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The Pulmonary Function Test (PFT) is a widely utilized and rigorous classification test for evaluating lung function, serving as a comprehensive diagnostic tool for lung conditions. Meanwhile, Electrical Impedance Tomography (EIT) is a rapidly advancing clinical technique that visualizes conductivity distribution induced by ventilation. EIT provides additional spatial and temporal information on lung ventilation beyond traditional PFT. However, relying solely on conventional isolated interpretations of PFT results and EIT images overlooks the continuous dynamic aspects of lung ventilation. This study aims to classify lung ventilation patterns by extracting spatial and temporal features from the 3D EIT image series. The study uses a Variational Autoencoder (VAE) with a MultiRes block to compress the spatial distribution in a 3D image into a one-dimensional vector. These vectors are then stacked to create a feature map for the exhibition of temporal features. A simple convolutional neural network is used for classification. Data from 137 subjects were utilized for the training phase. Initially, the model underwent validation through a leave-one-out cross-validation process. During this validation, the model achieved an accuracy and sensitivity of 0.96 and 1.00, respectively, with an f1-score of 0.98 when identifying the normal subjects. To assess pipeline reliability and feasibility, we tested it on 9 newly recruited subjects, with accurate ventilation mode predictions for 8 out of 9. In addition, we included 2D EIT results for comparison and conducted ablation experiments to validate the effectiveness of the VAE. The study demonstrates the potential of using image series for lung ventilation mode classification, providing a feasible method for patient prescreening and presenting an alternative form of PFT.

Microwave imaging is a promising method for early diagnosing and monitoring brain strokes. It is portable, non-invasive, and safe to the human body. Conventional techniques solve for unknown electrical properties represented as pixels or voxels, but often result in inadequate structural information and high computational costs. We propose to reconstruct the three dimensional (3D) electrical properties of the human brain in a feature space, where the unknowns are latent codes of a variational autoencoder (VAE). The decoder of the VAE, with prior knowledge of the brain, acts as a module of data inversion. The codes in the feature space are optimized by minimizing the misfit between measured and simulated data. A dataset of 3D heads characterized by permittivity and conductivity is constructed to train the VAE. Numerical examples show that our method increases structural similarity by 14% and speeds up the solution process by over 3 orders of magnitude using only 4.8% number of the unknowns compared to the voxel-based method. This high-resolution imaging of electrical properties leads to more accurate stroke diagnosis and offers new insights into the study of the human brain.

In perovskite solar cells (PSCs), the numerous defects present on the surface of the SnO2electron transport layer (ETL) and the bottom of the perovskite film limit their power conversion efficiency (PCE) and stability. In view of this, a bidirectional modification strategy is designed using formamidine acetate (FAAc) to passivate the defects on the SnO2ETL surface and bottom of the perovskite simultaneously. FA+cations act on the harmful hydroxyl groups on the SnO2ETL surface, whereas Ac–anions act on the iodine vacancy defect at the bottom of the perovskite. Because the interface defect is well passivated by FAAc, the interfacial charge recombination is restrained. This results in a significant increase in the filling factor of the PSC to ∼0.83 and the consequent increase in PCE to 23.05%, which considerably improves the stability. Bidirectional modification technology is an effective strategy for improving the PCE and stability of PSCs.

To investigate the value of drug exposure and host germline genetic factors in predicting apatinib (APA)-related toxicities. Method: In this prospective study, plasma APA concentrations were quantified using liquid chromatography with tandem mass spectrometry, and 57 germline mutations were genotyped in 126 advanced solid tumor patients receiving 250 mg daily APA, a vascular endothelial growth factor receptor II inhibitor. The correlation between drug exposure, genetic factors, and the toxicity profile was analyzed. Results: Non–small cell lung cancer (NSCLC) was more prone to APA-related toxicities and plasma concentrations of APA, and its main metabolite M1-1 could be associated with high-grade adverse events (AEs) (P< 0.01; M1-1, P< 0.01) and high-grade antiangiogenetic toxicities (APA, P= 0.034; P< 0.05), including hypertension, proteinuria, and hand-foot syndrome, in the subgroup of NSCLC. Besides, CYP2C9 rs34532201 TT carriers tended to have higher levels of APA (P< 0.001) and M1-1 (P< 0.01), whereas CYP2C9 rs1936968 GG carriers were predisposed to higher levels of M1-1 (P< 0.01). Conclusion: Plasma APA and M1-1 exposures were able to predict severe AEs in NSCLC patients. Dose optimization and drug exposure monitoring might need consideration in NSCLC patients with CYP2C9 rs34532201 TT and rs1936968 GG.SIGNIFICANCE STATEMENTApatinib is an anti-VEGFR2 inhibitor for the treatment of multiple cancers. Though substantial in response, apatinib-induced toxicity has been a critical issue that is worth clinical surveillance. Few data on the role of drug exposure and genetic factors in apatinib-induced toxicity are available. Our study demonstrated a distinct drug-exposure relationship in NSCLC but not other tumors and provided invaluable evidence of drug exposure levels and single nucleotide polymorphisms as predictive biomarkers in apatinib-induced severe toxicities.

In this communication, we propose a new physics-constrained approach to solve 2-D inverse scattering problems (ISPs) by extending physics-informed supervised residual learning (PhiSRL) with Born approximation (BA). By embedding the fixed-point iteration method in residual neural network (ResNet), PhiSRL aims to solve ISPs iteratively by applying the convolutional neural networks (CNNs) to learn the update rules of reconstructions. PhiSRL is employed to invert lossy scatterers by introducing BA to linearize ISPs and further reduce the computational burden of forward modeling. Both numerical and experimental results validate the effectiveness of the proposed approach.

In this article, we propose the neural Born iterative method (NeuralBIM) for solving 2-D inverse scattering problems (ISPs) by drawing on the scheme of the physics-informed supervised residual learning (PhiSRL) to emulate the computing process of the traditional Born iterative method (TBIM). NeuralBIM uses independent convolutional neural networks (CNNs) to learn the alternate update rules of two different candidate solutions regarding the residuals. Two different schemes are presented in this article, including the supervised and unsupervised learning schemes. With the dataset generated by the method of moments (MoM), supervised NeuralBIM is trained with the knowledge of the total fields and contrasts. Unsupervised NeuralBIM is guided by the physics-embedded objective function founding on the governing equations of ISPs, which results in no requirement of the total fields and contrasts for training. Numerical and experimental results further validate the efficacy of NeuralBIM.

The time-difference image reconstruction problem of electrical impedance tomography (EIT) refers to reconstructing the conductivity change in a human body part between two time points using the boundary impedance measurements. Conventionally, the problem can be formulated as a linear inverse problem. However, due to the physical property of the forward process, the inverse problem is seriously ill-posed. As a result, traditional regularized least-squares (LSs)-based methods usually produce low-resolution images that are difficult to interpret. This work proposes a framework that uses a deep generative model (DGM) to constrain the unknown conductivity. Specifically, this framework allows the inclusion of a constraint that describes a mathematical relationship between the generative model and the unknown conductivity. The resultant constrained minimization problem is solved using an extended alternating direction method of multipliers (ADMM). The effectiveness of the framework is demonstrated by the example of 3-D time-difference chest EIT imaging. Numerical experiment shows a significant improvement of the image quality compared with total variation (TV)-regularized LSs method (peak signal-to-noise ratio (PSNR) is improved by 4.3% for 10% noise and 4.6% for 30% noise; structural similarity (SSIM) is improved by 4.8% for 10% noise and 6.0% for 30% noise). Human experiments show improved correlation between the reconstructed images and images from reference techniques.

In this work, we present a joint inversion framework that enables simultaneous reconstruction of electrical impedance tomography (EIT), microwave tomography (MWT), and ultrasound tomography (UST). The structural and value features of the three modality images are decoupled and encoded into structure code and value code by the proposed disentangled variational autoencoder (DVAE). The inversion is then performed at the feature level, with the structure code and value code treated as unknown parameters to be inverted by the Gauss-Newton method. The interaction among the three modalities is achieved through structure code fusion, where the structure codes of the three modalities are averaged in the inversion as the fused structure code, thus ensuring the consistency of the structures in the three modalities. Two schemes are proposed to utilize the fused structure code. The initial code scheme (ICS) employs it as the initial structure code for the Gauss-Newton update of each modality. Meanwhile, the reference code scheme (RCS) designates it as the reference structure code. Numerical examples of human thorax imaging verify the effectiveness of the two schemes and demonstrate the advantages of the proposed joint inversion framework over separate inversions.

The electron transport layer (ETL) of SnO2with a rough surface, low conductivity, and poor wettability has limited its application in perovskite solar cells (PSCs). To address this issue, we propose a strategy that involves the simultaneous use of bulk dopant NaPF6and polymer stabilizer NH2-PEG-NH2in SnO2. NH2-PEG-NH2is compatible with both SnO2and NaPF6, resulting in a homogeneous distribution. Additionally, the intrinsic hydrophilicity of the polymer facilitates the formation of a continuous and ordered ETL with improved wettability. The inclusion of NaPF6as a bulk dopant enhances conductivity and promotes upper perovskite growth. As a result, optimized morphology, aligned energy levels, improved crystallinity, and reduced bottom defects are achieved in the fabricated perovskite layer. The champion device exhibits a power conversion efficiency (PCE) of ∼23.36%, which is ∼11.88% higher than that of the pristine device (PCE = 20.88%). Notably, the VOCreaches ∼1.2 V with only ∼0.08 V of VOCloss, which is among the highest report one. Furthermore, the PCE of the modified unpackaged PSC was only attenuated by 25% after 250 h of maximum power point tracking in the environment. These results present an alternative and effective approach for preparing high-quality SnO2ETL for efficient PSCs.

Metal-N4(M-N4) macrocyclic complexes are interesting electrocatalysts due to their well-defined structures and rich molecular tuning. Among them, metal phthalocyanines have been widely studied for the carbon dioxide reduction reaction (CO2RR) in heterogeneous systems and demonstrated good electrocatalytic performance. However, other complexes like metal corroles and metal porphyrins are much less explored, and often show inferior performances. In this study, three cobalt macrocyclic complexes, cobalt phthalocyanine, cobalt meso-tetraphenylporphyrin, and cobalt meso-triphenylcorrole (CoPc, CoTPP and CoTPC) are investigated in heterogeneous electrocatalysis of CO2RR. Although CoPc/carbon nanotube (CNT) hybrid exhibits high electrocatalytic activity, CNT hybridization does not work for CoTPC and CoTPP that hold weak interactions with CNTs. By the drop-dry method with a high molecular loading of 5.4 × 10–7mol cm–2, CoTPC and CoTPP could deliver appreciable electrode activities. Poly(4-vinylpyridine) (PVP) introduction is further demonstrated as a facile method to afford enhanced activities for CoTPP at low molecular loadings through enhancing molecule-substrate interactions. The partial current density of carbon monoxide for CoTPP+CNT/PVP is around 8 times higher than the sample without PVP at –0.67 V versus reversible hydrogen electrode. This work provides solutions to enhance the electrode activities of molecular electrocatalysts with weak substrate interactions in heterogeneous systems.

Single-atom catalysts (SACs) with atomically dispersed metal sites in nitrogen-doped carbon matrices (M–N/C) have been identified as promising candidates for the electrocatalytic CO2reduction reaction (CO2RR). However, recent studies aiming at economic viability have been inhibited by the low faradaic efficiency (FE) and instability under high current density. Herein, we report a series of SACs derived from cyano-substituted metal phthalocyanines (MePc-CN) in ZIFs (denoted as Me-SACs (Pc)). These phthalocyanine molecules enable the efficient construction of SACs, affording higher metal loading and less variation when compared with their counterparts from metal nitrates (denoted as Me-SACs (S)). Thus, Me-SACs (Pc) exhibit higher activities and selectivities than Me-SACs (S) in H-cell measurements. In gas-diffusion electrode (GDE) setups, the unstable Fe-SAC (Pc) shows only a 50% FE of CO (FEco) at −100 mA cm–2. In contrast, Ni-SAC (Pc) exhibits a higher FEco of >96% at current densities from −10 to −200 mA cm–2and can stably operate for over 16 h at −200 mA cm–2. The performances of Ni-SAC (Pc) are comparable to those of precious metal catalysts and the best SACs reported so far, representing a promising candidate for practical electrolyzer devices for CO2RR.

Nao-Xin-Qing (NXQ) tablets are standardized proprietary herbal products containing an extract of Chinese persimmon leaves (Diospyros kakiL.f., World Checklist) and other natural ingredients. NXQ has also been indicated for atherosclerosis (AS), but the mechanisms of its antiatherosclerotic activity are unclear. In this study, its mechanisms were investigated by using preclinical models, and provide evidence for its potential application against cardiovascular disorders.

Polymer solar cells (PSCs) processed from non-halogenated solvents are favorable for large scale production. However, the photovoltaic performance of devices from non-halogenated solvents is generally inferior to that of the counterparts from halogenated solvents. Herein, we report the utilization of 5-alkyl-4-(methylthio)thiophene (MTT) as a conjugated side chain of the polymeric donor to achieve efficient PSCs processed from toluene. The derived polymer PMTT56 exhibits lower energy levels than the 5-alkyl-4-methoxythiophene (MOT) counterpart (PMOT39) and 5-(alkylthio)thiophene counterpart (PEHTT). PMTT56 is superior to PMOT39 with an enhanced interchain interaction and hole mobility, and also shows better solubility than PEHTT in toluene. In toluene-processed PSCs, the PMTT56 devices deliver a power conversion efficiency (PCE) of up to 12.6% with IT-2F as the acceptor. By introducing [6,6]-phenyl C71-butyric acid methyl ester (PC71BM) as the second acceptor, a remarkable PCE of 13.2% is further achieved in the ternary devices, which is among the best for PSCs processed from toluene. Our study presents a fine-tuning approach to develop efficient organic photovoltaic materials processed from non-halogenated solvents.

Defect passivation plays a critical role in manufacturing high-performance perovskite solar cells (PSCs). Herein, engineered components of metformin halides are introduced to the bulk and surface of perovskite layers in PSCs to realize synergistic defect passivation. It is found that the bulk addition of metformin hydrochloride influences the orientation distribution of perovskite crystals and contributes to better perovskite films. Further, the modification of metformin iodide could treat residual defects on the surface of perovskite films. As a result, the synergistically passivated PSCs shows an improved power conversion efficiency (PCE), which increased from 19.36% to 22.17%, together with a higher short-circuit current and open-circuit voltage than those of the control device. Moreover, the as-treated PSCs exhibit excellent thermal and humidity stabilities, maintaining 95% of their initial PCE after being stored under air conditions for over 1000 h. This work provides a novel strategy to passivate perovskite defects and improve the PCE and stability of PSCs.

Cathode interface layers (CILs) are important for electron extraction in polymer solar cells (PSCs). Currently, the thickness of CILs is often below 15 nm due to their low electron mobility, which is not favorable for large-scale fabrication. Herein, we report a thick CIL for efficient PSCs by modifying the ZnO nanocrystals (NCs) film with perylene diimides functionalized with amino oxide (PDINO). The combined ZnO NCs/PDINO CIL inherits the high electron mobility of ZnO NCs and dense morphology of PDINO, affording higher power conversion efficiencies (PCEs) of its devices than the sole component controls. The PSCs with the ZnO NCs/PDINO CIL also exhibit good tolerance to the CIL thickness, and the PM6:Y6 and PM6:BTP-eC9 devices can achieve high PCEs of over 15% at the CIL thickness of 70 nm. Further, the ZnO NCs/PDINO devices show better stability than those with sole ZnO NCs or PDINO. Our results provide a new way to construct potential CILs for high performance PSCs.

In 1977, the Low-level Working Group of the International Committee on Radionuclide Metrology met in Boston, MA (USA) to define the characteristics of a new set of environmental radioactivity reference materials. These reference materials were to provide the radiochemist with the same analytical challenges faced when assaying environmental samples. It was decided that radionuclide bearing natural materials should be collected from sites where there had been sufficient time for natural processes to redistribute the various chemically different species of the radionuclides. Over the succeeding years, the National Institute of Standards and Technology (NIST), in cooperation with other highly experienced laboratories, certified and issued a number of these as low-level radioactivity Standard Reference Materials (SRMs) for fission and activation product and actinide concentrations. The experience of certifying these SRMs has given NIST the opportunity to compare radioanalytical methods and learn of their limitations. NIST convened an international workshop in 1994 to define the natural-matrix radionuclide SRM needs for ocean studies. The highest priorities proposed at the workshop were for sediment, shellfish, seaweed, fish flesh and water matrix SRMs certified for mBq per sample concentrations of 90Sr, 137Cs and 239Pu + 240Pu. The most recent low-level environmental radionuclide SRM issued by NIST, Ocean Sediment (SRM 4357) has certified and uncertified values for the following 22 radionuclides: 40K, 90Sr, 129I, 137Cs, 155Eu, 210Pb, 210Po, 212Pb, 214Bi, 226Ra, 228Ra, 228Th, 230Th, 232Th, 234U, 235U, 237Np, 238U, 238Pu, 239Pu + 240Pu, and 241Am. The uncertainties for a number of the certified radionuclides are non-symmetrical and relatively large because of the non-normal distribution of reported values. NIST is continuing its efforts to provide the ocean studies community with additional natural matrix radionuclide SRMs. The freeze-dried shellfish flesh matrix has been prepared and recently sent to participating laboratories for analysis and we anticipate receiving radioanalytical results in 2000. The research and development work at NIST produce well characterized SRMs that provide the world's environment-studies community with an important foundation component for radionuclide metrology.

One of the most critical elements of a performance evaluation (PE) program for radioactivity measurements is the traceability of the PE materials to the national standards. The requirements and criteria for the production of traceable environmental and radiobioassay PE materials have been defined by ANSI N42.22 and ANSI N13.30 standards. It is important to note that use of traceable source materials does not necessarily ensure the traceability of subsequently derived PE materials unless verification measurements exist in conjunction with the preparation processes. This paper describes the protocol currently used by NIST for the preparation and verification of air filter, acidified water, spiked soil, synthetic urine, and synthetic fecal PE materials for low-level radioactivity measurements. The process involves gravimetric dilutions and mixing of primary radionuclide NIST Standard Reference Materials (SRMs), addition of the derived master solution to sample matrices, and subsequent verification measurements. Several gamma-emitters were used to trace the gravimetric dilutions and spike addition through an unbroken chain of gamma comparison measurements. The massic activities of alpha- and beta-emitters in the diluted solutions and PE samples were also measured by radiochemical methods and compared with their gravimetric values. A correlation analysis demonstrated that the gamma emitters quantitatively followed 90Sr, 238U, 238Pu, and 241Am throughout the dilution and spiking and can be used as effective process monitors. The statistical results from t-tests, box plots, and normal probability tests suggested that traceability of radionuclides in the PE materials to their primary standards can be verified to within 1%, with an overall precision better than 2% (1s).

Interlaboratory comparisons are an essential component of method validation, laboratory proficiency and equivalency evaluation, and standard reference material certification. The reliability of the results from interlaboratory comparison largely depends upon analyzing the data with appropriate statistical methods. Intercomparison data are often found to be non-normally distributed as a result of sample heterogeneity, uneven laboratory variance, and methodology bias. The statistical methods that require data normality are inappropriate for analyzing such skewed data. Instead of using data transformation or non-parametric methods, an alternative method which sets aside the assumption of data normality is utilized in the certification of new National Institute of Standards and Technology (NIST) Ocean Sediment Standard Reference Material (SRM-4357). It first determines the best-fit distribution for the data, then calculates appropriate distributional mean and tolerance limits, and finally estimates the uncertainties for the derived statistics by bootstrap analysis. The method was applied to evaluate the data from the SRM intercomparison, and revealed homogeneity for natural radionuclides and inhomogeneity for anthropogenic radionuclides in the SRM. It was found that the data of natural radionuclides in the SRM are normally distributed. In contrast, the data of anthropogenic radionuclides, such as 90Sr, 137Cs, 238Pu, and (239+240)Pu, are best fitted by Weibull distributions. The difference on data distributions for the two types of radionuclides is believed to be related to "hot" particles in the SRM. By determining the best-fit data distribution and applying bootstrap analysis, more reliable and realistic massic activity (Bq·kg-1) for anthropogenic radionuclides were certified in comparison to those obtained using data transformation and nonparametric methods. Although the developed method is computationally intensive, it provides a straightforward, rigorous procedure for computing the statistics that would otherwise be difficult to obtain when the data distribution form is complicated. With the help of advanced personal computers and use of the DataPlot software from NIST, this method becomes very practical and has enhanced interlaboratory comparison data studies and certification of the NIST standard reference materials.

Glucosamine hydrochloride (GAH), one of the most basic and important derivatives of chitin, is obtained by hydrolysis of chitin in concentrated hydrochloric acid. At present, little is known about how GAH functions in skeletal development. In this report, we demonstrate that GAH, extracted from the cell wall of Agaricus bisporus, acts in a dose-dependent manner to promote not only cartilage and bone development in larvae but also caudal fin regeneration in adult fish. Furthermore, GAH treatment causes a significant increase in expression of bone-related marker genes, indicating its important role in promoting skeletal development. We show that in both larval and adult osteoporosis models induced by high iron osteogenic defects are significantly ameliorated after treatment with GAH, which regulates expression of a series of bone-related genes. Finally, we demonstrate that GAH promotes skeletal development and injury repair through bone morphogenetic protein (Bmp) signaling, and it works at the downstream of the receptor level. Taken together, our findings not only provide a strong research foundation and strategy for the screening of natural osteoporosis drugs and product development using a zebrafish model but also establish the potential for the development of Agaricus bisporus-derived GAH as a new drug for osteoporosis treatment.

Objectives: Lung cancer (LC) is often accompanied by significant methylation abnormalities. This study aimed to develop a decision tree (DT) accompanied the stature homeobox 2 gene (SHOX2) / prostaglandin E receptor 4 (PTGER4) gene DNA methylation with traditional tumor marker in the differential diagnosis of benign and malignant lung nodule.Methods: We performed a study with 104 patients enrolled in the LC group and 36 patients in the benign lung diseases group. All the clinical data of these patients were collected through electronic medical record. Total Methylation (TM) status of both SHOX2 and PTGER4 was defined as methylation levels of SHOX2 plus methylation levels of PTGER4. One-way analysis was used to compare the concentrations of serum samples and t-test was used to compare pairwise mean values between groups. Receiver operating curve (ROC) was used to evaluate the diagnostic value. Furthermore, the strategy was validated in 19 LC patients and 11 patients with benign lung diseases.Results: There were significant differences between the concentration of neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), cytokeratin 19 fragments (CYFRA21 -1) and the methylation levels of SHOX2, PTGER4 and TM in lung benign diseases and cancer group. The AUCs of NSE, CEA, CYFRA21 -1, Methylation SHOX2, Methylation PTGER4 and TM were 0.721 (95% CI: 0.627–0.816), 0.753 (95% CI: 0.673–0.833) and 0.778(95% CI: 0.700–0.856), 0.851(0.786-0.916), 0.847(0.780-0.913) and 0.861(0.800-0.922) respectively. We developed a DT model with TM and CYFRA21 -1 used in this study, and the area under the curve (AUC) of DT was 0.921 and the sensitivity up to 0.856. In the validation cohort, the AUC of SHOX2, PTGER4 and TM was also much higher than traditional serum markers.Conclusions: Our results indicated that the DT model calculated from the TM and CYFRA21 -1 can accurately classify LC and benign diseases, which showed better diagnostic performance than traditional serum parameter.

A transient response of nuclear spins in GaAs to a change in a magnetic field was analyzed based on the time evolution of nuclear spin temperature. Simulation results well reproduced our experimental results on transient oblique Hanle signals observed in an all-electrical spin injection device, enabling a quantitative understanding of nuclear spin dynamics in the presence of a hyperfine interaction between nuclei and polarized electrons. Analysis regarding the time evolution of nuclear spin temperature revealed that the hysteretic nature of a nuclear field with respect to the sweep direction of an external field was caused by the delay of time response of nuclear spin temperature to a change in the magnetic field. This analysis provides a deep understanding of nuclear spin dynamics in semiconductors.