Effect of Excitation Signal on Double-Coil Inductive Displacement Transducer

A double-coil inductive displacement transducer is a non-contact element for measuring displacement and is widely used in large power equipment systems such as construction machinery and agricultural machinery equipment. The type of coil excitation signal has an impact on the performance of the transducer, but there is little research on this. Therefore, the influence of the coil excitation signal on transducer performance is investigated. The working principle and characteristics of the double-coil inductive displacement transducer are analyzed, and the circuit simulation model of the transducer is established. From the aspects of phase shift, linearity, and sensitivity, the effects of a sine signal, a triangle signal, and a pulse signal on the transducer are compared and analyzed. The results show that the average phase shift, linearity, and sensitivity of the sine signal were 11.53°, 1.61%, and 0.372 V/mm, respectively; the average phase shift, linearity and sensitivity of the triangular signal were 1.38°, 1.56%, and 0.300 V/mm, respectively; and the average phase shift, linearity, and sensitivity of the pulse signal were 0.73°, 1.95%, and 0.621 V/mm, respectively. It can be seen that the phase shift of a triangle signal and a pulse signal is smaller than that of a sine signal, which can result in better signal phase-locked processing. The linearity of the triangle signal is better than the sine signal, and the sensitivity of the pulse signal is better than that of the sine signal.

Effect of the Coil Excitation Method on the Performance of a Dual-Coil Inductive Displacement Transducer

A dual-coil inductive displacement transducer is a non-contact type measuring element for measuring displacement and is widely used in large power equipment systems such as construction machinery and agricultural equipment. However, the effect of the coil excitation method on the performance of dual-coil inductive displacement sensors has not been studied. This paper investigates the impact of different coil excitation methods on the operating performance of displacement transducers. The working principle, electromagnetic characteristics, and electrical characteristics were analyzed by building a mathematical model. A transducer measurement device was used to determine the relationship between core displacement and coil inductance. Three coil excitation methods were proposed, and the effects of the three coil excitation methods on the amplitude variation, phase shift, linearity, and sensitivity of the output signal were studied by simulation based on the AD630 chip as the core of the conditioning circuit. Finally, the study's feasibility was demonstrated by comparing the experiment to the simulation. The results show that, under the uniform magnetic field strength distribution in the coil, the coil voltage variation is proportional to the inductive core displacement. The amplitude variation is the largest for the dual-coil series three-wire (DCSTW) and is the same for the dual-coil series four-wire (DCSFW) and dual-coil parallel differential (DCPD). DCSFW has an enormous phase shift. DCSTW has the best linearity. The research in this paper provides a theoretical basis for selecting a suitable coil excitation, which is conducive to further improving the operating performance of dual-coil inductive displacement transducers.

Validation of portable electronic equipment (Accutrend® Plus) to determine glucose, total cholesterol, and triglycerides in rats (Rattus) and dogs (Canis lupus familiaris)

Objective

This study aimed to validate the analytical precision of the Accutrend^® Plus portable electronic equipment (PE) to determine glucose (GLU), total cholesterol (TC), and triglycerides (TG) in rats and dogs using the conventional laboratory method (CM) as a reference.

Materials and Methods

To determine the analytical accuracy of the Accutrend^® Plus in the measurement of GLU, CT, and TG. The EP-9-A2 guide (Clinical and Laboratory Standards Institute), Bland-Altman graphical analysis, and Lin's correlation coefficient of concordance (CCC) were implemented.

Results

The average differences (p > 0.05) between PE and CM for GLU, TC, and TG were 2.21, 1.20, and 0.72 mg·dl^-1, respectively, in rats and 1.06, 4.30, and 2.41 mg·dl^-1, respectively, in dogs (p > 0.05). Both methods showed a linear relationship with Pearson's correlation coefficients > 0.96 and R ² > 0.97 for the three biochemical indicators evaluated in both species. The GLU, TC, and TG values obtained by the PE were substantial, as evident from Lin's CCC > 0.96.

Conclusion

The PE Accutrend^® Plus is potent for monitoring GLU, TC, and TG in rats and dogs because of its precision and ability to facilitate measurement by reducing stress in animals during sampling.

Improving the linearity of synaptic plasticity of single-walled carbon nanotube field-effect transistors via CdSe quantum dots decoration

The linearity of synaptic plasticity of single-walled carbon nanotube field-effect transistor (SWCNT FET) was improved by CdSe quantum dots decoration. The linearity of synaptic plasticity in SWCNT FET with decorating QDs was further improved by reducing the P-type doping level from the atmosphere. The synaptic behavior of SWCNT FET is found to be dominated by the charging and discharging processes of interface traps and surface traps, which are predominantly composed of H₂O/O₂redox couples. The improved synaptic behavior is mainly due to the reduction of the interface trap charging process after QDs decoration. The inherent correlation between the device synaptic behavior and the electron capture process of the traps are investigated through charging-based trap characterization. This study provides an effective scheme for improving linearity and designing new-type SWCNT synaptic devices.

Linearity Enhancement Techniques for PGA Design

This paper presents some techniques to improve the linearity of traditional resistive feedback PGAs. By utilizing the switched op-amp in the PGA, the MOS switches in the feedback resistor array can be eliminated and thus the PGA's linearity can be improved. The PGA's linearity is further improved with an additional capacitor, which is used for pre-charging the sampling capacitor to strengthen its capability to drive the sampling capacitor without any extra power consumption. The pre-charge technique is especially suitable for the case where the PGA drives a large sampling capacitance. Implemented in SMIC 0.18 um CMOS technology, the proposed PGA can achieve a gain of 0.5 or 1 and consumes 4.68 mW at a single 5 V supply with the switched output stage enabled. When driving a 20 pF sampling capacitor at a sampling frequency of 200 kHz, the simulation results show that the proposed PGA can give a 9 dBc improvement in SFDR of the sampled signal compared to the traditional PGA design and the SFDR can reach up to 114 dBc.

Processing Validation Metrics of Syva Enzyme-Multiplied Immunoassay Technique (EMIT) Methotrexate Assay for Beckman Coulter System

BACKGROUND

High-dose methotrexate (HDMTX), defined as a dose greater than 500 mg/m², is used to treat a variety of cancers; and though safe, it can cause major toxicity. Syva enzyme-multiplied immunoassay technique (EMIT) methotrexate (MTX) assay (Gurgaon, India: Siemens Healthcare Diagnostics Ltd.) uses a homogeneous enzyme immunoassay method. Low-end precision performances are very important for laboratory methods, especially when their results have clinical significance at these levels.

METHODOLOGY

A total of 25 replicates (five replicates per run, for five runs) were analyzed for profiling. Precision, accuracy, linearity, limit of blank, limit of detection, and limit of quantification were determined using existing guidelines. Imprecision profile and limit of quantitation (LoQ) at 10% were determined by fitting data with hyperbolic regression.

RESULTS

The coefficient of variation percentage (CV%) for low, mid, and high-level internal quality control (IQC) was 1.25%, 3.45%, and 1.55%, respectively. Similarly, estimated bias was -4.58%, -3.54%, -7.21% for each level. The assay linearity was maintained from a range of 0.041-1.993 mmol/L with an R² of 0.959. The limit of detection was estimated to be 0.07 mmol/L.

CONCLUSION

Syva EMIT MTX assay can be precisely and accurately used to measure low levels of serum methotrexate at levels lower than claimed by the manufacturer, aiding in the monitoring of toxicity in patients.

Double Gate 6H-Silicon Carbide Schottky Barrier FET as Dielectrically Modulated Label Free Biosensor

This article presents a novel structure for efficient label free biosensing applications. The proposed device comprises of 6H-Silicon Carbide based double gate Schottky Barrier FET with two cavities to detect the biomolecules. Using Atlas TCAD simulations, it has been verified that the proposed device has the maximum ON current sensitivity of 1.02 × 10⁵, transconductance sensitivity of 7.741 × 10⁴, I_ON/I_OFF sensitivity of 31.4, sub-threshold swing sensitivity of 77.19 mV/decade and threshold voltage sensitivity of 34.54 mV for neutral biomolecule with K = 12. Similar simulations have also been performed for different charged biomolecules, varying from ± 5 × 10¹⁰ C/cm² to ± 1 × 10¹² C/cm². Besides, the proposed biosensor shows exceptional performance in terms of ON-current selectivity and sub-threshold swing selectivity. Finally, to check the device response for the changing input parameters, linearity of the biosensor has been analyzed. The achieved near-unity value of the Pearson’s fitness coefficient signifies the strong positive correlation between I_ON/I_OFF and dielectric property of the biomolecules.

Enhancing Performance of a MEMS-Based Piezoresistive Pressure Sensor by Groove: Investigation of Groove Design Using Finite Element Method

The optimal groove design of a MEMS piezoresistive pressure sensor for ultra-low pressure measurement is proposed in this work. Two designs of the local groove and one design of the annular groove are investigated. The sensitivity and linearity of the sensor are investigated due to the variations of two dimensionless geometric parameters of these grooves. The finite element method is used to determine the stress and deflection of the diaphragm in order to find the sensor performances. The sensor performances can be enhanced by creating the annular or local groove on the diaphragm with the optimal dimensionless groove depth and length. In contrast, the performances are diminished when the local groove is created on the beam at the piezoresistor. The sensitivity can be increased by increasing the dimensionless groove length and depth. However, to maintain low nonlinearity error, the annular and local grooves should be created on the top of the diaphragm. With the optimal designs of annular and local grooves, the net volume of the annular groove is four times greater than that of the local groove. Finally, the functional forms of the stress and deflection of the diaphragm are constructed for both annular and local groove cases.

An Analysis of Noise in Multi-Bit ΣΔ Modulators with Low-Frequency Input Signals

Digital and smart sensors are commonly implemented using multi-bit ΣΔ Modulators. Undesired signals can be present at the ADC input, such as low-frequency signals with medium or high amplitude, as a consequence of mechanical artifacts in the MEMS and/or temporary signal overload. Simulations and measurements of those sensors with such signals show temporary increments of in-band noise power. This paper investigates the factors that produce this transient performance loss. Interestingly, noise increments happen when the modulator is forced to toggle between three adjacent levels and is not correlated with the typical tonal behavior of ΣΔ Modulators. Hence, the sensor performance is sensitive to some specific input patterns even if tonal behavior is decreased by dithering the input of the ADC. Different error sources, such as the mismatch between DAC cells, loop filter linearity error, and quantization error, contribute to the observed noise increments. Our aim is to analyze each of these error sources to understand and quantify in-band noise power increments, and to desensitize the ADC from the undesired input patterns. Some estimation equations are proposed and verified through extensive simulations, by means of deterministic and stochastic methods. These equations are influenced by some modulator parameters and can be used to optimize them in order to reduce such in-band noise power increments.

Association between age and the presence and mortality of breast cancer synchronous brain metastases in the United States: A neglected SEER analysis

Background

The extent of the relationship between age and the presence of breast cancer synchronous brain metastases (BCSBMs) and mortality has not yet been well-identified or sufficiently quantified. We aimed to examine the association of age with the presence of BCSBMs and all-cause and cancer-specific mortality outcomes using the SEER database.

Methods

Age-associated risk of the presence and survival of BCSBMs were evaluated on a continuous scale (restricted cubic spline, RCS) with logistic or Cox regression models. The main endpoints were the presence of BCSBMs and all-cause mortality or cancer-specific mortality. Cox proportional hazards regression and competing risk models were used in survival analysis.

Results

Among 374,132 adult breast cancer patients, 1,441 (0.38%) had BMs. The presence of BCSBMs displayed a U-shaped relationship with age, with the highest point of the curve occurring at the age of 62. In both the younger (age ≤ 61) and older (age ≥ 62) groups, the observed curve showed a nearly linear relationship between age and the presence of BCSBMs. The relationship between age and all-cause mortality (ASM) and cancer-specific mortality (CSM) was linear. Older age at diagnosis was associated with a higher risk of ASM (HR 1.019, 95% CI: 1.013-1.024, p < 0.001) and CSM (HR 1.016, 95% CI: 1.010-1.023, p < 0.001) in multivariable Cox models. Age (sHR 1.007, 95% CI 1-1.013, p = 0.049) was substantially related to a significantly increased risk of CSM in competing risk models.

Conclusion

Age had a non-linear U-shaped relationship with the presence of BCSBMs and a linear relationship with BCSBMs mortality.

Directly Matching an MMIC Amplifier Integrated with MIMO Antenna through DNNs for Future Networks

Due to the exponential growth of data communications, linearity specification is deteriorating and, in high frequency systems, impedance transformation leading to power delivering from power amplifiers (PAs) to antennas is becoming an increasingly important concept. Intelligent-based optimization methods can be a suitable solution for enhancing this characteristic in the transceiver systems. Herein, to tackle the problems of linearity and impedance transformations, deep neural network (DNN)-based optimizations are employed. In the first phase, the antenna is modeled through the DNN with using the long short-term memory (LSTM) leading to forecast the load impedances in the a wide frequency band. Afterwards, the PA is modeled and optimized through another LSTM-based DNN using Multivariate Newton's Method where the optimal drain impedances are predicted from the first DNN (i.e., modeled antenna). The whole optimization methodology is executed automatically leading to enhance linearity specification of the whole system. For proving the novelty of the proposed method, monolithic microwave integrated circuit (MMIC) along with the multiple-input multiple-output (MIMO) antenna is designed, modeled, and optimized concurrently in the frequency band from 7.49 GHz to 12.44 GHz. The proposed method leads to enhancing the linearity of the transceiver in an effective way where DNN-based PA model gives rise to a solution for achieving the most optimal drain impedance through the modeled DNN-based antenna.

Controlled Growth of Wafer-Scale Transition Metal Dichalcogenides with a Vertical Composition Gradient for Artificial Synapses with High Linearity

Artificial synapses are promising for dealing with large amounts of data computing. Great progress has been made recently in terms of improving the on/off current ratio, the number of states, and the energy efficiency of synapse devices. However, the nonlinear weight update behavior of a synapse caused by the uncertain direction of the conductive filament leads to complex weight modulation, which degrades the delivery accuracy of information. Here we propose a strategy to improve the weight update behavior of synapses using chemical-vapor-deposition-grown transition metal dichalcogenides (TMDCs) with a vertical composition gradient, where the sulfur concentration decreases gradually along the thickness direction of TMDCs and thus forms a certain direction of the conduction filament for synapse devices. It is worth noting that the devices show an excellent linear conductance of potentiation and depression with a high linearity of 0.994 (surpassing most state-of-the-art synapses), have a large number of states, and are able to fabricate synapse arrays with wafer-scale. Furthermore, the devices based on the TMDCs with the vertical composition gradient exhibit an asymmetric feature of potentiation and depression behaviors with high linearity and follow the simulated linear Leaky ReLU function, resulting in a high recognition accuracy of 94.73%, which overcomes the unreliability issue in the Sigmoid function due to the vanishing gradient phenomenon. This study not only provides a universal method to grow TMDCs with a vertical composition gradient but also contributes to exploring highly linear synapses toward neuromorphic computing.

Wireless and Linear Hydrogen Detection up to 4% with High Sensitivity through Phase-Transition-Inhibited Pd Nanowires

Palladium (Pd) has been drawing increasing attention as a hydrogen (H₂) detecting material due to its highly selective sensitivity to H₂. However, at H₂ concentrations above 2%, Pd undergoes an inevitable phase transition, causing undesirable electrical and mechanical alterations. In particular, nonlinear gas response (ΔR/R₀) that accompanies phase transition has been a great bottleneck for detecting H₂ in high concentrations, which is especially important as there is a risk of explosion over 4% H₂. Here, we propose a phase-transition-inhibited Pd nanowire H₂ sensor that can detect up to 4% H₂ with high linearity and high sensitivity. Based on the calculation of the change in free energy, we designed Pd nanowires that are highly adhered to the substrate to withstand the stress that leads to phase transition. We theoretically optimized the Pd nanowire dimensions using a finite element method simulation and then experimentally fabricated the proposed sensor by exploiting a developed nanofabrication method. The proposed sensor exhibits a high sensing linearity (98.9%) with high and stable sensitivity (ΔR/R₀/[H₂] = 875%·bar^-1) over a full range of H₂ concentrations (0.1-4%). Using the fabricated Pd sensors, we have successfully demonstrated a wireless sensor module that can detect H₂ with high linearity, notifying real-time H₂ leakage through remote communication. Overall, our work suggests a nanostructuring strategy for detecting H₂ with a phase-transition-inhibited pure Pd H₂ sensor with rigorous scientific exploration.

Progressive and Stable Synaptic Plasticity with Femtojoule Energy Consumption by the Interface Engineering of a Metal/Ferroelectric/Semiconductor

In the era of "big data," the cognitive system of the human brain is being mimicked through hardware implementation of highly accurate neuromorphic computing by progressive weight update in synaptic electronics. Low-energy synaptic operation requires both low reading current and short operation time to be applicable to large-scale neuromorphic computing systems. In this study, an energy-efficient synaptic device is implemented comprising a Ni/Pb(Zr_0.52 Ti_0.48 )O₃ (PZT)/0.5 wt.% Nb-doped SrTiO₃ (Nb:STO) heterojunction with a low reading current of 10 nA and short operation time of 20-100 ns. Ultralow femtojoule operation below 9 fJ at a synaptic event, which is comparable to the energy required for synaptic events in the human brain (10 fJ), is achieved by adjusting the Schottky barrier between the top electrode and ferroelectric film. Moreover, progressive domain switching in ferroelectric PZT successfully induces both low nonlinearity/asymmetry and good stability of the weight update. The synaptic device developed here can facilitate the development of large-scale neuromorphic arrays for artificial neural networks with low energy consumption and high accuracy.

Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis

Urinalysis, an elementary chemical reaction-based method for analyzing color conversion factors, facilitates examination of pathological conditions in the human body. Recently, considerable urinalysis-centered research has been conducted on the analysis of urine dipstick colors using smartphone cameras; however, such methods have a drawback: the problem of reproducibility of accuracy through quantitative analysis. In this study, to solve this problem, the function values for each concentration of a range of analysis factors were implemented in an algorithm through urine dipstick RGB semi-quantitative color analysis to enable real-time results. Herein, pH, glucose, ketones, hemoglobin, bilirubin, protein (albumin), and nitrites were selected as analysis factors, and the accuracy levels of the existing equipment and the test application were compared and evaluated using artificial urine. In the semi-quantitative analysis, the red (R), green (G), and blue (B) characteristic values were analyzed by extracting the RGB characteristic values of the analysis factors for each concentration of artificial urine and obtaining linear function values. In addition, to improve the reproducibility of detection accuracy, the measurement value of the existing test equipment was set to an absolute value; using a machine-learning technique, the confusion matrix, we attempted to stabilize test results that vary with environment.

Synaptic Segmented Transistor with Improved Linearity by Schottky Junctions and Accelerated Speed by Double-Layered Nitride

Neuromorphic devices have been extensively studied to overcome the limitations of a von Neumann system for artificial intelligence. A synaptic device is one of the most important components in the hardware integration for a neuromorphic system because a number of synaptic devices can be connected to a neuron with compactness as high as possible. Therefore, synaptic devices using silicon-based memory, which are advantageous for a high packing density and mass production due to matured fabrication technologies, have attracted considerable attention. In this study, a segmented transistor devoted to an artificial synapse is proposed for the first time to improve the linearity of the potentiation and depression (P/D). It is a complementary metal oxide semiconductor (CMOS)-compatible device that harnesses both non-ohmic Schottky junctions of the source and drain for improved weight linearity and double-layered nitride for enhanced speed. It shows three distinct and unique segments in drain current-gate voltage transfer characteristics induced by Schottky junctions. In addition, the different stoichiometries of Si_xN_y for a double-layered nitride is utilized as a charge trap layer for boosting the operation speed. This work can bring the industry potentially one step closer to realizing the mass production of hardware-based synaptic devices in the future.

Carbon Nanotube Coated Fibrous Tubes for Highly Stretchable Strain Sensors Having High Linearity

Strain sensors are currently limited by an inability to operate over large deformations or to exhibit linear responses to strain. Producing strain sensors meeting these criteria remains a particularly difficult challenge. In this work, the fabrication of a highly flexible strain sensor based on electrospun thermoplastic polyurethane (TPU) fibrous tubes comprising wavy and oriented fibers coated with carboxylated multiwall carbon nanotubes (CNTs) is described. By combining spraying and ultrasonic-assisted deposition, the number of CNTs deposited on the electrospun TPU fibrous tube could reach 12 wt%, which can potentially lead to the formation of an excellent conductive network with high conductivity of 0.01 S/cm. The as-prepared strain sensors exhibited a wide strain sensing range of 0-760% and importantly high linearity over the whole sensing range while maintaining high sensitivity with a GF of 57. Moreover, the strain sensors were capable of detecting a low strain (2%) and achieved a fast response time whilst retaining a high level of durability. The TPU/CNTs fibrous tube-based strain sensors were found capable of accurately monitoring both large and small human body motions. Additionally, the strain sensors exhibited rapid response time, (e.g., 45 ms) combined with reliable long-term stability and durability when subjected to 60 min of water washing. The strain sensors developed in this research had the ability to detect large and subtle human motions, (e.g., bending of the finger, wrist, and knee, and swallowing). Consequently, this work provides an effective method for designing and manufacturing high-performance fiber-based wearable strain sensors, which offer wide strain sensing ranges and high linearity over broad working strain ranges.

'He called me out of the blue': An ethnographic exploration of contrasting temporalities in a social prescribing intervention

Social prescribing, a way of connecting patients to local services, is central to the NHS Personalised Care agenda. This paper employs ethnographic data, generated with 19 participants between November 2018 and July 2020, to explore the socio-temporal relations shaping their experiences of a local social prescribing intervention. Our focus is on the ways in which the intervention synchronised with the multitude of shifting, complex and often contradictory 'timespaces' of our participants. Our focus on the temporal rhythms of everyday practice allows us to trace a tension between the linearity and long horizon of the intervention and the oft contrasting timeframes of participants, sometimes leading to a mismatch that limited the intervention's impact. Further, we observed an interventional 'drift' from continuity towards unsupported signposting and 'out-of-the-blue' contacts which favour the temporality of the intervention. We demonstrate a need for intervention planning to be flexible to multiple, often conflicting, temporalities. We argue that health interventions must account for the temporal relations lived by the people they seek to support.

Novel training method for metal-oxide memristive synapse device to overcome trade-off between linearity and dynamic range

Synapse devices are essential for the hardware implementation of neuromorphic computing systems. However, it is difficult to realize ideal synapse devices because of issues such as nonlinear conductance change (linearity) and a small number of conductance states (dynamic range). In this study, the correlation between the linearity and dynamic range was investigated. Consequently, we found a trade-off relationship between the linearity and dynamic range and proposed a novel training method to overcome this trade-off.

LC-QTOF-MS evaluation of rabbit-specific peptide markers for meat quantitation

Ten rabbit-specific tryptic peptide markers and one marker peptide specific to both rabbit and hare were evaluated for mass signal linearity in binary meat mixtures using liquid chromatography-quadrupole time-of-flight-mass spectrometry. Seven meat mixtures containing chicken and varying percentages of rabbit (1%, 5%, 10%, 30%, 60%, 90%, and 100%) were analyzed. Additionally, the signal linearity of twelve peptide markers for chicken meat was examined. The best candidate peptides for the quantification of meat content were selected. Five of eleven peptides for rabbit meat and five of twelve peptides for chicken meat showed good linearity (R 2 > 0.97). The limits of detection and limits of quantification for these markers were in the range of 0.43–1.91% [w/w] and 1.44–6.38% [w/w], respectively. The method allowed determination of the percentage content of rabbit and chicken meat in two- and three-component meat mixtures with good accuracy. The preliminary quantification data provide a starting point for developing label-free and absolute quantification methods for rabbit and chicken meat using multiple reaction monitoring of peptide markers.

A Single-Round Infection Fluorescent SARS-CoV-2 Neutralization Test for COVID-19 Serological Testing at a Biosafety Level-2 Laboratory

A robust serological test to measure neutralizing antibodies against SARS-CoV-2 in biosafety level-2 (BSL-2) laboratories is useful for monitoring antibody response after vaccination or natural infection. The gold standard assay is the conventional plaque reduction neutralization test (PRNT) which requires extensive labor, live viruses, and BSL-3 facilities. Recently, we developed a novel single-round infection fluorescent SARS-CoV-2 virus (SFV) that can be safely used at BSL-2 laboratories for high-throughput neutralization and antiviral testing. In this study, we evaluated the performance of the neutralization test using this SFV with 80 PRNT-positive and 92 PRNT-negative clinical serum or plasma specimens. The SFV neutralization test (SFVNT) has 100% sensitivity and specificity compared to the PRNT. Furthermore, the neutralizing titers generated by the SFVNT and PRNT are highly correlated, with R2 = 0.903 (p < 0.0001). Due to high sensitivity, specificity, accuracy, and reproducibility, the SFVNT can be deployed for the large-scale testing of COVID-19 patients or vaccinated people in general lab settings.

Are Mushrooms Parametric?

Designing with biological materials as a burgeoning approach in the architecture field requires the development of new design strategies and fabrication methods. In this paper, we question if designers can use a parametric design approach while working with living materials. The research uses fungi as a biomaterial probe to experiment with the parametric behavior of living systems. Running design experiments using fungi helps to understand the extent to which biological systems can be considered parametric and, if so, what kind of parametric systems they are. Answering these questions provides a method to work with complex biological systems and may lead to new approaches of fabricating materials by tuning the environmental parameters of biological growth.

Comparison between polynomial regression and weighted least squares regression analysis for verification of analytical measurement range

OBJECTIVES

Recently, the linearity evaluation protocol by the Clinical & Laboratory Standards Institute (CLSI) has been revised from EP6-A to EP6-ED2, with the statistical method of interpreting linearity evaluation data being changed from polynomial regression to weighted least squares linear regression (WLS). We analyzed and compared the analytical measurement range (AMR) verification results according to the present and prior linearity evaluation guidelines.

METHODS

The verification of AMR of clinical chemistry tests was performed using five samples with two replicates in three different laboratories. After analyzing the same evaluation data in each laboratory by the polynomial regression analysis and WLS methods, results were compared to determine whether linearity was verified across the five sample concentrations. In addition, whether the 90% confidence interval of deviation from linearity by WLS was included in the allowable deviation from linearity (ADL) was compared.

RESULTS

A linearity of 42.3-56.8% of the chemistry items was verified by polynomial regression analysis in three laboratories. For analysis of the same data by WLS, a linearity of 63.5-78.3% of the test items was verified where the deviation from linearity of all five samples was within the ADL criteria, and the cases where the 90% confidence interval of all deviation from linearity overlapped the ADL was 78.8-91.3%.

CONCLUSIONS

Interpreting AMR verification data by the WLS method according to the newly revised CLSI document EP6-ED2 could reduce laboratory workload, enabling efficient laboratory practice.

Evaluation of the scil vCell 5, a novel laser- and impedance-based point-of-care hematology analyzer, for use in dogs and cats

A novel laser- and impedance-based point-of-care hematology analyzer (POCA), the vCell 5 (scil Animal Care), providing a complete blood count with 5-part leukocyte differential count has recently been introduced to veterinary laboratories. We evaluated the analyzer for use in dogs and cats including method comparison and assessment of linearity, carryover, and precision. Fresh blood samples from 192 healthy and diseased dogs and 159 cats were analyzed, and results were compared to reference methods (i.e., microhematocrit [PCV], Advia 2120 hematology analyzer). Total observed error (TEo) was calculated from CV, obtained at 3 concentrations, and bias%, and compared to total allowable error (TEa). For both species, excellent correlation (rs = 0.93-0.99) was seen between methods for WBC and RBC, hematocrit, hemoglobin, and platelet counts (PLT), except for feline PLT (rs = 0.79). Quality requirements (TEo < TEa) were fulfilled for WBC (TEo = 8.6-11.1%; TEa = 20%) and RBC (TEo = 3.5-7%; TEa = 10%), hematocrit (TEo = 5.7-9.4%; TEa = 10%), PCV (cat TEo = 7.8%; TEa = 10%), mean corpuscular volume (cat TEo = 5.1%; TEa = 7%), and PLT (TEo = 13.1-24.1%; TEa = 25%). Excellent linearity was demonstrated for WBC, RBC, and PLT, and hemoglobin. CVs of <2% for WBC, RBC, hematocrit, hemoglobin, and of <5% (dog) and 8% (cat) for PLT were demonstrated for values within the RI. Except for calculated variables and well-known species-specific deviations in feline PLT, scil POCA results were correlated favorably with reference method results and complied with quality requirements for cats and dogs.

Graded Interlocks for Iontronic Pressure Sensors with High Sensitivity and High Linearity over a Broad Range

Flexible pressure sensors that have high sensitivity, high linearity, and a wide pressure-response range are highly desired in applications of robotic sensation and human health monitoring. The challenge comes from the incompressibility of soft materials and the stiffening of microstructures in the device interfaces that lead to gradually saturated response. Therefore, the signal is nonlinear and pressure-response range is limited. Here, we show an iontronic flexible pressure sensor that can achieve high sensitivity (49.1 kPa^-1), linear response (R² > 0.995) over a broad pressure range (up to 485 kPa) enabled by graded interlocks of an array of hemispheres with fine pillars in the ionic layer. The high linearity comes from the fact that the pillar deformation can compensate for the effect of structural stiffening. The response-relaxation time of the sensor is <5 ms, allowing the device to detect vibration signals with frequencies up to 200 Hz. Our sensor has been used to recognize objects with different weights based on machine learning during the gripper grasping tasks. This work provides a strategy to make flexible pressure sensors that have combined performances of high sensitivity, high linearity, and wide pressure-response range.