Quantum Non-demolition Detection of Single Microwave Photons in a Circuit

Thorough control of quantum measurement is key to the development of quantum information technologies. Many measurements are destructive, removing more information from the system than they obtain. Quantum non-demolition (QND) measurements allow repeated measurements that give the same eigenvalue. They could be used for several quantum information processing tasks such as error correction, preparation by measurement, and one-way quantum computing. Achieving QND measurements of photons is especially challenging because the detector must be completely transparent to the photons while still acquiring information about them. Recent progress in manipulating microwave photons in superconducting circuits has increased demand for a QND detector which operates in the gigahertz frequency range. Here we demonstrate a QND detection scheme which measures the number of photons inside a high quality-factor microwave cavity on a chip. This scheme maps a photon number onto a qubit state in a single-shot via qubit-photon logic gates. We verify the operation of the device by analyzing the average correlations of repeated measurements, and show that it is 90% QND. It differs from previously reported detectors because its sensitivity is strongly selective to chosen photon number states. This scheme could be used to monitor the state of a photon-based memory in a quantum computer.Comment: 5 pages, 4 figures, includes supplementary materia

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined

Ricin Toxicokinetics and Its Sensitive Detection in Mouse Sera or Feces Using Immuno-PCR

Ricin (also called RCA-II or RCA(60)), one of the most potent toxins and documented bioweapons, is derived from castor beans of Ricinus communis. Several in vitro methods have been designed for ricin detection in complex food matrices in the event of intentional contamination. Recently, a novel Immuno-PCR (IPCR) assay was developed with a limit of detection of 10 fg/ml in a buffer matrix and about 10-1000-fold greater sensitivity than other methods in various food matrices.In order to devise a better diagnostic test for ricin, the IPCR assay was adapted for the detection of ricin in biological samples collected from mice after intoxication. The limit of detection in both mouse sera and feces was as low as 1 pg/ml. Using the mouse intravenous (iv) model for ricin intoxication, a biphasic half-life of ricin, with a rapid t(1/2)α of 4 min and a slower t(1/2)β of 86 min were observed. The molecular biodistribution time for ricin following oral ingestion was estimated using an antibody neutralization assay. Ricin was detected in the blood stream starting at approximately 6-7 h post- oral intoxication. Whole animal histopathological analysis was performed on mice treated orally or systemically with ricin. Severe lesions were observed in the pancreas, spleen and intestinal mesenteric lymph nodes, but no severe pathology in other major organs was observed.The determination of in vivo toxicokinetics and pathological effects of ricin following systemic and oral intoxication provide a better understanding of the etiology of intoxication and will help in the future design of more effective diagnostic and therapeutic methods

Analyses of apoptotic regulators CASP9 and DFFA at 1P36.2, reveal rare allele variants in human neuroblastoma tumours

The genes encoding Caspase-9 and DFF45 have both recently been mapped to chromosome region 1p36.2, that is a region alleged to involve one or several tumour suppressor genes in neuroblastoma tumours. This study presents an update contig of the ‘Smallest Region of Overlap of deletions’ in Scandinavian neuroblastoma tumours and suggests that DFF45 is localized in the region. The genomic organization of the human DFF45 gene, deduced by in-silico comparisons of DNA sequences, is described for the first time in this paper. In the present study 44 primary tumours were screened for mutation by analysis of the genomic sequences of the genes. In two out of the 44 tumours this detected in the DFFA gene one rare allele variant that caused a non-polar to a polar amino acid exchange in a preserved hydrophobic patch of DFF45. One case was hemizygous due to deletion of the more common allele of this polymorphism. Out of 194 normal control alleles only one was found to carry this variant allele, so in respect of it, no healthy control individual out of 97 was homozygous. Moreover, our RT–PCR expression studies showed that DFF45 is preferably expressed in low-stage neuroblastoma tumours and to a lesser degree in high-stage neuroblastomas. We conclude that although coding mutations of Caspase-9 and DFF45 are infrequent in neuroblastoma tumours, our discovery of a rare allele in two neuroblastoma cases should be taken to warrant further studies of the role of DFF45 in neuroblastoma genetics

Normalizing single-cell RNA sequencing data: challenges and opportunities

Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users

Telomerase activity, estrogen receptors (α, β), Bcl-2 expression in human breast cancer and treatment response

BACKGROUND: The mechanism for maintaining telomere integrity is controlled by telomerase, a ribonucleoprotein enzyme that specifically restores telomere sequences, lost during replication by means of an intrinsic RNA component as a template for polymerization. Among the telomerase subunits, hTERT (human telomerase reverse transcriptase) is expressed concomitantly with the activation of telomerase. The role of estrogens and their receptors in the transcriptional regulation of hTERT has been demonstrated. The current study determines the possible association between telomerase activity, the expression of both molecular forms of estrogen receptor (ERα and ERβ) and the protein bcl-2, and their relative associations with clinical parameters. METHODS: Tissue samples from 44 patients with breast cancer were used to assess telomerase activity using the TRAP method and the expression of ERα, ERβ and bcl-2 by means of immunocytochemical techniques. RESULTS: Telomerase activity was detected in 59% of the 44 breast tumors examined. Telomerase activity ranged from 0 to 49.93 units of total product generated (TPG). A correlation was found between telomerase activity and differentiation grade (p = 0.03). The only significant independent marker of response to treatment was clinical stage. We found differences between the frequency of expression of ERα (88%) and ERβ (36%) (p = 0.007); bcl-2 was expressed in 79.5% of invasive breast carcinomas. We also found a significant correlation between low levels of telomerase activity and a lack of ERβ expression (p = 0.03). CONCLUSION: Lower telomerase activity was found among tumors that did not express estrogen receptor beta. This is the first published study demonstrating that the absence of expression of ERβ is associated with low levels of telomerase activity