scholarly journals Optimizing Irradiation Geometry in LED-Based Photoacoustic Imaging with 3D Printed Flexible and Modular Light Delivery System

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3789 ◽  
Author(s):  
Maju Kuriakose ◽  
Christopher D. Nguyen ◽  
Mithun Kuniyil Ajith Singh ◽  
Srivalleesha Mallidi
Keyword(s):  
Angular Dependence ◽  
Photoacoustic Imaging ◽  
Low Cost ◽  
A Priori ◽  
Strong Dependence ◽  
Point Sources ◽  
Tissue Imaging ◽  
Scattering Media ◽  
Laser Systems ◽  
Imaging Plane

Photoacoustic (PA) imaging–a technique combining the ability of optical imaging to probe functional properties of the tissue and deep structural imaging ability of ultrasound–has gained significant popularity in the past two decades for its utility in several biomedical applications. More recently, light-emitting diodes (LED) are being explored as an alternative to bulky and expensive laser systems used in PA imaging for their portability and low-cost. Due to the large beam divergence of LEDs compared to traditional laser beams, it is imperative to quantify the angular dependence of LED-based illumination and optimize its performance for imaging superficial or deep-seated lesions. A custom-built modular 3-D printed hinge system and tissue-mimicking phantoms with various absorption and scattering properties were used in this study to quantify the angular dependence of LED-based illumination. We also experimentally calculated the source divergence of the pulsed-LED arrays to be 58° ± 8°. Our results from point sources (pencil lead phantom) in non-scattering medium obey the cotangential relationship between the angle of irradiation and maximum PA intensity obtained at various imaging depths, as expected. Strong dependence on the angle of illumination at superficial depths (−5°/mm at 10 mm) was observed that becomes weaker at intermediate depths (−2.5°/mm at 20 mm) and negligible at deeper locations (−1.1°/mm at 30 mm). The results from the tissue-mimicking phantom in scattering media indicate that angles between 30–75° could be used for imaging lesions at various depths (12 mm–28 mm) where lower LED illumination angles (closer to being parallel to the imaging plane) are preferable for deep tissue imaging and superficial lesion imaging is possible with higher LED illumination angles (closer to being perpendicular to the imaging plane). Our results can serve as a priori knowledge for the future LED-based PA system designs employed for both preclinical and clinical applications.

scholarly journals An Unconventional Multiple Low-Cost IMU and GPS-Integrated Kinematic Positioning and Navigation Method Based on Singer Model

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4274
Author(s):  
Zhu ◽  
Yu ◽  
Xiao
Keyword(s):  
Low Cost ◽  
A Priori ◽  
Strong Dependence ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Trajectory Model ◽  
The Road ◽  
Integration Strategy ◽  
Smooth Transitions

To release the strong dependence of the conventional inertial navigation mechanization on the a priori low-cost inertial measurement unit (IMU) error model, this research applies an unconventional multi-sensor integration strategy to integrate multiple low-cost IMUs and a global positioning system (GPS) for mass-market automotive applications. The unconventional integration strategy utilizes a basic three-dimensional (3D) kinematic trajectory model as the system model to directly estimate navigational parameters, and it allows the measurements from all of the sensors independently participating in measurement updates. However, the less complex kinematic model cannot realize smooth transitions between different motion statuses for the road vehicle with acceleration maneuvers. In this manuscript, we establish a more practical 3D kinematic trajectory model based on a “current” statistical Singer acceleration model to realize smooth transitions for the maneuvering vehicle. In addition, taking advantage of the unconventional strategy, we individually model the systematic errors of each IMU and the measurements of all sensors, in contrast to most existing approaches that adopt the common-mode errors for different sensors of the same design. A real dataset involving a GPS and multiple IMUs is processed to validate the success of the proposed algorithm model under the unconventional integration strategy.

scholarly journals High contrast three-dimensional photoacoustic imaging through scattering media by localized optical fluence enhancement

2013 ◽  
Vol 21 (22) ◽  
pp. 26671 ◽  
Author(s):  
Antonio M. Caravaca-Aguirre ◽  
Donald B. Conkey ◽  
Jacob D. Dove ◽  
Hengyi Ju ◽  
Todd W. Murray ◽  
...  
Keyword(s):  
Photoacoustic Imaging ◽  
Three Dimensional ◽  
High Contrast ◽  
Scattering Media

scholarly journals Unmet need of essential treatments for critical illness in Malawi

2021 ◽  
Author(s):  
Raphael Kazidule Kayambankadzanja ◽  
Carl Otto Schell ◽  
Isaac Mbingwani ◽  
Samson Kwazizira Mndolo ◽  
Markus Castegren ◽  
...  
Keyword(s):  
Critical Illness ◽  
Low Cost ◽  
A Priori ◽  
Unmet Need ◽  
Vital Signs ◽  
Lateral Position ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Oropharyngeal Airway ◽  
Conscious Level

AbstractBackgroundCritical illness is common throughout the world and has been the focus of a dramatic increase in attention in the COVID-19 pandemic. Severely deranged vital signs can identify critical illness, are simple to check and treatments that aim to correct derangements are established, basic and low-cost. The aim of the study was to estimate the unmet need of essential treatments for severely deranged vital signs in all adults admitted to hospitals in Malawi.MethodsWe conducted a cross-sectional study with follow-up of adult hospitalized patients in Malawi. All in-patients aged ≥18 on single days Queen Elizabeth Central Hospital (QECH) and Chiradzulu District Hospital (CDH) were screened.. Patients with hypoxia (oxygen saturation <90%), hypotension (systolic blood pressure <90mmHg) and reduced conscious level (Glasgow Coma Score <9) were included in the study. The a-priori defined essential treatments were oxygen therapy for hypoxia, intravenous fluid for hypotension and an action to protect the airway for reduced consciousness (placing the patient in the lateral position, insertion of an oropharyngeal airway or endo-tracheal tube or manual airway protection).ResultsOf the 1135 hospital in-patients screened, 45 (4.0%) had hypoxia, 103 (9.1%) had hypotension, and 17 (1.5%) had a reduced conscious level. Of those with hypoxia, 40 were not receiving oxygen (88.9%). Of those with hypotension, 94 were not receiving intravenous fluids (91.3%). Of those with a reduced conscious level, nine were not receiving an action to protect the airway (53.0%).ConclusionThere was a large unmet need of essential treatments for critical illness in two hospitals in Malawi.

Automated immunostaining-free prediction of breast carcinoma ER and PR status, Ki67 count, patient therapy stratification index and quantification of prognostic quiescence burden in TNBC from pretreatment H&E stained histopathology images in breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12535-e12535
Author(s):  
Tathagata Dasgupta ◽  
Satabhisa Mukhopadhyay ◽  
Nicolas M Orsi ◽  
Michele Cummings ◽  
Angelene Berwick
Keyword(s):  
Low Cost ◽  
A Priori ◽  
Disease Recurrence ◽  
Mitotic Checkpoint ◽  
Luminal A ◽  
Slide Image ◽  
Luminal Type ◽  
Therapy Stratification ◽  
Pre Treatment ◽  
Stratification System

e12535 Background: Categorical combinations of ER, PR, HER2, and Ki67 levels are traditionally used to classify patients into luminal A and B-like subtypes in order to inform treatment choice. Accounting for nearly 70% of all breast malignancies, luminal cancer is heterogeneous, harboring subtypes with distinct molecular profiles and clinical outcomes. Although most patients with luminal-type disease respond well to endocrine therapy alone, some develop recurrences benefiting from additional cytotoxic therapy. Identifying such cases a priori remains a challenge but would enable patients to be spared the debilitating side-effects of ineffective chemotherapy. In this regard, the efficacy of chemotherapy and disease recurrence relate to (i) ER driven G1/S perturbations and/or (ii) quiescent cell populations arrested in the G0/G1 phase of cell cycle. This study aimed to develop a histopathology whole slide image (WSI)-based, low cost, rapid and automated approach to: (i) predict ER/PR/Ki67 status, (ii) quantify quiescence burden, (iii) develop a G1/S-based patient stratification system for luminal A/B patients, and (iv) achieve a quiescence burden-based stratification of TNBC patients. Methods: This investigation centered on the initial clinical validation of a novel, immunostaining-free technology which uses information extracted from pre-treatment hematoxylin and eosin (H&E) stained slide WSIs alone to achieve these aims. Unlike conventional artificial intelligence-based approaches, the underlying proprietary algorithm and its prediction criteria are based on deterministic, hard-coded observational relationships of continuous scales drawn from WSI morphological features. In this instance, these represent tumor-related biological pathway disruptions and mitotic checkpoint perturbations, where G1/S perturbations enable luminal subtype stratification, and G0/G1 perturbations reflect quiescence burden. Back projecting the algorithm’s quiescence burden output on to the original WSIs enables morphological patterns to be mapped to quiescence burden.

Estimating local CH4 emissions in the Upper Silesian Coal Basin using inverse modelling

2021 ◽  
Author(s):  
Sebastian Wolff ◽  
Friedemann Reum ◽  
Christoph Kiemle ◽  
Gerhard Ehret ◽  
Mathieu Quatrevalet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
A Priori ◽  
Point Sources ◽  
Airborne Lidar ◽  
Inverse Modelling ◽  
Emission Rates ◽  
Coal Basin ◽  
Upper Silesian Coal Basin ◽  
The Individual ◽  
Modelling Approach

&lt;p&gt;Methane (CH&lt;sub&gt;4&lt;/sub&gt;) is the second most important anthropogenic greenhouse gas (GHG) with respect to radiative forcing. Since pre-industrial times, the globally averaged CH&lt;sub&gt;4&lt;/sub&gt; concentration in the atmosphere has risen by a factor of 2.5. A large fraction of global anthropogenic CH&lt;sub&gt;4&lt;/sub&gt; emissions originates from localized point sources, e.g. coal mine ventilation shafts. International treaties foresee GHG emission reductions, entailing independent monitoring and verification support capacities. Considering the spatially widespread distribution of point sources, remote sensing approaches are favourable, in order to enable rapid survey of larger areas. In this respect, active remote sensing by airborne lidar is promising, such as provided by the integrated-path differential-absorption lidar CHARM-F operated by DLR. Installed onboard the German research aircraft HALO, CHARM-F serves as a demonstrator for future satellite missions, e.g. MERLIN. CHARM-F simultaneously measures weighted vertical column mixing ratios of CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt; below the aircraft. In spring 2018, during the CoMet field campaign, measurements were taken in the Upper Silesian Coal Basin (USCB) in Poland. The USCB is considered to be a European hotspot of CH&lt;sub&gt;4&lt;/sub&gt; emissions, covering an area of approximately 50 km &amp;#215; 50 km. Due to the high number of coal mines and density of ventilation shafts in the USCB, individual CH&lt;sub&gt;4&lt;/sub&gt; exhaust plumes can overlap. This makes simple approaches to determine the emission rates of single shafts, i.e. the cross-sectional flux method, difficult. Therefore, we use an inverse modelling approach to obtain an estimate of the individual emission rates. Specifically, we employ the Weather Research and Forecast Model (WRF) coupled to the CarbonTracker Data Assimilation Shell (CTDAS), an Ensemble Kalman Filter. CTDAS-WRF propagates an ensemble realization of the a priori CH&lt;sub&gt;4&lt;/sub&gt; emissions forward in space and time, samples the simulated CH&lt;sub&gt;4&lt;/sub&gt; concentrations along the measurement&amp;#8217;s flight path, and scales the a priori emission rates to optimally fit the measured values, while remaining tied to the prior. Hereby, we obtain a regularized a posteriori best emission estimate for the individual ventilation shafts. Here, we report on the results of this inverse modelling approach, including individual and aggregated emission estimates, their uncertainties, and to which extent the data are able to constrain individual emitters independently.&lt;/p&gt;

Reconstruction of high frequency methane peaks from measurements of metal oxide low-cost sensors using machine learning

2021 ◽  
Author(s):  
Rodrigo Rivera Martinez ◽  
Diego Santaren ◽  
Olivier Laurent ◽  
Ford Cropley ◽  
Cecile Mallet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Metal Oxide ◽  
High Frequency ◽  
Polynomial Regression ◽  
Low Cost ◽  
Point Sources ◽  
Baseline Correction ◽  
High Concentration ◽  
Industrial Facilities

&lt;p&gt;Deploying a dense network of sensors around emitting industrial facilities allows to detect and quantify possible CH&lt;sub&gt;4&amp;#160;&lt;/sub&gt;leaks and monitor the emissions continuously. Designing such a monitoring network with highly precise instruments is limited by the elevated cost of instruments, requirements of power consumption and maintenance. Low cost and low power metal oxide sensor could come handy to be an alternative to deploy this kind of network at a fraction of the cost with satisfactory quality of measurements for such applications.&lt;/p&gt;&lt;p&gt;Recent studies have tested Metal Oxide Sensors (MO&lt;sub&gt;x&lt;/sub&gt;) on natural and controlled conditions to measure atmospheric methane concentrations and showed a fair agreement with high precision instruments, such as those from Cavity Ring Down Spectrometers (CRDS). Such results open perspectives regarding the potential of MOx to be employed as an alternative to measure and quantify CH&lt;sub&gt;4&lt;/sub&gt; emissions on industrial facilities. However, such sensors are known to drift with time, to be highly sensitive to water vapor mole fraction, have a poor selectivity with several known cross-sensitivities to other species and present significant sensitivity environmental factors like temperature and pressure. Different approaches for the derivation of CH&lt;sub&gt;4&lt;/sub&gt; mole fractions from the MO&lt;sub&gt;x&lt;/sub&gt; signal and ancillary parameter measurements have been employed to overcome these problems, from traditional approaches like linear or multilinear regressions to machine learning (ANN, SVM or Random Forest).&lt;/p&gt;&lt;p&gt;Most studies were focused on the derivation of ambient CH&lt;sub&gt;4&lt;/sub&gt; concentrations under different conditions, but few tests assessed the performance of these sensors to capture CH&lt;sub&gt;4&lt;/sub&gt; variations at high frequency, with peaks of elevated concentrations, which corresponds well with the signal observed from point sources in industrial sites presenting leakage and isolated methane emission. We conducted a continuous controlled experiment over four months (from November 2019 to February 2020) in which three types of MOx Sensors from Figaro&amp;#174; measured high frequency CH&lt;sub&gt;4&lt;/sub&gt; peaks with concentrations varying between atmospheric background levels up to 24 ppm at LSCE, Saclay, France. We develop a calibration strategy including a two-step baseline correction and compared different approaches to reconstruct CH&lt;sub&gt;4&lt;/sub&gt; spikes such as linear, multilinear and polynomial regression, and ANN and random forest algorithms. We found that baseline correction in the pre-processing stage improved the reconstruction of CH&lt;sub&gt;4&lt;/sub&gt; concentrations in the spikes. The random forest models performed better than other methods achieving a mean RMSE = 0.25 ppm when reconstructing peaks amplitude over windows of 4 days. In addition, we conducted tests to determine the minimum amount of data required to train successful models for predicting CH&lt;sub&gt;4&lt;/sub&gt; spikes, and the needed frequency of re-calibration / re-training under these controlled circumstances. We concluded that for a target RMSE &lt;= 0.3 ppm at a measurement frequency of 5s, 4 days of training are required, and a recalibration / re-training is recommended every 30 days.&lt;/p&gt;&lt;p&gt;Our study presents a new approach to process and reconstruct observations from low cost CH&lt;sub&gt;4&lt;/sub&gt; sensors and highlights its potential to quantify high concentration releases in industrial facilities.&lt;/p&gt;

scholarly journals 128 Factors Associated with Cost Savings Following Use of a Pharmacogenetic Assay in Individuals with Mood and Anxiety Disorders

CNS Spectrums ◽  
2020 ◽  
Vol 25 (2) ◽  
pp. 281-282
Author(s):  
Alison M Edwards ◽  
Roy H Perlis ◽  
David S Krause
Keyword(s):  
Bipolar Disorder ◽  
Anxiety Disorders ◽  
Low Cost ◽  
A Priori ◽  
Cost Savings ◽  
Initial Study ◽  
Baseline Period ◽  
Cost Difference ◽  
Outpatient Visits ◽  
Mood And Anxiety Disorders

Abstract:Background:In a study conducted in the database of a large commercial healthcare insurer, we previously demonstrated that use of a commercial pharmacogenetic assay for individuals with mood disorders was associated with decreased resource utilization and cost in the 6 month period following use compared to propensity-score matched controls. We conducted a post hoc analysis to understand variables associated with high cost savings.Methods:The results and methods of the initial study have previously been described. Cases were individuals with mood and anxiety disorders who received a commercial pharmacogenetic assay (Genomind, King of Prussia PA) to inform pharmacotherapy. 817 tested individuals (cases) with mood and/or anxiety disorders were matched to 2745 controls. Overall costs were estimated to be $1,948 lower in the tested group. The differences were largely the result of lesser emergency room and inpatient utilization for cases. In the present analysis, cost difference for cases compared to their matched controls was rank ordered by decile. High cost savers were arbitrarily defined a priori as the top 20% of savers. Using multivariable modeling techniques, an ordinal logistic regression model was generated in which baseline or follow-up variables were statistically tested for independent associations with high, low, and no cost savings.Results:606 (74%) of cases were net cost savers compared to their controls (cost difference <0). High cost savers (n=121) saved on average $10,690 compared to their matched controls. They were statistically more likely to have been diagnosed with bipolar disorder (n=33/121) than low cost savers (n=57/485) or non-savers (n=31/211), and had a lower Charlson Comorbidity index. High cost savers had fewer mean number of antidepressants in the baseline period (mean=3.16) compared to non-savers (3.73) but more than low cost savers (2.72) (p<0.05 across groups). In a multivariable model, bipolar, count of antidepressants, outpatient visits, and inpatient visits were statistically associated with being a high cost saver; antidepressant count and all-cause inpatient and outpatient visits in the baseline period were inversely associated with cost savings.Conclusions:Use of a pharmacogenetic assay was associated with cost-savings in the database of a large commercial insurer. Patients with bipolar disorder were more likely to be high cost savers than individuals with other mood and anxiety disorders.Funding Acknowledgements:Genomind

scholarly journals The BErkeley Atmospheric CO<sub>2</sub> Observation Network: field calibration and evaluation of low-cost air quality sensors

2018 ◽  
Vol 11 (4) ◽  
pp. 1937-1946 ◽  
Author(s):  
Jinsol Kim ◽  
Alexis A. Shusterman ◽  
Kaitlyn J. Lieschke ◽  
Catherine Newman ◽  
Ronald C. Cohen
Keyword(s):  
Air Quality ◽  
San Francisco ◽  
Low Cost ◽  
A Priori ◽  
Sensor Nodes ◽  
Bay Area ◽  
Attractive Option ◽  
Global Background ◽  
Zero Offset ◽  
The Individual

Abstract. The newest generation of air quality sensors is small, low cost, and easy to deploy. These sensors are an attractive option for developing dense observation networks in support of regulatory activities and scientific research. They are also of interest for use by individuals to characterize their home environment and for citizen science. However, these sensors are difficult to interpret. Although some have an approximately linear response to the target analyte, that response may vary with time, temperature, and/or humidity, and the cross-sensitivity to non-target analytes can be large enough to be confounding. Standard approaches to calibration that are sufficient to account for these variations require a quantity of equipment and labor that negates the attractiveness of the sensors' low cost. Here we describe a novel calibration strategy for a set of sensors, including CO, NO, NO2, and O3, that makes use of (1) multiple co-located sensors, (2) a priori knowledge about the chemistry of NO, NO2, and O3, (3) an estimate of mean emission factors for CO, and (4) the global background of CO. The strategy requires one or more well calibrated anchor points within the network domain, but it does not require direct calibration of any of the individual low-cost sensors. The procedure nonetheless accounts for temperature and drift, in both the sensitivity and zero offset. We demonstrate this calibration on a subset of the sensors comprising BEACO2N, a distributed network of approximately 50 sensor “nodes”, each measuring CO2, CO, NO, NO2, O3 and particulate matter at 10 s time resolution and approximately 2 km spacing within the San Francisco Bay Area.

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