Background: Sudden Infant Death Syndrome is thought to partly result from unseen brain abnormalities affecting cardio-respiratory function, but for which no clear genetic or environmental mechanisms are known. Mouse genetic and exposure models present an opportunity to uncover molecular and environmental mechanisms. However, measuring cardio-respiratory function in neonate mice is expensive, difficult, and inefficient. One major challenge is the time needed to carry out such measurements. The neonate autoresuscitation assay, consisting of repeated anoxic exposures followed by recovery, requires the full attention of an observer for the multi-hour duration of a single-subject assay, limiting throughput. To address this, we developed a closed-loop feature detection platform for automated neonate cardio-respiratory measurements. Methods: Our platform design consists of: 1) a pneumotachograph face-mask for precise respiratory measurements. 2) a micro-controller automated bell-housing gas switching system for rapid induction of respiratory challenges. 3) a micro-computer-based data-acquisition system with real-time feature detection and outputs for controlling gas exposure. 4) a data analysis suite that assists with recordkeeping and provides a facile method to extract key outcome measures. Results: Gas challenges are administered via the rotating bell housing system. A python program detects waveform features in real-time, such as apnea and bradycardia. Upon apnea detection, the system switches to a rescue gas. Resumption of normal breathing and heart rate can also be detected and incorporated into criteria for automated initiation of the next anoxic exposure trial. Data is stored for later offline automated analysis using the data analysis suite. Conclusions: The system offers a relatively inexpensive approach for automated high throughput neonate cardio-respiratory assessment. The improvements permitting increased throughput and reduced variation in experimental parameters makes the system suitable for screening of genetic and exposure risks as well as potential therapeutics for Sudden Infant Death Syndrome. NIH: R01 HL130249, UM1HG006348, R01DK114356. BCM KOMP2 Precision Disease Modeling Pilot Award, BCM McNair Scholar Program, March of Dimes Basil O’Connor Research Award, Parker B. Francis Fellowship, CJ Foundation for SIDS This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly accelerating the throughput of the workflow for scientists. We present a framework that uses vision to address specific pain points in the characterization of perovskite semiconductors, a group of materials with the potential to form new types of solar cells. With this approach, we automate the measurement and computation of chemical and optoelectronic properties of perovskites. Our framework proposes the following four key contributions: (i) a computer vision tool for scalable segmentation to arbitrarily many material samples, (ii) a tool to extract the chemical composition of all material samples, (iii) an algorithm capable of automatically computing band gap across arbitrarily many unique samples using vision-segmented hyperspectral reflectance data, and (iv) automating the stability measurement of multi-hour perovskite degradation experiments with vision for spatially non-uniform samples. We demonstrate the key contributions of the proposed framework on eighty samples of unique composition from the formamidinium-methylammonium lead tri-iodide perovskite system and validate the accuracy of each method using human evaluation and X-ray diffraction.
Sudden Infant Death Syndrome (SIDS, a leading cause of death for neonate infants 1 month to 1 year old) and some forms of neonate death (ND, the leading cause of death for newborns 28 days old or less) are thought to result, in part, from unseen brain abnormalities affecting cardio‐respiratory function, but for which no clear genetic or environmental mechanisms are known. Mouse genetic and exposure models present an opportunity to uncover potential molecular and environmental mechanisms in SIDS and ND. However, measuring cardio‐respiratory function to model SIDS and ND in neonate mice is expensive, difficult, and inefficient. Previously, we have developed for in‐lab facile production an inexpensive face‐mask pneumotachograph system for neonate respiratory measurements. A remaining challenge is the time and effort needed to carryout neonate respiratory measurements. For example, the neonate autoresuscitation assay consisting of repeated anoxic exposures followed by recovery requires the full attention of a single observer for the duration of the assay (2‐3hrs) for a single subject, thus limiting the number of pups in a litter that can be assayed. To address these inefficiencies, we sought to develop a closed loop feature detection platform for automated neonate cardio‐respiratory measurements that can be deployed in large numbers for high throughput parallel studies by a single person. The platform design consists of three sub‐systems. First, we leveraged our previously published pneumotachograph face‐mask system for precise respiratory measurements (PMID: 28213294). Second, we developed a micro‐controller automated bell‐housing gas switching system for near square wave induction of respiratory challenges. Third we developed a micro‐computer‐based data‐acquisition system with real‐time feature detection and response outputs for controlling gas exposure times. The gas switching system consists of linear and rotational actuated carousel that holds four bell housings into which distinct gas mixes can flow by solenoid valves. Through the rotational and linear actuation driven by a pair of stepper motors, the required bell housing is rotated into position and moved over the end of the pneumotach and challenge (e.g. anoxic) gas flow is initiated by a solenoid valve, all under the control of the microcontroller. Micro‐controller action is directed by serial input from a micro‐computer receiving cardio‐respiratory waveform data through an analog to digital converter board. On the micro‐computer, a python program detects specific wave form features in real time (lag = 100ms), such as an apnea and bradycardia. Upon apnea detection, the micro‐controller is directed to switch to a rescue gas by retraction of the bell housing, rotation, and extension of a different bell housing containing the rescue gas mix. The resumption of normal breathing and heart rate can also be detected and incorporated into the criteria for the automated initiation of the next anoxic exposure trial. Data is stored for later offline automated analysis....
Sudden Infant Death Syndrome (SIDS, a leading cause of death for neonate infants 1 month to 1 year old) and some forms of neonate death (ND, the leading cause of death for newborns 28 days old or less) are thought to result, in part, from unseen brain abnormalities affecting cardio‐respiratory function, but for which no clear genetic or environmental mechanisms are known. Mouse genetic and exposure models present an opportunity to uncover potential molecular and environmental mechanisms in SIDS and ND. However, measuring cardio‐respiratory function to model SIDS and ND in neonate mice is expensive, difficult, and inefficient. Previously, we have developed for in‐lab facile production an inexpensive face‐mask pneumotachograph system for neonate respiratory measurements. A remaining challenge is the time and effort needed to carryout neonate respiratory measurements. For example, the neonate autoresuscitation assay consisting of repeated anoxic exposures followed by recovery requires the full attention of a single observer for the duration of the assay (2–3hrs) for a single subject, thus limiting the number of mice that can be assayed. To address these inefficiencies, we sought to develop a closed loop feature detection platform for automated neonate cardio‐respiratory measurements that can be deployed in large numbers for high throughput parallel studies by a single person. The platform design consists of three sub‐systems. First, we leveraged our previously published pneumotachograph face‐mask system for precise respiratory measurements (PMID:28213294). Second, we developed a micro‐controller automated bell‐housing gas switching system for near square wave induction of respiratory challenges. Third we developed a micro‐computer‐based data‐acquisition system with real‐time feature detection and response outputs for controlling gas exposure times. The gas switching system consists of linear and rotational actuated carousel that holds four bell housings into which distinct gas mixes can flow by solenoid valves. Through the rotational and linear actuation driven by a pair of stepper motors, the required bell housing is rotated into position and moved over the end of the pneumotach and challenge (e.g. anoxic) gas flow is initiated by a a solenoid valve, all under the control of the microcontroller. Micro‐controller action is directed by GPIO input from a micro‐computer receiving cardio‐respiratory waveform data through an analog to digital converter board. On the micro‐computer, a python program detects specific wave form features in real time (lag = 100ms), such as an apnea. Upon apnea detection, the micro‐controller is directed to switch to a rescue gas by retraction of the bell housing, rotation, and extension of a different bell housing containing the rescue gas mix. The resumption of normal breathing and heart rate can also be detected and incorporated into the criteria for the automated initiation of the next anoxic exposure trial. Data is stored for later offline automated analysis. The described apparatus offe...
Sudden Infant Death Syndrome (SIDS) is defined as a diagnosis of exclusion in the sudden and unexpected passing of an infant, usually during sleep, with an unknown etiology. SIDS remains a leading cause of death in infants and is predominant in males (60%). A leading hypothesis is that many SIDS cases are driven by a failure in the autoresuscitation reflex. The autoresuscitation reflex is a series of gasps that occur, when an infant is experiencing an apnea and bradycardia due to sever hypoxic conditions (i.e. sleeping in a prone position), in order to revitalize cardiorespiratory function. Additionally, increasing number of SIDS cases are associated with genetic variants in key genes. However, it remains unclear what genes are critical to the development and function of the neonate auto-resuscitation reflex. In order to identify genes involved in neonate respiration and protective respiratory reflexes, we aim to build a phenotyping pipeline to screen large numbers of mouse mutants generated by the NIH Knock Out Mouse Project (KOMP). KOMP is a national and international consortium effort to knock out and phenotype every gene in the mouse genome. Based on preliminary KOMP data, as many as 10 percent of the mutated genes (≈ 2000) are expected to present perinatal viability phenotypes without any obvious structural abnormalities, offering potential SIDS models. To assay mutant mouse lines, we employ the neonate autoresuscitation assay, a high face-value assay to model face down or obstructed sleeping conditions thought to be faced by many SIDS infants. This assay consists of a series of anoxic challenges to induce apnea, followed by a return to room air rescue to facilitate autoresuscitation. However, the current manual approach for the assay is time consuming, highly variable, and low through-put creating a barrier to screening large numbers of genetic mutants. To address these barriers, we developed an automated approach to the autoresuscitation assay which is paired with a comprehensive cardio-respiratory data analysis software suite, both developed in the Ray lab. The automation allows for high-throughput, parallel screening of multiple mice while standardizing various parameters, such as exposure time and recovery parameters without the need for human observers, thus reducing variability. Utilizing this automated pipeline, we present results from a pilot study assaying 5 mouse lines with loss of function mutations in genes likely to be involved in protective respiratory reflexes. Here we report the successful identification of a previously untested gene involved autoresuscitation which displays a sex-specific vulnerability in the neonate autoresuscitation reflex. NIH/NHLBI R01 HLN161142-01 This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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