Through-substrate terahertz time-domain reflection spectroscopy for environmental graphene conductivity mapping

We demonstrate how terahertz time-domain spectroscopy (THz-TDS) operating in reflection geometry can be used for quantitative conductivity mapping of large area chemical vapor deposited graphene films through silicon support. We validate the technique against measurements performed using the established transmission based THz-TDS. Our through-substrate approach allows unhindered access to the graphene top surface and thus, as we discuss, opens up pathways to perform in situ and in-operando THz-TDS using environmental cells

Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls

Clinical adoption of human genome sequencing requires methods that output genotypes with known accuracy at millions or billions of positions across a genome. Because of substantial discordance among calls made by existing sequencing methods and algorithms, there is a need for a highly accurate set of genotypes across a genome that can be used as a benchmark. Here we present methods to make high-confidence, single-nucleotide polymorphism (SNP), indel and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias toward any method by integrating and arbitrating between 14 data sets from five sequencing technologies, seven read mappers and three variant callers. We identify regions for which no confident genotype call could be made, and classify them into different categories based on reasons for uncertainty. Our genotype calls are publicly available on the Genome Comparison and Analytic Testing website to enable real-time benchmarking of any method

Double quantum dot turnstile as an electron spin entangler

We study the conditions for a double quantum dot system to work as a reliable electron spin entangler, and the efficiency of a beam splitter as a detector for the resulting entangled electron pairs. In particular, we focus on the relative strengths of the tunneling matrix elements, the applied bias and gate voltage, the necessity of time-dependent input/output barriers, and the consequence of considering wavepacket states for the electrons as they leave the double dot to enter the beam splitter. We show that a double quantum dot turnstile is, in principle, an efficient electron spin entangler or entanglement filter because of the exchange coupling between the dots and the tunable input/output potential barriers, provided certain conditions are satisfied in the experimental set-up.Comment: published version; minor error correcte

Secondary zoonotic dog-to-human transmission of SARS-CoV-2 suggested by timeline but refuted by viral genome sequencing

Purpose: The risk of secondary zoonotic transmission of SARS-CoV-2 from pet animals remains unclear. Here, we report on a 44 year old Caucasian male presenting to our clinic with COVID-19 pneumonia, who reported that his dog displayed respiratory signs shortly prior to his infection. The dog tested real-time-PCR (RT-PCR) positive for SARS-CoV-2 RNA and the timeline of events suggested a transmission from the dog to the patient. Methods: RT-PCR and serological assays were used to confirm SARS-CoV-2 infection in the nasopharyngeal tract in the dog and the patient. We performed SARS-CoV-2-targeted amplicon-based next generation sequencing of respiratory samples from the dog and patient for sequence comparisons. Results: SARS-CoV-2 infection of the dog was confirmed by three independent PCR-positive pharyngeal swabs and subsequent seroconversion. Sequence analysis identified two separate SARS-CoV-2 lineages in the canine and the patient’s respiratory samples. The timeline strongly suggested dog-to-human transmission, yet due to the genetic distance of the canine and the patient’s samples paired-transmission was highly unlikely. Conclusion: The results of this case support current knowledge about the low risk of secondary zoonotic dog-to-human transmissions of SARS-CoV-2 and emphasizes the strength of genomic sequencing in deciphering viral transmission chains

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

Context: Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective: To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design: Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting: Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients: Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions: Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures: Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results: Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions: The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient

Parasitic Energy Loss in the LEP Superconducting Cavities

The energy loss of bunches in the LEP superconducting (SC) cavities has been determined by measuring the closed orbit as a function of current with the beam position monitors located at finite dispersion. This method has already been used in earlier experiments to determine the distribution of the longitudinal impedance of different parts of LEP. In the present experiment the energy loss in two straight sections, containing only SC cavities, was compared with that in sections having both copper cavities and SC cavities. The results confirm the impedance calculations for the two types of cavities. The accuracy of the measurements was considerably improved by determining simultaneously the orbits of bunches with different currents. At the same time with these beam-based impedance measurements, the power dissipation was observed directly by local temperature monitors in different elements: the inter-cavity bellows inside the cryostat, the warm intermodule bellows, and Ferrite absorbers which were installed in two places to reduce the energy leaking out of cavities. These observations were correlated with the change of cryogenics power consumption, and showed an unexpected dependence of energy loss on beam energy

Integrating Murine Gene Expression Studies to Understand Obstructive Lung Disease due to Chronic Inhaled Endotoxin

Rationale: Endotoxin is a near ubiquitous environmental exposure that that has been associated with both asthma and chronic obstructive pulmonary disease (COPD). These obstructive lung diseases have a complex pathophysiology, making them difficult to study comprehensively in the context of endotoxin. Genome-wide gene expression studies have been used to identify a molecular snapshot of the response to environmental exposures. Identification of differentially expressed genes shared across all published murine models of chronic inhaled endotoxin will provide insight into the biology underlying endotoxin-associated lung disease. Methods: We identified three published murine models with gene expression profiling after repeated low-dose inhaled endotoxin. All array data from these experiments were re-analyzed, annotated consistently, and tested for shared genes found to be differentially expressed. Additional functional comparison was conducted by testing for significant enrichment of differentially expressed genes in known pathways. The importance of this gene signature in smoking-related lung disease was assessed using hierarchical clustering in an independent experiment where mice were exposed to endotoxin, smoke, and endotoxin plus smoke. Results: A 101-gene signature was detected in three murine models, more than expected by chance. The three model systems exhibit additional similarity beyond shared genes when compared at the pathway level, with increasing enrichment of inflammatory pathways associated with longer duration of endotoxin exposure. Genes and pathways important in both asthma and COPD were shared across all endotoxin models. Mice exposed to endotoxin, smoke, and smoke plus endotoxin were accurately classified with the endotoxin gene signature. Conclusions: Despite the differences in laboratory, duration of exposure, and strain of mouse used in three experimental models of chronic inhaled endotoxin, surprising similarities in gene expression were observed. The endotoxin component of tobacco smoke may play an important role in disease development