Maximum Sensitivity Research Articles

Experimentally validated predictive PI-PD control strategy for delay-dominant chemical processes

Controlling delay dominant chemical processes is a challenging control problem. Hence, a new method for designing a Smith predictor (SP)-based proportional-integral proportional-derivative (PI-PD) control strategy is proposed in this study. The inner-loop PD parameters are determined using the phase margin (PM) and maximum sensitivity (Ms) specifications. To determine the outer-loop PI parameters, the moment matching technique is augmented with maximum sensitivity considerations. The performance and robustness of this design is compared with that of its contemporaries using four benchmark chemical process models including that of a stirred tank reactor and a heat exchanger. The resilience of this control strategy amid uncertainties in process parameters is studied and performance improvement achieved over contemporary works is quantified. Finally, the proposed design is also experimentally validated on a two-tank level control loop.

Bi3+/Mn4+ co-doped Y2MgTiO6 dual-emitting phosphors for plant growth lighting and temperature sensor

Novel Bi3+/Mn4+ co-doped Y2MgTiO6 dual-emitting phosphors were synthesized using a high-temperature solid-phase method. The crystal structure and luminescence properties of phosphors were systematically investigated by experimental characterization and theoretical calculation. Under excitation at 375 nm, Y2MgTiO6:Bi3+, Mn4+ phosphors exhibit two emission bands located at 410 nm and 696 nm (blue and red), which can well be matched with the absorption bands of chlorophylls a, b and phytochrome. The Y2MgTiO6: Bi3+, Mn4+ luminescence remains at 66.2 % intensity at 423 K and quantum yield is 52.06 %. Optical temperature properties of Y2MgTiO6:Bi3+, Mn4+ were investigated using fluorescence intensity ratio (FIR) analysis and were found to reach a maximum relative sensitivity of 2.93 % K−1 at 498 K. The study suggests that this material could be a potential option for indoor plant culture and optical temperature sensing.

Yb3+ concentration influence on NIR and upconversion emission and temperature sensing properties of Er3+/Yb3+ co-doped Ta2O5 particles

We synthesized Er3+ and Yb3+ co-doped tantalum oxide (Ta2O5) spherical sub-micrometric particles by a facile method involving formation of tantalum glycolates after pouring in acetone. Then, we investigated how the annealing temperature and Yb3+ concentration (0–30 mol %) affect their structural and luminescence properties. Particle sizes ranged from 146 to 258 nm for the samples annealed at 800 °C. XRD analysis revealed the orthorhombic L-Ta2O5 crystalline structure. The different symmetry sites that the rare earth ions can occupy within the Ta2O5 structure led to intense and broad emission bands centered around 1530 nm. Up-conversion measurements and the calculated number of photons for intense green and red emissions obtained with 980 and 1550 nm excitations helped us to elucidate the mechanisms involved at both excitation wavelengths. We investigated how the 0.5 mol % Er3+/1.5 mol % Yb3+ co-doped Ta2O5 sample performs as a primary thermometer by using the Boltzmann distribution law to predict the absolute temperature from the ratio between the Er3+ 2H11/2 → 4I15/2 and 4S3/2 → 4I15/2 transitions. We obtained a maximum relative thermal sensitivity of 0.97 ± 0.04 % K−1 and a minimum δT of 2.41 K. The calculated repeatability was above 95%. These results show that promising nanothermometer based on Ta2O5 with controlled size and morphology can be designed.

Dual Sensing Signal Decoupling Based on Thermoelectric Polymer Aerogels for Precise Temperature and Pressure Recognition

AbstractThe capability to emulate skin‐like temperature and pressure sensing is fundamental for next‐generation artificial intelligence products. However, detecting temperature and pressure simultaneously with a single sensor without signal interference is challenging. Herein, a novel PCC aerogel sensor composed of PEDOT:PSS, CNTs, and CNF via directional freezing technology is developed. The PCC sensor can decouple temperature and pressure stimuli into individual voltage and resistance signals. It exhibits high‐precision temperature sensing capabilities, boasting an exceptionally high Seebeck coefficient of 30.4 µV K‐1 and the ability to detect temperature variations as low as 0.1 K. PCC sensors show excellent sensitivity and fast response times for detecting static and dynamic pressures, as well as high stability after 1000 cycles. Its maximum pressure sensitivity can reach 159.1% kPa−1, and the lowest detection limit is 10 Pa. Additionally, its excellent thermoelectric properties also enable to generating thermopower from human skin for self‐powered pressure sensing. A 3×3 PCC sensor array has been proposed to simulate the unique features of human skin in temperature and pressure recognition. This work provides a scalable manufacturing strategy for multi‐functional tactile sensors.

Directional Multi-Resonant Micro-Electromechanical System Acoustic Sensor for Low Frequency Detection.

This paper reports on the design, modeling, and characterization of a multi-resonant, directional, MEMS acoustic sensor. The design builds on previous resonant MEMS sensor designs to broaden the sensor's usable bandwidth while maintaining a high signal-to-noise ratio (SNR). These improvements make the sensor more attractive for detecting and tracking sound sources with acoustic signatures that are broader than discrete tones. In-air sensor characterization was conducted in an anechoic chamber. The sensor was characterized underwater in a semi-anechoic pool and in a standing wave tube. The sensor demonstrated a cosine-like directionality, a maximum acoustic sensitivity of 47.6 V/Pa, and a maximum SNR of 88.6 dB, for 1 Pa sound pressure, over the bandwidth of the sensor circuitry (100 Hz-3 kHz). The presented design represents a significant improvement in sensor performance compared to similar resonant MEMS sensor designs. Increasing the sensitivity of a single-resonator design is typically associated with a decrease in bandwidth. This multi-resonant design overcomes that limitation.

Comparative Study of Biofilm and Non-Biofilm Producing Klebsiella pneumoniae with Special Reference to Metallo-Beta-Lactamase Production

Klebsiella pneumoniae is one of the most common bacteria among all biofilm-producing as well as the beta-lactamase producing strains, which is responsible for multi-drug resistance. For better therapeutic applications, it is important to detect the biofilm production by Klebsiella pneumoniae and their antibiogram along with the ability to produce ESBL, MBL, and AmpC β-lactamases. The aim of the study was to determine the prevalence of biofilm formation and ESBL, MBL, AmpC β-lactamase phenotypes in K. pneumoniae as well as the antibiogram of all (biofilm and MBL-producing and non-producing) isolates of K. pneumoniae. Isolates of K. pneumoniae were tested for biofilm formation by the Congo-red agar method. ESBL, MBL, and AmpC β-lactamase detection were done by both screening and confirmatory tests as per CLSI guidelines. The antibiogram was obtained by the Kirby-Bauer disc diffusion method. Among the total 100 isolates of K. pneumoniae, 40% were biofilm-producing. Most of them were from urine specimens. Out of biofilm-producing isolates, ESBL – 28%, MBL- 47% and AmpC β-lactamase- 25.8% producers were observed. K. pneumoniae isolates were seen to have maximum resistance to ceftazidime and maximum sensitivity to nitrofurantoin. Study findings suggest the importance of assessment of biofilm formation for better treatment. The scenario further worsens if such biofilm-producing isolates are also MBL-positive leading to limited therapeutic options.

Optical switching with high-Q Fano resonance of all-dielectric metasurface governed by bound states in the continuum

The discovery of bound states in the continuum (BIC) of optical nanostructures has garnered significant research interest and found widespread application in the field of optics, leading to an attractive approach to achieve high-Q (Quality factor) Fano resonance. Herein, an all-dielectric metasurface consisting of four gallium phosphide (Gap) cylinders on the MgF2 substrate is designed and analyzed by the finite element method (FEM). By breaking the symmetry of the plane, specifically by moving the two cylinders to one side, it is possible to achieve a transition from the symmetry-protected BIC to quasi-BIC. This transition enables the excitation of sharp dual-band Fano resonance at wavelengths of 1,045.4 nm and 1,139.6 nm, with the maximum Q factors reaching 1.47 × 104 and 1.28 × 104, respectively. The multipole decomposition and near-field distributions show that these two QBICs are dominated by the electric quadrupole (EQ) and magnetic quadrupole (MQ). Furthermore, bidirectional optical switching can be accomplished by changing the polarization direction of the incident light. As a result, the maximum sensitivity and figure of merit (FOM) are 488.9 nm/RIU and 2.51 × 105 RIU−1, respectively. The results enrich our knowledge about BIC and reveal a platform for the development of high-performance photonics devices such as optical switches and sensors.

Resistive flexible strain sensor based on AgNWs-Gr-PDMS for wearable electronics

In order to obtain a flexible strain sensor with higher sensitivity, a resistive flexible strain sensor based on the AgNWs-graphene-PDMS sensitive layer is prepared in this paper. This paper presents the preparation of a flexible sensitive layer, the preparation and encapsulation of the flexible strain sensor, and the performance tests of the sensor prepared with AgNWs-graphene (AgNWs-Gr) mixtures at different concentrations. The test results indicate that the prepared sensor has a wide detection range with a maximum stretchable length of 118%. The flexible strain sensor prepared with the AgNWs-Gr mixture at a concentration of 5% has the highest sensitivity and the maximum strain sensitivity of up to 236. It also has good repeatability, stability, and rapid response, and it has been tested for practical applications. The results show that the flexible strain sensor has a broad application prospect in fields such as wearable devices and intelligent robots.

Dual lanthanide Complexes-Loaded ratiometric nanothermometer for elucidating intracellular variations of temperature and calcium transport

Herein, we report a ratiometric nanothermometer to study the correlation of heat/temperature and intracellular calcium transport. Firstly, poly(methyl methacrylate)-based copolymeric nanoparticles (NPs) from nanoprecipitation encapsulates two lanthanide complexes (Eu(DNPD)3Phen and Sm(DBM)3Phen) to enhance fluorescence of lanthanide ions (Ln3+). NPs were thereafter coated with an amphiphilic block copolymer pluronic F-127 to prevent nonspecific interaction between NPs and proteins, which ensures internalization of the nanothermometer into living cells. The back energy transfer (BET) from the excited state of Ln3+ to the triplet energy levels of ligand endows the temperature dependence on ratiometric fluorescence of Eu3+ and Sm3+. This nanothermometer has excellent anti-interference ability, ensuring accurate cell temperature measurement within a range of 29.0–40.0℃. It exhibits a maximum relative thermal sensitivity of 1.9 % ℃-1 at 29.0℃ and a minimum temperature resolution of 0.1℃ at 39.0℃ in live HeLa cells. The nanothermometer was applied for studying temperature or heat induced intracellular Ca2+ transport. It illustrated that free cytoplasmic Ca2+ concentration ([Ca2+]i) increased only when the cell temperature reached a certain threshold under photothermal stimulation, and the rise of temperature corresponds to the increase of [Ca2+]i. Furthermore, Ca2+ burst induced Ca2+ transport from cytoplasm to endoplasmic reticulum, which promoted ATP hydrolysis along with the rise of intracellular temperature. 200 s stimulation with FCCP enhanced ca. 1.7℃ of intracellular temperature along with 2.2 folds increment of [Ca2+]i, as FCCP induces Ca2+ efflux from mitochondria by increasing proton concentration therein. These results indicate that a close relationship between intracellular temperature and Ca2+ transport, which is of great significance for further understanding the role of temperature in signaling pathways and neurotransmitter release events.

Excessive Hypercholesterolemia in Pregnancy Impairs Later-life Maternal Vascular Function in Rats

Background: Preeclampsia is a significant risk factor for the development of later-life cardiovascular disease; however, the underlying molecular mechanisms are not fully understood. Studies have shown that excessive hypercholesteremia occurring during pregnancy increases the risk of vascular pregnancy complications, such as preeclampsia. Bioavailability of nitric oxide (NO), a critical endothelium-derived vasodilator in pregnancy, is reduced in both hypercholesteremia and preeclampsia. However, the later-life vascular impact of excessive pregnancy-specific hypercholesteremia is not known. Hypothesis: Excessive hypercholesteremia in pregnancy impairs later-life maternal vascular function by reducing NO availability. Methods: Sprague-Dawley rats were fed a control diet (CD) or high cholesterol diet (HCD; 2% cholesterol + 0.5% cholic acid) from gestational day 6 to 20 (term=22 days; n=10-12). After pregnancy, all dams received a CD. Three months after pregnancy (equal to ~10 years in humans), mesenteric (systemic resistance arteries) and carotid (vascular bed that supplies blood to the cerebral vasculature) arteries were isolated, and endothelium-dependent (to methacholine, MCh) and -independent (to sodium nitroprusside, SNP) vasodilation, plus vasoconstriction responses (to phenylephrine, PE) were assessed using wire myography. NO contribution was assessed using a pan-NO synthase inhibitor (L-NAME; 100 μmol). Data were summarized as maximum responses (Emax), sensitivity (pEC50), or delta Emax (control vs. L-NAME). The statistical tests applied were a Student’s t-test or two-way ANOVA with Sidak’s post-hoc test (significance: p<0.05). Results: In mesenteric arteries, vasodilation to MCh or SNP was not different between the CD and HCD groups three months after the insult. However, L-NAME reduced maximum vasodilation to MCh in the HCD group (p=0.0433) but not in CD animals. Similarly, vasoconstriction to PE was not different between groups, but L-NAME increased the PE sensitivity in the HCD group (p=0.0423), without effects in the CD animals. In carotid arteries, a HCD during pregnancy reduced vasodilation to MCh (p=0.0345), and while L-NAME prevented vasodilation to MCh in both groups (p<0.0001), this effect was more pronounced in the HCD compared to CD group (delta Emax, p=0.0422). Vasodilation to SNP was similar between groups. Vasoconstriction responses to PE were increased in carotid arteries of HCD compared to CD animals (p=0.0357). L-NAME increased PE-mediated vasoconstriction in both groups (CD: 4.32-fold and HCD: 2.71-fold), but this NO modulation was significantly less in the HCD group compared to CD (delta Emax, p=0.0337). Conclusion: Exposure to a HCD, during pregnancy only, impaired later-life maternal vascular function in carotid, but not mesenteric arteries, suggesting vascular-bed specific effects in animals with a history of preeclampsia. Moreover, a HCD during pregnancy altered the NO pathway in both vascular beds, indicating that long-term alterations in NO were established during pregnancy. In summary, excessive pregnancy-specific hypercholesterolemia contributes to later-life maternal vascular dysfunction via modulation of the NO pathway, which may help explain the increased cardiovascular risk after preeclampsia. This work was supported by a foundation grant from the Canadian Institutes of Health Research (CIHR FS154313) and by the Women and Children’s Health Research Institute (WCHRI) through the generosity of the Stollery Children’s Hospital Foundation and the Alberta Women’s Health Foundation. A.A.O. is supported by a WCHRI postdoctoral fellowship through the generosity of the Stollery Children’s Hospital Foundation and the Alberta Women’s Health Foundation. E.E. was supported by a summer research studentship from Alberta Innovates. This is the full abstract presented at the American Physiology Summit 2024 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.

PID controller tuning based on PSO and dominant pole placement

This paper proposed a new approach for proportional integral derivative (PID) controller tuning. particle swarm optimization (PSO) algorithm and dominant pole placement were used for PID controller tuning. Minimizing the estimation criterion with integral of absolute error (IAE) and overshoot by PSO algorithm, we determined the dominant pole and crossover frequency satisfying the maximum sensitivity and phase margin. This dominant pole and crossover frequency were used in order to design the PID controller. It satisfied robust stability and improves the control effect of the system to balance the trade-off between overshoot and settling time. We simulated this approach for SOPDT model. A simulation results were given to demonstrate the effectiveness of this approach.

Incremental Trainable Parameter Selection in Deep Neural Networks.

This article explores the utilization of the effective degree-of-freedom (DoF) of a deep learning model to regularize its stochastic gradient descent (SGD)-based training. The effective DoF of a deep learning model is defined only by a subset of its total parameters. This subset is highly responsive or sensitive toward the training loss, and its cardinality can be used to govern the effective DoF of a model during training. To this aim, the incremental trainable parameter selection (ITPS) algorithm is introduced in this article. The proposed ITPS algorithm acts as a wrapper over SGD and incrementally selects the parameters for updation that exhibit the maximum sensitivity toward the training loss. Hence, it gradually increases the DoF of the model during training. In ideal cases, the proposed algorithm arrives at a model configuration (i.e., DoF) optimum for the task at hand. This whole process results in a regularization-like behavior induced by a gradual increment of the DoF. Since the selection and updation of parameters is a function of the training loss, the proposed algorithm can be seen as a task and data-dependent regularization mechanism. This article exhibits the general utility of ITPS by evaluating it on various prominent neural network architectures such as CNNs, transformers, recurrent neural networks (RNNs), and multilayer perceptrons. These models are trained for image classification and healthcare tasks using the publicly available CIFAR-10, SLT-10, and MIMIC-III datasets.

Role of lateral Core-Offset in the sensitivity enhancement of single Mode-No core-Single mode fiber cascaded structure

In this study, an extensive examination of the influence of core-offset on different sensitivities of a single-mode-no-core-single-mode (SNS) fiber sensor probe in a variety of external disturbances, including refractive index changes, strain, and temperature, has been presented and thoroughly investigated. To achieve this, a consistent sensing length of 5 cm no-core fiber (NCF) was utilized between two single-mode fibers (SMFs) with different offset values, facilitated by the built-in options of a fusion splicer. The theoretical and experimental principles of sensitivity are discussed in detail. Key aspects such as spectral analysis and fast Fourier transform of the disclosed transmission spectrum underwent meticulous scrutiny. The outcome of the experiment indicates that the SNS fiber sensor exhibits an increasing sensitivity to refractive index (RI) over the 1.333–1.398 range when the offset value increases, with a maximum of 201.221 nm/RIU sensitivity. On the other hand, the RI sensitivity decreases with increasing offset value in the range of 1.402–1.441; the highest sensitivity attained is 656.641 nm/RIU. The strain sensitivity shows a decreasing trend with increasing offset, with the maximum sensitivity reaching −1.59 pm/µε in a wide range of 0 to 2000 µε. For temperature sensing, the sensitivity remains unaffected with a core offset; consistently, the achieved sensitivity is approximately 11.51 pm/°C over a broad range of temperature of 30 °C to 120 °C Compared with RI, it is found that the reliance of core-offset on strain sensitivity is very low whereas on temperature sensing almost insensitive, provided that the sensing length and diameter remain constant. These results provide insightful direction for the creation of extremely sensitive sensors for a variety of applications.

Multiple toroidal dipole Fano resonances from quasi-bound states in the continuum in an all-dielectric metasurface

In this paper, a highly sensitive sensor consisting of a silicon nanorod and symmetric rings (SNSR) is presented. Theoretically, three Fano resonances with high Q-factors are excited in the near-infrared range by breaking the symmetry structure based on quasi-bound states in the continuum (Q-BICs). The electromagnetic near-field analysis confirms that the resonances are mainly controlled by toroidal dipole (TD) resonance. The structure is optimized by adjusting different geometrical parameters, and the maximum Q-factor of the Fano resonances can reach 7427. To evaluate the sensing performance of the structure, the sensitivity and the figure of merit (FOM) are calculated by adjusting the environmental refractive index: the maximum sensitivity of 474 nm/RIU and the maximum FOM of 3306 RIU-1. The SNSR can be fabricated by semiconductor-compatible processes, which is experimentally evaluated for changes in transmission spectra at different solution concentrations. The results show that the sensitivity and the Q-factor of the designed metasurface can reach 295 nm/RIU and 850, while the FOM can reach 235 RIU-1. Therefore, the metasurface of SNSR is characterized by high sensitivity and multi-wavelength sensing, which are current research hotspots in the field of optics and can be applied to biomedical sensing and multi-target detection.

A Theoretical Approach to Study the Frequency Dependent Dielectric behavior of Breast Cancer Cells using AlN buffer-based AlGaN/GaN HEMT

Abstract This article investigates the applicability of open gated high electron mobility transistor (HEMT) as a biosensing platform for Breast cancer detection. The present study reports the detection of healthy (MDA-10) and malignant cells (MDA-MB-231) associated with breast cancer using high-quality AlN buffer AlGaN/GaN HEMT with dual cavity structure formed by etching out the gate metal from source and drain sides under the gate region. The AlN buffer AlGaN/GaN HEMT provides better 2DEG confinement and large conduction band offset than GaN buffer. The proposed design is analyzed at frequencies of 900 MHz and 10 GHz, as breast cells have distinct dielectric properties at different microwave frequencies. The variation in dielectric constant of the cavity region corresponding to biomolecular species will change the device’s electrical characteristics and hence can be used to detect breast cancer cells. In this work, the device sensing parameters considered for analysis are shift in threshold voltage and drain current sensitivity. The device performance has also been analyzed by optimizing cavity thickness and length to select the best design for the sensing. The effect of non-ideal conditions such as steric hindrance and probing is also studied by emulating these real time effects in simulation. The device shows a maximum drain current sensitivity of 29.94% for 20 nm of cavity thickness and 1µm of the cavity length. The results depict that the proposed device design exhibits a highly sensitive response and can be promising alternate for future biosensing applications.

Learning active subspaces and discovering important features with Gaussian radial basis functions neural networks

Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this challenge, we propose a modification of the radial basis function neural network model by equipping its Gaussian kernel with a learnable precision matrix. We show that precious information is contained in the spectrum of the precision matrix that can be extracted once the training of the model is completed. In particular, the eigenvectors explain the directions of maximum sensitivity of the model revealing the active subspace and suggesting potential applications for supervised dimensionality reduction. At the same time, the eigenvectors highlight the relationship in terms of absolute variation between the input and the latent variables, thereby allowing us to extract a ranking of the input variables based on their importance to the prediction task enhancing the model interpretability. We conducted numerical experiments for regression, classification, and feature selection tasks, comparing our model against popular machine learning models, the state-of-the-art deep learning-based embedding feature selection techniques, and a transformer model for tabular data. Our results demonstrate that the proposed model does not only yield an attractive prediction performance compared to the competitors but also provides meaningful and interpretable results that potentially could assist the decision-making process in real-world applications. A PyTorch implementation of the model is available on GitHub at the following link.11https://github.com/dannyzx/Gaussian-RBFNN.

Characterization of tumors of jaw: Additive value of contrast enhancement and dual-energy computed tomography.

Currently, the differentiation of jaw tumors is mainly based on the lesion's morphology rather than the enhancement characteristics, which are important in the differentiation of neoplasms across the body. There is a paucity of literature on the enhancement characteristics of jaw tumors. This is mainly because, even though computed tomography (CT) is used to evaluate these lesions, they are often imaged without intravenous contrast. This study hypothesised that the enhancement characteristics of the solid component of jaw tumors can aid in the differentiation of these lesions in addition to their morphology by dual-energy CT, therefore improving the ability to differentiate between various pathologies. To evaluate the role of contrast enhancement and dual-energy quantitative parameters in CT in the differentiation of jaw tumors. Fifty-seven patients with jaw tumors underwent contrast-enhanced dual-energy CT. Morphological analysis of the tumor, including the enhancing solid component, was done, followed by quantitative analysis of iodine concentration (IC), water concentration (WC), HU, and normalized IC. The study population was divided into four subgroups based on histopathological analysis-central giant cell granuloma (CGCG), ameloblastoma, odontogenic keratocyst (OKC), and other jaw tumors. A one-way ANOVA test for parametric variables and the Kruskal-Wallis test for non-parametric variables were used. If significant differences were found, a series of independent t-tests or Mann-Whitney U tests were used. Ameloblastoma was the most common pathology (n = 20), followed by CGCG (n = 11) and OKC. CGCG showed a higher mean concentration of all quantitative parameters than ameloblastomas (P < 0.05). An IC threshold of 31.35 × 100 μg/cm3 had the maximum sensitivity (81.8%) and specificity (65%). Between ameloblastomas and OKC, the former showed a higher mean concentration of all quantitative parameters (P < 0.001), however when comparing unilocular ameloblastomas with OKCs, the latter showed significantly higher WC. Also, ameloblastoma had a higher IC and lower WC compared to "other jaw tumors" group. Enhancement characteristics of solid components combined with dual-energy parameters offer a more precise way to differentiate between jaw tumors.

Multiplexed serum biomarkers to discriminate nonviable and ectopic pregnancy

ObjectiveThe use of multiplexed biomarkers may improve the diagnosis of normal and abnormal early pregnancies. In this study we assessed 24 markers with multiple machine learning-based methodologies to evaluate combinations of top candidates to develop a multiplexed prediction model for identification of 1) viability and 2) location of an early pregnancy. DesignA nested case-control design evaluating the predictive ability and discrimination of biomarkers in patients at risk of early pregnancy failure in the first trimester to classify viability and location Subjects218 individuals with a symptomatic (pain and/or bleeding) early pregnancy: 75 with an ongoing intrauterine gestation, 68 ectopic pregnancies, and 75 miscarriages. InterventionsSerum values of 24 biomarkers were assessed in the same patients. Multiple machine learning-based methodologies to evaluate combinations of these top candidates to develop a multiplexed prediction model for identification of 1) a nonviable pregnancy (ongoing intrauterine pregnancy vs miscarriage or ectopic pregnancy) and 2) an ectopic pregnancy (ectopic pregnancy vs ongoing intrauterine pregnancy or miscarriage). Main Outcome MeasuresThe predicted classification by each model was compared to actual diagnosis and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), conclusive classification, and accuracy were calculated. ResultsModels using classification regression tree analysis using three markers (PSG3, CG-Alpha and PAPPA) were able to predict a maximum sensitivity 93.3%, a maximum specificity 98.6%. The model with the highest accuracy was 97.4% (with 70.2% receiving classification). Models using an overlapping group of three markers (sFLT, PSG3 and TFP12) achieved a maximum sensitivity of 98.5%. and a maximum specificity of 95.3%. The model with the highest accuracy was 94.4% (with 65.6% receiving classification). When the models were used simultaneously the conclusive classification increased to 72.7% with an accuracy 95.9%. The predictive ability of the biomarkers random forest produced similar test characteristics when using 11 predictive markers. ConclusionWe have demonstrated a pool of biomarkers from divergent biological pathways that can be used to classify individuals with potential early pregnancy loss. The biomarkers CG-Alpha, PAPPA and PSG3 can be used to predict viability and sFLT, TPFI2 and PSG3 can be used to predict pregnancy location.

The Application of Optical Sensors with Built-in Anchor-like Cavities in the Detection of Hemoglobin Concentration

This paper introduces a refractive index sensor based on Fano resonance, utilizing a metal–insulator–metal (MIM) waveguide structure with an Anchor-like cavity. This study utilizes the finite element method (FEM) for analyzing the propagation characteristics of the structure. The evaluation concentrated on assessing how the refractive index and the structure’s geometric parameters affect its sensing characteristics. The designed structure demonstrates optimum performance, achieving a maximum sensitivity of 2440 nm/RIU and an FOM of 63. Given its high sensitivity, this nanoscale refractive index sensor is ideal for detecting hemoglobin concentrations in blood, and the sensor’s sensitivity is 0.6 nm·g/L, aiding in clinical prevention and treatment.

Diurnal variation of air pollutants and their relationship with land surface temperature in Bengaluru and Hyderabad cities of India

Air pollution and Land Surface Temperature (LST) are crucial components of urban environments. As urbanization and industrialization increase, the two components affect the natural environment of the urban areas. The present study investigated the daytime and nighttime spatiotemporal distribution of air pollutants and examined their relationship with LST for Bengaluru and Hyderabad cities of India. Daytime/nighttime/seasonal/spatial variations of air pollutants and LST, sensitivity of air pollutant in affecting LST, relationship between air pollutants and LST, and hotspots/coldspots analysis for LST and air pollutants was carried out. The study utilized, air pollutants data of 5-years from air quality monitoring stations and LST data of overlapping period from MODIS satellite. PM2.5 exceeded the national and international air quality standards while all other parameters were within specified limits/guideline values, at both the cities. The study found that LST had maximum sensitivity towards PM2.5 and NO2 for both the cities indicating that both these parameters are more effective in causing changes in the LST values. High Urban Thermal Field Variance Index (UTFVI) values for urban areas demonstrated strong surface urban heat island (SUHI) during nighttime in both the cities. Pearson's correlation analysis showed that LST, at Hyderabad, was positively correlated with PM2.5 (r = 0.714) during daytime. Relationship of LST with all air pollutants and meteorological parameters was investigated simultaneously and very strong relationship of LST with all other parameters was observed for both daytime and nighttime for both the cities. The R2 values at Bengaluru were 0.98 and 0.92 for nighttime and daytime, respectively, while the corresponding values were 0.87 and 0.93, respectively for Hyderabad. Hotspot analysis showed that total area of hotspots/coldspots undergoes temporal variations and the corresponding area during daytime was higher/lower compared to nighttime for different parameters. The trend of increase/decrease of daytime area of hotspots/coldspots, over nighttime area, was also different from different parameters as well as it was observed different for both the cities. Overall, the present study provides holistic insights into the interaction between air pollutants and LST in urban contexts. By understating these interactions and variations it helps urban planning and policymaking mitigate the challenge of the rise in LST and air pollutants.