A low cost MEMS based NDIR system for the monitoring of carbon dioxide in breath analysis at ppm levels

The molecules in our breath can provide a wealth of information about the health and well-being of a person. The level of carbon dioxide (CO2) is not only a sign of life but also when combined with the level of exhaled oxygen provides valuable health information in the form of our metabolic rate. We report upon the development of a MEMS-based non-dispersive infrared CO2 sensor for inclusion in a hand held portable breath analyser. Our novel sensor system comprises a thermopile detector and low power MEMS silicon on insulator (SOI) wideband infrared (IR) emitter. A lock-in amplifier design permits a CO2 concentration of 50 ppm to be detected on gas bench rig. Different IR path lengths were studied with gases in dry and humid (25% and 50% RH) in order to design a sensor suitable for detecting CO2 in breath with concentrations in the range of 4 to 5%. A breath analyser was constructed from acetal and in part 3D printed with a side-stream sampling mechanism and tested on a range of subjects with two data-sets presented here. The performance of the novel MEMS based sensor was validated using a reference commercial breath-by-breath sensor and produced comparable results and gave a response time of 1.3 s. Further work involves the detection of other compounds on breath for further metabolic analysis and reducing the overall resolution of our MEMS sensor system from ca. 250 ppm to 10 ppm

Time-Frequency Ridge Analysis Based on the Reassignment Vector

International audienceThis paper considers the problem of detecting and estimating AM/FM components in the time-frequency plane. It introduces a new algorithm to estimate the ridges corresponding to the instantaneous frequencies of the components, and to segment the time-frequency plane into different `basins of attraction', each basin corresponding to one mode. The technique is based on the structure of the reassignment vector, which is commonly used for sharpening time-frequency representations. Compared with previous approaches, this new method does not need extra parameters, exhibits less sensitivity to the choice of the window and shows better reconstruction performance. Its effectiveness is demonstrated on simulated and real datasets

Printable microscale interfaces for long-term peripheral nerve mapping and precision control

The nascent field of bioelectronic medicine seeks to decode and modulate peripheral nervous system signals to obtain therapeutic control of targeted end organs and effectors. Current approaches rely heavily on electrode-based devices, but size scalability, material and microfabrication challenges, limited surgical accessibility, and the biomechanically dynamic implantation environment are significant impediments to developing and deploying advanced peripheral interfacing technologies. Here, we present a microscale implantable device – the nanoclip – for chronic interfacing with fine peripheral nerves in small animal models that begins to meet these constraints. We demonstrate the capability to make stable, high-resolution recordings of behaviorally-linked nerve activity over multi-week timescales. In addition, we show that multi-channel, current-steering-based stimulation can achieve a high degree of functionally-relevant modulatory specificity within the small scale of the device. These results highlight the potential of new microscale design and fabrication techniques for the realization of viable implantable devices for long-term peripheral interfacing.https://www.biorxiv.org/node/801468.fullFirst author draf

Development of a low-cost NDIR system for ppm detection of carbon dioxide in exhaled breath analysis

The composition of exhaled breath contains important information regarding the health of our body. Measurements of the level of exhaled carbon dioxide can help both diagnose respiratory diseases and determine metabolic rate. A low-cost NDIR sensor has been developed that offers the detection of CO2 from the ppm range up to 5% level in human breath. An innovative lock-in amplifier system allows a 10 Hz drive signal to be recovered from the high frequency noise associated with a silicon thermopile infra-red detector. Laboratory experiments have demonstrated excellent stability (±0.10% in 25% RH) and repeatability between dry and humid conditions (±1.2% for 25% humidity increase). The response time is typically 2.4s, limited by the low drive frequency necessary for the MEMS-based wideband infra-red source. The current system has a resolution of ca. 10 ppm of CO2. Further refinement in signal processing and a higher drive frequency should permit even lower concentrations of CO2 to be detected with an ultimate target of 1 ppm. Existing performance has been shown to be suitable for breath analysis using a side-stream analyser

Does the butcher-on-the-bus phenomenon require a dual-process explanation? A signal detection analysis

The butcher-on-the-bus is a rhetorical device or hypothetical phenomenon that is often used to illustrate how recognition decisions can be based on different memory processes (Mandler, 1980). The phenomenon describes a scenario in which a person is recognized but the recognition is accompanied by a sense of familiarity or knowing characterized by an absence of contextual details such as the person’s identity. We report two recognition memory experiments that use signal detection analyses to determine whether this phenomenon is evidence for a recollection plus familiarity model of recognition or is better explained by a univariate signal detection model. We conclude that there is an interaction between confidence estimates and remember-know judgments which is not explained fully by either single-process signal detection or traditional dual-process models

Total Pancreatectomy with Islet Autologous Transplantation: The Cure for Chronic Pancreatitis?

Chronic pancreatitis (CP) is a debilitating disease that leads to varying degrees of pancreatic endocrine and exocrine dysfunction. One of the most difficult symptoms of CP is severe abdominal pain, which is often challenging to control with available analgesics and therapies. In the last decade, total pancreatectomy with autologous islet cell transplantation has emerged as a promising treatment for the refractory pain of CP and is currently performed at approximately a dozen centers in the United States. While total pancreatectomy is not a new procedure, the endocrine function-preserving autologous islet cell isolation and re-implantation have made the prospect of total pancreatectomy more acceptable to patients and clinicians. This review will focus on the current status of total pancreatectomy with autologous islet cell transplant including patient selection, technical considerations, and outcomes. As the procedure is performed at an increasing number of centers, this review will highlight opportunities for quality improvement and outcome optimization

On the Choice and Number of Microarrays for Transcriptional Regulatory Network Inference

<p>Abstract</p> <p>Background</p> <p>Transcriptional regulatory network inference (TRNI) from large compendia of DNA microarrays has become a fundamental approach for discovering transcription factor (TF)-gene interactions at the genome-wide level. In correlation-based TRNI, network edges can in principle be evaluated using standard statistical tests. However, while such tests nominally assume independent microarray experiments, we expect dependency between the experiments in microarray compendia, due to both project-specific factors (e.g., microarray preparation, environmental effects) in the multi-project compendium setting and effective dependency induced by gene-gene correlations. Herein, we characterize the nature of dependency in an <it>Escherichia coli </it>microarray compendium and explore its consequences on the problem of determining which and how many arrays to use in correlation-based TRNI.</p> <p>Results</p> <p>We present evidence of substantial effective dependency among microarrays in this compendium, and characterize that dependency with respect to experimental condition factors. We then introduce a measure <it>n</it>_{<it>eff </it>}of the effective number of experiments in a compendium, and find that corresponding to the dependency observed in this particular compendium there is a huge reduction in effective sample size i.e., <it>n</it>_{<it>eff </it>}= 14.7 versus <it>n </it>= 376. Furthermore, we found that the <it>n</it>_{<it>eff </it>}of select subsets of experiments actually exceeded <it>n</it>_{<it>eff </it>}of the full compendium, suggesting that the adage 'less is more' applies here. Consistent with this latter result, we observed improved performance in TRNI using subsets of the data compared to results using the full compendium. We identified experimental condition factors that trend with changes in TRNI performance and <it>n</it>_{<it>eff </it>}, including growth phase and media type. Finally, using the set of known E. coli genetic regulatory interactions from RegulonDB, we demonstrated that false discovery rates (FDR) derived from <it>n</it>_{<it>eff </it>}-adjusted p-values were well-matched to FDR based on the RegulonDB truth set.</p> <p>Conclusions</p> <p>These results support utilization of <it>n</it>_{<it>eff </it>}as a potent descriptor of microarray compendia. In addition, they highlight a straightforward correlation-based method for TRNI with demonstrated meaningful statistical testing for significant edges, readily applicable to compendia from any species, even when a truth set is not available. This work facilitates a more refined approach to construction and utilization of mRNA expression compendia in TRNI.</p

Fast Micron-Scale 3D Printing with a Resonant-Scanning Two-Photon Microscope

3D printing allows rapid fabrication of complex objects from digital designs. One 3D-printing process, direct laser writing, polymerises a light-sensitive material by steering a focused laser beam through the shape of the object to be created. The highest-resolution direct laser writing systems use a femtosecond laser to effect two-photon polymerisation. The focal (polymerisation) point is steered over the shape of the desired object with mechanised stages or galvanometer-controlled mirrors. Here we report a new high-resolution direct laser writing system that employs a resonant mirror scanner to achieve a significant increase in printing speed over galvanometer- or piezo-based methods while maintaining resolution on the order of a micron. This printer is based on a software modification to a commerically available resonant-scanning two-photon microscope. We demonstrate the complete process chain from hardware configuration and control software to the printing of objects of approximately $400\times 400\times 350\;\mu$m, and validate performance with objective benchmarks. Released under an open-source license, this work makes micro-scale 3D printing available the large community of two-photon microscope users, and paves the way toward widespread availability of precision-printed devices.Comment: Corresponding author: BWP ([email protected]). TJG and TMO contributed equally to this work. TJG is an employee of Neuralink In

Does the Butcher-on-the-Bus Phenomenon Require a Dual-Process Explanation? A Signal Detection Analysis

The butcher-on-the-bus is a rhetorical device or hypothetical phenomenon that is often used to illustrate how recognition decisions can be based on different memory processes (Mandler, 1980). The phenomenon describes a scenario in which a person is recognized but the recognition is accompanied by a sense of familiarity or knowing characterized by an absence of contextual details such as the person’s identity. We report two recognition memory experiments that use signal detection analyses to determine whether this phenomenon is evidence for a recollection plus familiarity model of recognition or is better explained by a univariate signal detection model. We conclude that there is an interaction between confidence estimates and remember-know judgments which is not explained fully by either single-process signal detection or traditional dual-process models

Investigation of the response of high-bandwidth MOX sensors to gas plumes for application on a mobile robot in hazardous environments

A custom sensor module has been developed comprising high-bandwidth metal oxide (MOX), low-cost non-dispersive infra-red (NDIR) and miniature solidly mounted resonator (SMR) acoustic sensors for use on a mobile exploration robot. The module has been tested in a wind tunnel in order to evaluate the performance of three MOX sensors (with coatings of PdPt SnO2, WO3 and NiO) to plumes of 2-propanol (concentration < 2.5 ppm). The formation of the VOC (volatile organic compound) plumes was verified through mapping of sensor responses across a grid of 9 positions in the wind tunnel. Fluctuating sensor responses were observed (±5%), demonstrating variation of VOC concentration within the gas plumes. Higher sensor responses were demonstrated with the n-type SnO2 and WO3 based devices (80% and 40% change relative to baseline, respectively) compared to the p-type NiO device (10%). Short plumes of VOC demonstrated the effect of gas pulse broadening, where longer duration responses (10% greater) were observed at locations further from the VOC source (∼0.4 m distance variation tested). Finally, the module was tested in a real-world environment, where plumes of VOC were observed using the MOX sensors and verified using a commercial Photoionization Detector (PID)