at the Fermi level.

Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC.

BACKGROUND: Early diagnosis is crucial for effective medical management of cancer patients. Tissue biopsy has been widely used for cancer diagnosis, but its invasive nature limits its application, especially when repeated biopsies are needed. Over the past few years, genomic explorations have led to the discovery of various blood-based biomarkers. Tumor Educated Platelets (TEPs) have, of late, generated considerable interest due to their ability to infer tumor existence and subtype accurately. So far, a majority of the studies involving TEPs have offered marker-panels consisting of several hundreds of genes. Profiling large numbers of genes incur a significant cost, impeding its diagnostic adoption. As such, it is important to construct minimalistic molecular signatures comprising a small number of genes. RESULTS: To address the aforesaid challenges, we analyzed publicly available TEP expression profiles and identified a panel of 11 platelet-genes that reliably discriminates between cancer and healthy samples. To validate its efficacy, we chose non-small cell lung cancer (NSCLC), the most prevalent type of lung malignancy. When applied to platelet-gene expression data from a published study, our machine learning model could accurately discriminate between non-metastatic NSCLC cases and healthy samples. We further experimentally validated the panel on an in-house cohort of metastatic NSCLC patients and healthy controls via real-time quantitative Polymerase Chain Reaction (RT-qPCR) (AUC = 0.97). Model performance was boosted significantly after artificial data-augmentation using the EigenSample method (AUC = 0.99). Lastly, we demonstrated the cancer-specificity of the proposed gene-panel by benchmarking it on platelet transcriptomes from patients with Myocardial Infarction (MI). CONCLUSION: We demonstrated an end-to-end bioinformatic plus experimental workflow for identifying a minimal set of TEP associated marker-genes that are predictive of the existence of cancers. We also discussed a strategy for boosting the predictive model performance by artificial augmentation of gene expression data.

Correction to: Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC.

An amendment to this paper has been published and can be accessed via the original article.

Neurodynamical classifiers with low model complexity.

The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an upper bound on the Vapnik-Chervonenkis (VC) dimension. The VC dimension measures the capacity or model complexity of a learning machine. Vapnik's risk formula indicates that models with smaller VC dimension are expected to show improved generalization. On many benchmark datasets, the MCM generalizes better than SVMs and uses far fewer support vectors than the number used by SVMs. In this paper, we describe a neural network that converges to the MCM solution. We employ the MCM neurodynamical system as the final layer of a neural network architecture. Our approach also optimizes the weights of all layers in order to minimize the objective, which is a combination of a bound on the VC dimension and the classification error. We illustrate the use of this model for robust binary and multi-class classification. Numerical experiments on benchmark datasets from the UCI repository show that the proposed approach is scalable and accurate, and learns models with improved accuracies and fewer support vectors.