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Abstract Background The optimal timing of performing ICSI on immature oocytes for POSEIDON patients is still unknown to get better early embryonic development outcomes. The purpose of this study was to implore the most appropriate time to carry out ICSI on in vitro maturation GV and MI oocytes for POSEIDON patients. Methods Two hundred thirty-nine immature oocytes from 163 POSEIDON patients were prospectively performed ICSI at different timings: P-ICSI (ICSI was performed on in vitro matured oocytes 4–6 h after the first polar body extrusion, N = 81), R-ICSI (ICSI was performed on in vitro matured oocytes less than 4 h after the first polar body extrusion, N = 80), and E-ICSI (ICSI was performed on in vitro matured oocytes the next day after oocytes retrieval, N = 78). Fertilization and embryonic development outcomes were collected and statistically analyzed. Mitochondria distribution of cytoplasm of in vitro matured oocytes with different time cultures after the first polar body (PB1) extrusion was stained. Results Compared to the E-ICSI group, more day 3 embryos from P-ICSI became blastocysts after sequential culture though without statistical significance (OR = 3.71, 95% CI: 0.94—14.63, P = 0.061). Compared to the E-ICSI group, more embryos from both P-ICSI and R-ICSI groups were clinically used with statistical significance (OR = 5.67, 95% CI: 2.24—14.35, P = 0.000 for P-ICSI embryos; OR = 3.23, 95% CI: 1.23—8.45, P = 0.017 for R-ICSI embryos). Compared to the E-ICSI group, transferred embryos from P-ICSI and R-ICSI had a higher implantation rate though without statistical significance (35.3% for P-ICSI embryos; 9.1% or R-ICSI embryos and 0% for E-ICSI embryos, P = 0.050). Among the three group, there were most healthy babies delivered from the P-ICSI group (5, 1 and 0 for P-ICSI, R-ICSI and E-ICSI respectively). The mitochondria in the cytoplasm of in vitro matured oocytes with a less than 4 h and 4–6 h culture after PB1 extrusion presented semiperipheral and diffused distribution patterns, respectively. Conclusions Our results revealed P-ICSI (ICSI was performed on in vitro matured oocytes 4–6 h after the first polar body extrusion) provided the most efficient method to utilize the immaturation oocytes basing on embryos utilization and live birth outcome for low prognosis patients under the POSEIDON classification. The mitochondria distribution of the in vitro matured oocytes’ cytoplasm from P-ICSI varied that from R-ICSI.

Abstract Background The decline in the quantity and quality of mitochondria are closely associated with infertility, particularly in advanced maternal age. Transferring autologous mitochondria into the oocytes of infertile females represents an innovative and viable strategy for treating infertility, with no concerns regarding ethical considerations. As the donor cells of mitochondria, stem cells have biological advantages but research and evidence in this area are quite scarce. Methods To screen out suitable human autologous ooplasmic mitochondrial donor cells, we performed comprehensive assessment of mitochondrial physiology, function and metabolic capacity on a varity of autologous adipose, marrow, and urine-derived mesenchymal stromal cells (ADSC, BMSC and USC) and ovarian germline granulosa cells (GC). Further, to explore the biosafety, effect and mechanism of stem cell-derived mitochondria transfer on human early embryo development, randomized in-vitro basic studies were performed in both of the young and aged oocytes from infertile females. Results Compared with other types of mesenchymal stromal cells, USC demonstrated a non-fused spherical mitochondrial morphology and low oxidative stress status which resembled the oocyte stage. Moreover, USC mitochondrial content, activity and function were all higher than other cell types and less affected by age, and it also exhibited a biphasic metabolic pattern similar to the pre-implantation stage of embryonic development. After the biosafety identification of the USC mitochondrial genome, early embryos after USC mitochondrial transfer showed improvements in mitochondrial content, activity, and cytoplasmic Ca2+ levels. Further, aging embryos also showed improvements in embryonic morphological indicators, euploidy rates, and oxidative stress status. Conclusion Autologous non-invasively derived USC mitochondria transfer may be an effective strategy to improve embryonic development and metabolism, especially in infertile females with advanced age or repeated pregnancy failure. It provides evidence and possibility for the autologous treatment of infertile females without invasive and ethical concerns.

AIM: To assess the long-term efficacy and safety of yttrium-aluminum garnet (YAG) laser vitreolysis for vision degrading myodesopsia (VDM) caused by posterior vitreous detachment (PVD). METHODS: This retrospective study reviewed VDM patients of PVD type undergoing YAG laser vitreolysis. The baseline demographic information, the patterns of floaters, the number of floaters, and the subjective improvement of floater sympotoms (ranging from 0 to 100%) from medical records were collected. Significant improvement was defined as a relief of floater symptoms of ≥50% at the final visit. The long-term efficacy and safety of YAG laser vitreolysis were analyzed. The risk factors linked to significant improvement of floater symptoms were defined using univariate and multivariate logistic regression analyses. RESULTS: The final analysis included 221 patients with VDM. The mean age of patients was 61.08±7.74y, and the mean length of follow-up was 21.38±5.61mo. Totally 57.01% of patients experienced a significant improvement in their floater symptoms after YAG laser therapy, and none of them developed delayed retinal abnormalities such as retinal tears or detachments. Age (OR=1.049, 95%CI=1.007-1.092, P=0.021) was identified as a significant risk factor for significant improvement in VDM. CONCLUSION: YAG laser vitreolysis is an effective and secure treatment for PVD-type VDM, and patients of advanced age are more likely to get favorable outcomes.

Abstract Background To investigate the diagnostic sensitivity of Optos imaging for vision degrading myodesopsia (VDM). Methods A total of 420 eyes from 345 patients with VDM were collected in this cross-sectional study. All eyes were classified as having posterior vitreous detachment (PVD) or not having PVD. The sensitivity of Optos imaging for the visibility of vitreous floaters was evaluated. The associated factors with the visibility of vitreous floaters on Optos images were analyzed in univariate and multivariate logistic regression analyses. Results The mean age of all patients was 56.19 ± 13.89 years old, and 66.67% of patients were female. The vitreous floaters were visible on the ultrasound B scan in all eyes, but only in 47.62% of Optos images (55.29% in eyes with PVD and 15% in eyes without PVD). In the multiple binary logistic regression analysis, age (OR = 1.094, 95%CI = 1.063–1.125, P

Abstract Background We aimed to develop an accurate model to predict live birth for patients receiving in vitro fertilization and embryo transfer (IVF-ET) treatment. Methods This is a prospective nested case–control study. Women aged between 18 and 38 years, whose body mass index (BMI) were between the range of 18.5–24 kg/m2, who had an endometrium of ≥ 8 mm at the thickest were enrolled from 2018/9 to 2020/8. All patients received IVF-ET treatment and were followed up until Jan. 2022 when they had reproductive outcomes. Endometrial samples during the window of implantation (LH + 6 to 9 days) were subjected to analyze specific endometrial receptivity genes’ expression using real-time PCR (RT-PCR). Patients were divided into live birth group and non-live birth group based on IVF-ET outcomes. Clinical signatures relevant to live birth were collected, analyzed, and used to establish a predictive model for live birth by univariate analysis (clinical model). Specific endometrial receptivity genes’ expression was analyzed, selected, and used to construct a predictive model for live birth by The Least Absolute Shrinkage and Selection Operator (LASSO) analysis (gene model). Finally, significant clinical factors and genes were used to construct a combined model for predicting live birth using multivariate logistical regression (combined model). Different models’ Area Under Curve (AUC) were compared to identify the most predictive model. Results Thirty-nine patients were enrolled in the study, twenty-four patients had live births, fifteen did not. In univariate analysis, the odds of live birth for women with ovulation dysfunction was 4 times higher than that for women with other IVF-ET indications (OR = 4.0, 95% CI: 1.125 − 8.910, P = 0.018). Age, body mass index, duration of infertility, primary infertility, repeated implantation failure, antral follicle counting, ovarian sensitivity index, anti-Mullerian hormone, controlled ovarian hyperstimulation protocol and duration, total dose of FSH/hMG, number of oocytes retrieved, regiment of endometrial preparation, endometrium thickness before embryo transfer, type of embryo transferred were not associated with live birth (P > 0.05). Only ovulation dysfunction was used to construct the clinical model and its AUC was 0.688. In lasso analysis, GAST, GPX3, THBS2 were found to promote the risk of live birth. AUCs for GAST, GPX3, THBS2 reached to 0.736, 0.672, and 0.678, respectively. The gene model was established based on these three genes and its AUC was 0.772. Ovulation dysfunction, GAST, GPX3, and THBS2 were finally used to construct the combined model, reaching the highest AUC (AUC = 0.842). Conclusions Compared to the single model, the combined model of clinical (Ovulation dysfunction) and specific genes (GAST, GPX3, THBS2) was more accurate to predict live birth for IVF-ET patients.

Abstract Our society’s appetite for ultra-high bandwidth communication networks and high-power optical sources, together with recent breakthroughs in mode multiplexing/demultiplexing schemes, forced the photonics community to reconsider the deployment of nonlinear multimode systems. These developments pose fundamental challenges stemming from the complexity of nonlinear mode-mode mixing by which they exchange energy in the process towards an equilibrium Rayleigh-Jeans (RJ) distribution. Here we develop a universal one-parameter scaling theory for the relaxation rates of out-of-equilibrium excitations towards their RJ thermal state. The theory predicts an exponential suppression of the rates with increasing disorder due to the formation of stable localization clusters resisting the nonlinear mode-mode interactions that tend to separate them. For low optical temperatures, the rates experience a crossover from linear to nonlinear temperature dependence which reflects a disorder-induced reorganization of the low frequency eigenmodes. Our theory will guide the design of nonlinear multimode photonic networks with tailored relaxation-scales.

Abstract Background Metabolites in spent embryo culture medium correlate with the embryo’s viability. However, there is no widely accepted method using metabolite dada to predict successful implantation. We sought to combine metabolomic profiling of spent embryo culture medium and clinical variables to create an implantation prediction model as an adjunct to morphological screening of day 3 embryos. Methods This investigation was a prospective, nested case-control study. Forty-two day 3 embryos from 34 patients were transferred, and the spent embryo culture medium was collected. Twenty-two embryos implanted successfully, and the others failed. Metabolites in the medium relevant to implantation were detected and measured by Liquid Chromatography-Mass Spectrometry. Clinical signatures relevant to embryo implantation were subjected to univariate analysis to select candidates for a prediction model. Multivariate logistical regression of the clinical and metabolomic candidates was used to construct a prediction model for embryo implantation potential. Results The levels of 13 metabolites were significantly different between the successful and failed groups, among which five were most relevant and interpretable selected by Least Absolute Shrinkage and Selection Operator regression analysis. None of the clinical variables significantly affected day 3 embryo implantation. The most relevant and interpretable set of metabolites was used to construct a prediction model for day 3 embryo implantation potential with an accuracy of 0.88. Conclusions Day 3 embryos’implantation potential could be noninvasively predicted by the spent embryo culture medium’s metabolites measured by LC-MS. This approach may become a useful adjunct to morphological evaluation of day 3 embryos.

Abstract Under high ambient pressure (5 MPa) and different injection pressures (90, 120, and 150 MPa), a high‐speed imaging technique was carried out to comparatively investigate the macroscopic spray characteristics of diesel with three different types of blended fuel in a constant volume chamber. The oxygenated fuels were n‐butanol (B), pine oil (P), and 2,5‐dimethylfuran (DMF). All their blending ratio with diesel were 20%. Results showed that less viscosity could be improved the spray characteristics of the fuel in the range of experimental conditions. Then, the tested fuels had a longer penetration and a greater spray area with increasing the injection pressure from 90 to 150 MPa. On the other hand, the percentage increases in the mean spray cone angle of D100, B20, P20, and DMF20 were 3%, 4.4%, 2.4%, and 2.9%, respectively. At the same experimental condition, the spray penetrations of DMF20 and P20 were larger than that of D100, but the spray penetration of B20 was basically similar to D100. Besides, the performance of the spray cone angle and spray area were D100

AIM: To investigate the annual eye examination of diabetic patients in Shenyang and analyze the associated factors by using the Chinese version of the Compliance with Annual Diabetic Eye Exam Survey(CADEES)questionnaire.METHODS: This is a cross-sectional study. The diabetic patients who first visited Shenyang He Eye Specialist Hospital from November 2021 to October 2022 were collected. The Chinese version of the CADEES questionnaire was used to investigate the previous annual eye examinations of these patients, and no fundus examination in the past 1a was defined as poor participation. Factors associated with poor participation in annual eye examinations were identified by multivariate binary logistic regression analyses.RESULTS: A total of 468 patients were collected, including 192 males and 276 females, with an average age of 67.42±10.66 years. There were 330 patients without diabetic retinopathy(DR)and 138 patients with DR, among which 88 patients had vision-threatening diabetic retinopathy(VTDR)and 50 patients had non-vision-threatening diabetic retinopathy(NVTDR). 34.2%(160 cases)of patients had poor annual eye examination. The multivariate logistic regression analysis showed that, after controlling gender, age and other influencing factors, patients in rural area(OR=1.704, 95%CI: 1.019～2.850, P=0.042), VTDR(OR=1.948, 95%CI: 1.145～3.313, P=0.014), the item 7(I have felt blue, downhearted, or depressed over the past 4wk; OR=0.624, 95%CI: 0.401～0.971, P=0.037)and item 42(I receive a reminder from my eye doctor's office when it is time to schedule an exam; OR=0.618, 95%CI: 0.387～0.989, P=0.045)of CADEES questionnaire were risk factors for poor participation in the annual ophthalmic examination.CONCLUSIONS: Approximately one-third of diabetic patients in the Shenyang region may not follow the guidelines for DR annual eye examination. Healthcare providers should improve DR health education, increasing eye exam participation through necessary reminders.

Traffic flow prediction is a key step to realizing the effective guidance and control of intelligent transportation systems. For using the short-term non-stationarity and spatio-temporal correlation presented in traffic flow, a spatio-temporal hybrid prediction model, which called ST-VGBiGRU, based on improved Variational Modal Decomposition (VMD), Graph Attention Network (GAT), and Bidirectional Gated Recurrent Unit (BiGRU) network is proposed. First, the traffic flow sequence is decomposed into a series of relatively stationary modal components using VMD algorithm to reduce its short-term non-stationarity. The high-frequency modal components are noise-reduced using the Fuzzy Entropy (Fuzzy En) method to improve the accuracy of decomposition. After that, the GAT network is used to capture the different attention levels of the prediction node to their neighboring traffic nodes, which obtain more spatial characteristics of traffic flow. Then, each modal component containing spatial features is fed into the BiGRU network separately to capture its temporal correlation. Each model parameter is trained to the optimum using the improved RMSProp algorithm, which improves the model’s prediction accuracy while speeding up the convergence of RMSProp algorithm. In order to illustrate the performance of the ST-VGBiGRU model, the RTMC traffic dataset is used to conduct ablation experiments on the improved VMD module, the GAT module, and the improved BiGRU module. Meanwhile, we combined the PeMS traffic dataset to conduct baseline experiments and multi-step prediction experiments with the other six models. The results show that the prediction performance of our model is better than all the other baseline models.

In this study, tin nanoparticles were green synthesized using the aqueous extract of Calendula officinalis. Different techniques such as FE-SEM, XRD, FT-IR, and EDS analysis were used to characterize SnNPs@ C. officinalis. A 21.87 nm was obtained for the crystal size of the tin nanoparticles using XRD analysis. The FE-SEM images show a spherical morphology for SnNPs@ C. officinalis with a range size of 21.63–64.07 for the synthetic nanoparticles. The anti-breast cancer effects of biologically synthesized SnNPs@ C. officinalis against breast cancer cell lines were assessed. The anti-breast cancer properties of the SnNPs@ C. officinalis could significantly remove the MCF7, Hs 319.T, and MCF10 cancer cell lines in a time and concentration-dependent manner by MTT assay. The antioxidant activity of SnNPs@ C. officinalis was determined by the DPPH method. The SnNPs@ C. officinalis showed the highest antioxidant activity according to the IC50 value. It seems the anti-human breast cancer effect of recent nanoparticles is due to their antioxidant effects.

In order to promote the catalytic activity of starch-based biomimetic glutathione peroxidase (GPx), the selenized starch (SCS) and cationic starch (CCS) prepared from cassava starch (CS) were mixed, resulting in the targeted starch (SCS/CCS) with both the catalytic center and the recognition site of substrate. First, CS was modified with octenyl succinic anhydride and sodium selenide hydride, respectively, to prepare SCS. The CCS was synthesized by modifying CS with 3-chloro-2-hydroxypropyltrimethylammonium chloride. Some technologies including nuclear magnetic resonance (1H NMR), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermal analysis (TG) and scanning electron microscopy (SEM) were used to characterize the samples. Finally, the effect of the mixture of SCS and CCS on the catalytic activity of GPx was investigated by changing the molar ration of nitrogen and selenium. Results showed that the 1H NMR spectrum of SCS with selenium content of 11.35 μg/g revealed some new peaks (0.6~2.5 ppm) attributed to protons of the octenyl succinate compared with CS. In comparison to the FT-IR spectrum of CS, SCS showed lower intensities in the characteristic peaks of hydroxyl (3300 cm−1) and bound water (1639 cm−1), indicating the consumption of hydroxyl groups of CS in the synthesis of SCS. This would increase the hydrophobicity of the resultant starch. The appearances of the proton signal quaternary ammonium group (3.22 ppm) in the 1H NMR spectrum of CCS and the C−N stretching (1483 cm−1) in the FT-IR spectrum indicated the introduction of the positively charged quaternary ammonium group on the CCS backbone. With a similar particle size to the CS, SCS and CCS revealed a rough surface, indicating that the modification reaction mainly occurred on the surface of starch granules. No significant change was observed in the XRD patterns and TG curves of CS, SCS and CCS, indicating that the modified reaction did not dramatically affect the crystalline structure and thermal stability of starch. When the molar ratio of nitrogen and selenium in SCS/CCS was 1200, the catalytic activities of the biomimetic GPx in the reaction systems of 4-nitrothiophenol+cumene hydroperoxide (NBT+CUOOH), 3-carboxy-4-nitrothiophenol+hydrogen oxide (TNB+H2O2), NBT+CUOOH and NBT+H2O2 were 13.94, 11.25, 12.91 and 10.87 µmol/min, respectively, which were 22.1%, 25.8%, 17.5%, 19.6% higher than that of SCS, respectively. This study provided a simple method for constructing starch-based biomimetic GPx with high catalytic activity.

AIM: To compare the postoperative visual acuity among eyes with proliferative diabetic retinopathy (PDR) of different stages after pars plana vitrectomy (PPV) in type 2 diabetic patients. METHODS: A retrospective study was conducted for PDR eyes undergoing PPV in type 2 diabetic patients. All patients were divided into three groups based on Chinese Ocular Fundus Diseases Society (COFDS) classification for PDR: Group A (primary vitreous hemorrhage), Group B (primary fibrovascular proliferation) and Group C (primary vitreous hemorrhage and/or fibrovascular proliferative combined with retinal detachment). The postoperative visual acuity and the change between postoperative and preoperative visual acuity were compared among three groups. The associated risk factors for postoperative visual acuity were analyzed in the univariate and multiple linear aggression. RESULTS: In total, 195 eyes of 195 patients were collected in this study, including 71 eyes of 71 patients in Group A, 75 eyes of 75 patients in Group B and 49 eyes of 49 patients in Group C. The eyes in Group A got better postoperative best-corrected visual acuity (BCVA) compared to the eyes in Group B and C (0.48±0.48 vs 0.89±0.63, P

Abstract The green internet of things with heterogeneous communication technologies can provide data transmission and computing services for low‐carbon operation of smart buildings. However, latency‐sensitive task offloading in smart buildings for multi‐mode green internet of things still faces several challenges such as coupling between multi‐mode channel and multiple gateway selection, diversified quality of service requirement guarantee, and contradiction of long‐term performance guarantee and short‐term optimisation objectives. To address these challenges, a three‐dimensional quota matching‐based latency‐sensitive task offloading algorithm is proposed to minimise the weighted difference between energy consumption and throughput under the long‐term queuing delay constraints. Specifically, the minimisation problem is decoupled by Lyapunov optimisation. The three‐dimensional quota matching among devices, gateways, and channels is employed to solve the conflicts between gateway selection and channel selection. Finally, the three‐dimensional quota matching is converted to a two‐side quota matching to further reduce complexity and solved iteratively. Numerical results demonstrate that compared with H3CG and MMCS, the proposed algorithm improves the weighted difference between energy consumption and throughput by 21.85% and 27.91%, respectively, and reduces the sensor‐side average queuing delay by 30.82% and 16.83%, and gateway‐side average queuing delay by 16.57% and 26.71%, respectively.

Deep learning methods have gained significant popularity in the field of polarimetric synthetic aperture radar (PolSAR) image classification. These methods aim to extract high-level semantic features from the original PolSAR data to learn the polarimetric information. However, using only original data, these methods cannot learn multiple scattering features and complex structures for extremely heterogeneous terrain objects. In addition, deep learning methods always cause edge confusion due to the high-level features. To overcome these limitations, we propose a novel approach that combines a new double-channel convolutional neural network (CNN) with an edge-preserving Markov random field (MRF) model for PolSAR image classification, abbreviated to “DCCNN-MRF”. Firstly, a double-channel convolution network (DCCNN) is developed to combine complex matrix data and multiple scattering features. The DCCNN consists of two subnetworks: a Wishart-based complex matrix network and a multi-feature network. The Wishart-based complex matrix network focuses on learning the statistical characteristics and channel correlation, and the multi-feature network is designed to learn high-level semantic features well. Then, a unified network framework is designed to fuse two kinds of weighted features in order to enhance advantageous features and reduce redundant ones. Finally, an edge-preserving MRF model is integrated with the DCCNN network. In the MRF model, a sketch map-based edge energy function is designed by defining an adaptive weighted neighborhood for edge pixels. Experiments were conducted on four real PolSAR datasets with different sensors and bands. The experimental results demonstrate the effectiveness of the proposed DCCNN-MRF method.

IntroductionDissolved organic matter (DOM) composition varies over space and time, with a multitude of factors driving the presence or absence of each compound found in the complex DOM mixture. Compounds ubiquitously present across a wide range of river systems (hereafter termed core compounds) may differ in chemical composition and reactivity from compounds present in only a few settings (hereafter termed satellite compounds). Here, we investigated the spatial patterns in DOM molecular formulae presence (occupancy) in surface water and sediments across 97 river corridors at a continental scale using the “Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems—WHONDRS” research consortium.MethodsWe used a novel data-driven approach to identify core and satellite compounds and compared their molecular properties identified with Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS).ResultsWe found that core compounds clustered around intermediate hydrogen/carbon and oxygen/carbon ratios across both sediment and surface water samples, whereas the satellite compounds varied widely in their elemental composition. Within surface water samples, core compounds were dominated by lignin-like formulae, whereas protein-like formulae dominated the core pool in sediment samples. In contrast, satellite molecular formulae were more evenly distributed between compound classes in both sediment and water molecules. Core compounds found in both sediment and water exhibited lower molecular mass, lower oxidation state, and a higher degree of aromaticity, and were inferred to be more persistent than global satellite compounds. Higher putative biochemical transformations were found in core than satellite compounds, suggesting that the core pool was more processed.DiscussionThe observed differences in chemical properties of core and satellite compounds point to potential differences in their sources and contribution to DOM processing in river corridors. Overall, our work points to the potential of data-driven approaches separating rare and common compounds to reduce some of the complexity inherent in studying riverine DOM.

Recurrent implantation failure (RIF) is a challenging scenario from different standpoints. This study aimed to investigate its correlation with the endometrial metabolic characteristics. Transcriptomics data of 70 RIF and 99 normal endometrium tissues were retrieved from the Gene Expression Omnibus database. Common differentially expressed metabolism-related genes were extracted and various enrichment analyses were applied. Then, RIF was classified using a consensus clustering approach. Three machine learning methods were employed for screening key genes, and they were validated through the RT-qPCR experiment in the endometrium of 10 RIF and 10 healthy individuals. Receiver operator characteristic (ROC) curves were generated and validated by 20 RIF and 20 healthy individuals from Peking University People’s Hospital. We uncovered 109 RIF-related metabolic genes and proposed a novel two-subtype RIF classification according to their metabolic features. Eight characteristic genes (SRD5A1, POLR3E, PPA2, PAPSS1, PRUNE, CA12, PDE6D, and RBKS) were identified, and the area under curve (AUC) was 0.902 and the external validated AUC was 0.867. Higher immune cell infiltration levels were found in RIF patients and a metabolism-related regulatory network was constructed. Our work has explored the metabolic and immune characteristics of RIF, which paves a new road to future investigation of the related pathogenic mechanisms.

Abstract Objectives To evaluate the impact of autoantibodies against the M2-muscarinic receptor (anti-M2-R) on the clinical outcomes of patients receiving the standard treatment for peripartum cardiomyopathy (PPCM). Methods A total of 107 PPCM patients who received standard heart failure (HF) treatment between January 1998 and June 2020 were enrolled in this study. According to anti-M2-R reactivity, they were classified into negative (n = 59) and positive (n = 48) groups, denoted as the anti-M2-R (−) and anti-M2-R (+) groups. Echocardiography, 6-min walk distance, serum digoxin concentration (SDC), and routine laboratory tests were performed regularly for 2 years. The all-cause mortality, cardiovascular mortality, and rehospitalisation rate for HF were compared between the two groups. Results A total of 103 patients were included in the final data analysis, with 46 in the anti-M2-R (+) group and 57 in the anti-M2-R (−) group. Heart rate was lower in the anti-M2-R (+) group than in the anti-M2-R (−) group at the baseline (102.7 ± 6.1 bpm vs. 96.0 ± 6.4 bpm, p

Abstract Background The occurrence of health events preceding a psoriatic arthritis (PsA) diagnosis may serve as predictors of diagnosis. We sought to assess patients’ real-world experiences in obtaining a PsA diagnosis. Methods This retrospective cohort study analyzed MarketScan claims data from January 2006 to April 2019. Included were adult patients with ≥ 2 PsA diagnoses (ICD-9-CM/ICD-10-CM) ≥ 30 days apart with ≥ 6 years of continuous enrolment before PsA diagnosis. Controls were matched 2:1 to patients with PsA. Health events (diagnoses and provider types) were analyzed before PsA diagnosis and additionally stratified by presence of psoriasis. Results Of 13,661 patients, those with PsA had an increased history of coding for arthritis and dermatologic issues (osteoarthritis [48% vs 22%], rheumatoid arthritis [18% vs 2%], and psoriasis [61% vs 2%]) vs those without PsA. Diagnoses of arthritis, axial symptoms, and tendonitis/enthesitis increased over time preceding PsA diagnosis; notably, a sharp rise in psoriasis diagnoses was observed 6 months before PsA diagnosis. Rheumatology consults were more common immediately preceding a PsA diagnosis. Dermatologists were unlikely to code for arthritis and musculoskeletal issues, while rheumatologists were unlikely to code for psoriasis; general practitioners focused on axial and musculoskeletal symptoms. PsA was most commonly diagnosed by rheumatologists (40%), general practitioners (22%), and dermatologists (7%). Conclusions Rheumatologists, general practitioners, and dermatologists diagnosed two thirds of patients with PsA. Musculoskeletal symptoms were common preceding a PsA diagnosis. Greater awareness of patterns of health events may alert healthcare providers to suspect a diagnosis of PsA.

Abstract Ultra‐wideband (UWB) radar is widely used in static through‐wall imaging (TWI). There are still many imaging problems of motion through‐wall targets for complex system structure and unknown wall environment. Herein, a motion compensation method based on the reference channel and a modified Kirchhoff migration imaging algorithm are proposed for Ultra‐wideband (UWB) MIMO sparse array, which is used for high‐precision 3D TWI of moving targets. The channels of the MIMO radar are time‐division multiplexing based on microwave switch. Besides, a reference channel is added for motion compensation of moving targets. Performing motion compensation on the target before 3D imaging can effectively avoid the problems of defocus and position deviation caused by target motion. In the proposed imaging method, the backward Green function is used to replace the forward Green function to solve the problem of a large field of view with small arrays. The experiment with a static through‐wall target proves that the proposed imaging algorithm can suppress sidelobes well compared with the traditional back‐projection algorithm (BPA). Through‐wall experiments are carried out for targets with different speeds, and results show that the positioning accuracy and sidelobe level of the proposed compensation method is better than previously proposed methods.

Pitting corrosion of rotating machinery is one of the most common faults in industrial engineering. The convolutional neural network (CNN) is increasingly applied to the fault diagnosis. However, the conventional CNN method will reduce the feature dimension of the collected signal and cause the loss of information during the pooling process. In this paper, a new method based on dilated CNN combined with spatial dropout (DCSD) is proposed to diagnose the early faults of rotating machinery. By filling the convolution kernel, the DCSD method can increase the receptive field of the CNN without increasing the number of parameters while retaining more features of the raw vibration signal of the rotating machine. To avoid the dropout method eliminates the adjacent elements with a strong correlation, the Spatial Dropout method is adopted to reduce the overfitting problem of deep networks. The early pitting gears experiment was designed to verify the DCSD method in this paper. The raw vibration signal data of 6 different healthy states were collected to verify the effectiveness of the method. The experimental results show that the DCSD method proposed can effectively distinguish the different early gears pitting, and the diagnostic accuracy is better than other popular deep learning methods.

The number of labeled samples has a great impact on the classification results of a very-high-resolution (VHR) remote sensing image. However, the acquisition of available labeled samples is difficult and time consuming. Faced with the limited labeled samples on a high-resolution remote sensing image, a semisupervised method becomes an effective way. In semisupervised learning, an accurate similarity prediction between unlabeled and labeled samples is very important. However, a reliable similarity prediction between high-dimensional features is difficult. For more reliable similarity prediction for the high-dimensional feature, a novel semisupervised classification framework via improved metric learning with a convolutional neural network is proposed. In the proposed method, a novel trainable metric learning network is designed to accurately evaluate the similarity between high-dimensional features. The vector distance parameter solving problem is transformed into a neural network design problem, which can automatically calculate parameters by the back-propagation algorithm. Finally, the pixel constraint mechanism is introduced to select the unlabeled samples. Experimental results conducted on three VHR remote sensing images, including Aerial, Xi'an, and Pavia University, and the results present that the proposed method performs better than the compared state-of-the-art methods.

Under the strategies of low-carbon and environmental protection, promoting green technology innovation to achieve carbon neutrality in the construction field has become a universal goal. As the building material with the highest consumption, concrete has gradually begun to transform into a multi-functional and intelligent product. Therefore, the research on carbon fiber-reinforced cement-based composites (CFRCs) is of relative interest. It mainly uses carbon fibers (CFs) with high elasticity, strength, and conductivity to disperse evenly into the concrete as a functional filler, to achieve the intelligent integration of concrete structures and function innovatively. Furthermore, the electrical conductivity of CFRC is not only related to the content of CFs and environmental factors but also largely depends on the uniform dispersion and the interfacial bonding strength of CFs in cement paste. This work systematically presents a review of the current research status of the enhancement and modification mechanism of CFRC and the evaluation methods of CF dispersion. Moreover, it further discusses the improvement effects of different strengthening mechanisms on the mechanical properties, durability, and smart properties (thermoelectric effect, electrothermal effect, strain-sensitive effect) of CFRC, as well as the application feasibility of CFRC in structural real-time health monitoring, thermal energy harvesting, intelligent deformation adjustment, and other fields. Furthermore, this paper summarizes the problems and challenges faced in the efficient and large-scale applications of CFRCs in civil engineering structures, and accordingly promotes some proposals for future research.

The primary objective of the study was to assess the values of serum anti-Müllerian hormone (AMH) levels and the combined index for the prediction of number of oocytes retrieved (NOR) and number of good-quality embryos (GQE) in infertile women undergoing IVF/ICSI treatment. A group of 521 infertile women aged 21–46 years were recruited as subject in this study. Serum AMH, hormones, and antral follicle count (AFC) were measured. The infertile women were categorized into three groups: 21–34 years (reproductive age), 35–39 years (reproductive age), and 40–46 years (advanced-age infertile). The predictive accuracy of variables was analyzed by the receiver operating characteristic (ROC) curve. AFC, AFC/age ratio, AMH/age ratio, and ovarian response prediction index (ORPI) decreased gradually, while AMH decreased significantly with increase in age. Moreover, NOR and GQE were positively correlated with AFC, AMH, AFC/age ratio, AMH/age ratio, and ORPI (P

Introduction. Many studies have shown that embryo density has an impact on day-3 embryo-developmental outcomes; however, embryo density remains controversial in clinical practice. We aimed to evaluate the association between embryo density and day-3 embryo-developmental outcomes in real world with the largest sample size. Methods. In 2018, we identified 10941 day-3 embryos from all female patients (n = 1568) in the study. The embryos were allocated to three embryonic densities: 30 μl/embryo (individual culture), 15 μl/embryo, and 10 μl/embryo (group culture). The primary outcomes were cleaving speed, quality, and proportion of successful implantations. The generalized estimate equation (GEE) model was used both in the univariate analysis and multivariable logistic regression analyses to investigate the relationship between embryo density and embryo-developmental outcomes. Results. There were 3064, 5695, and 2182 embryos in the 30 μl/embryo group, 15 μl/embryo group, and 10 μl/embryo group, respectively. The proportions of 7–10 cell embryos were 57.2%, 56.1%, and 58.3% in three densities with no statistical significance (P=0.37), respectively. The proportions of morphologically good embryos were 20%, 20.3%, and 20% in three densities with no statistical significance (P=0.85), respectively. Proportions of implanted embryos were 37.7%, 37.1%, and 27.8% with no statistical significance (P=0.36), respectively. After adjustment for confounders, which were significant in the univariate analysis, the embryo density was still not associated with day-3 embryo-cleaving speed, day-3 embryo quality, and day-3 embryo-implanting potential (all P>0.05). Conclusion. In a 30 μl microdrop, the culturing embryos with embryo densities of 15, 10, and 30 μl/embryo (from zygotes to day 3) had similar developmental outcomes. The embryo density had no impact on day-3 embryo development.

Pesticides are indispensable in today’s agriculture. Methyl-parathion hydrolase (MPH, E.C.3.1.8.1) could hydrolyze organophosphorus pesticides, including methyl-parathion. MPH could rehabilitate soil and water resources contaminated by organophosphorus pesticides. However, natural MPHs generally exhibited a low tolerance to high temperatures and low catalytic efficiency. In this study, we improved the catalytic efficiency toward methyl-parathion and the thermal stability of the MPH from Pseudomonas sp. WBC-3 through saturation mutagenesis and fusion with self-assembling amphipathic peptides (SAP). The experimental characterization showed that compared to the wild-type enzyme, the kcat/Km of the mutant T271S yielded by saturation mutagenesis was increased by 224.3% compared to the wild-type MPH. T50 and Tm of SAP3-MPH with an SAP fused at the N-terminus were increased by 6.2°C and 6.0°C, respectively. Compared to the wild-type enzyme, T271S fused with SAP3 at the N-terminus (SAP3-T271S) exhibited a 2.1-fold increase in kcat/Km without significantly affecting the thermal stability. The simultaneous improvement of the catalytic efficiency and thermal stability of MPH would be beneficial for its application in the degradation and detection of organophosphorus pesticides. Furthermore, our study provides a potential combination strategy for the design of the other enzyme preparations of pollutant degradation.

Language barriers are major obstacles that Asian American immigrants face when accessing health care in the USA. This study was conducted to explore the impact of language barriers and facilitators on the health care of Asian Americans. Qualitative, in-depth interviews and quantitative surveys were conducted with 69 Asian Americans (Chinese, Filipino, Japanese, Malaysian, Indonesian, Vietnamese, and mixed Asian backgrounds) living with HIV (AALWH) in three urban areas (New York, San Francisco, and Los Angeles) in 2013 and from 2017 to 2020. The quantitative data indicate that language ability is negatively associated with stigma. Major themes emerged related to communication, including the impact of language barriers on HIV care and the positive impact of language facilitators-family members/friends, case managers, or interpreters-who can communicate with healthcare providers in the AALWH's native language. Language barriers negatively impact access to HIV-related services and thus result in decreased adherence to antiretroviral therapy, increased unmet healthcare needs, and increased HIV-related stigma. Language facilitators enhanced the connection between AALWH and the healthcare system by facilitating their engagement with health care providers. Language barriers experienced by AALWH not only impact their healthcare decisions and treatment choices but also increase levels of external stigma which may influence the process of acculturation to the host country. Language facilitators and barriers to health services for AALWH represent a target for future interventions in this population.

Because land covers always have different scales, multiscale methods are widely used in very-high-resolution (VHR) remote sensing image classification. Traditional multiscale methods usually capture multiscale information by using rectangular windows of different sizes. Each scale contains the same number of training samples and is independently trained. Hence, the training process is time-consuming. In this article, a novel convolutional neural network with a class-driven loss (CNNs-CDL) model is proposed for multiscale VHR remote sensing image classification. First, a multiscale sample construction method is proposed to select a training sample and capture the relationships among different scale samples. The lowest-scale samples are selected on the lowest-resolution image and are mapped to the higher-resolution image without additional label information. Then, a CNN with class-driven loss is trained with the lowest-scale training samples. Class-driven loss can effectively learn the spatial dependence between the nonadjacent samples to promote classification accuracy. Finally, the CNN model is fine-tuned with the higher-scale samples. Although the number of higher-scale training samples increases, the fine-tuning process requires only a small number of iterations to converge. Hence, the proposed model can effectively reduce the training time. The experimental results for three VHR remote sensing images show that the proposed method performs better than several recently proposed methods.

This article presents a novel dual-path full convolutional network (DP-FCN) model for constructing a landslide inventory map (LIM) with bitemporal very high-resolution (VHR) remote sensing images. Unlike traditional methods for drawing LIM, the proposed DP-FCN directly draws LIMs from the bitemporal aerial images with VHR through a trained deep neural network without generating the change magnitude map. Thus, the proposed approach can effectively reduce the effects of pseudo changes caused by phenological differences rather than landslide events. The proposed DP-FCN model contains two modules, namely, deep feature extraction, and joint feature learning networks. Deep feature extraction aims to reduce redundancy while extracting the high-level deep features from bitemporal images. Joint feature learning establishes the relationship between the deep features of bitemporal images and the ground reference map. Experiments on the real datasets of the landslide sites in Lantau Island of Hong Kong, China, demonstrate the feasibility and superiority of the proposed approach in drawing LIM with VHR remote sensing images. Moreover, compared with the results obtained by the state-of-the-art algorithms, the proposed DP-FCN method achieves the best performance in terms of accuracy for landslide inventory mapping.

Introduction As the largest and most rapidly ageing population, Chinese people are now the major driver of the continued growth in dementia prevalence globally. The need for evidence-based interventions in Chinese communities is urgent. Although a wide range of pharmacological and non-pharmacological interventions for dementia have been trialled in Chinese populations, the evidence has not been systematically synthesised. This systematic review and meta-analysis aims to map out the interventions for people living with dementia and their carers in Chinese communities worldwide and compare the effectiveness of these interventions.Methods and analysis This protocol followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols checklist. We will search Chinese (China National Knowledge Infrastructure, WanFang DATA) and English bibliographical databases (MEDLINE, EMBASE, PsycINFO, CINAHL Plus, Global Health, WHO Global Index Medicus, Virtual Health Library, Cochrane CENTRAL, Social Care Online, BASE, MODelling Outcome and cost impacts of interventions for DEMentia (MODEM) Toolkit, Cochrane Database of Systematic Reviews), complemented by hand searching of reference lists. We will include studies evaluating the effectiveness of interventions for dementia or mild cognitive impairment in Chinese populations, using a randomised controlled trial design, and published between January 2008 and June 2020. We will use a standardised form to extract data and Version 2 of the Cochrane risk-of-bias tool for randomised trials to assess the risk of bias of the included studies. Collected data will be fully interpreted with narrative synthesis and analysed using pairwise and network meta-analyses to pool intervention effects where sufficient information is available. We will perform subgroup analysis and meta-regression to explore potential reasons for heterogeneity.Ethics and dissemination No formal ethics approval is required for this protocol. The findings will facilitate the development of studies on interventions for dementia and timely inform dementia policymaking and practice. Planned dissemination channels include peer-reviewed publications, conference presentations, public events and websites.PROSPERO registration number CRD42019134135.

Reproduction comes at a cost, including accelerated death. Previous studies of the interconnections between reproduction, lifespan, and fat metabolism in C. elegans were predominantly performed in low-reproduction conditions. To understand how increased reproduction affects lifespan and fat metabolism, we examined mated worms; we find that a Δ9 desaturase, FAT-7, is significantly up-regulated. Dietary supplementation of oleic acid (OA), the immediate downstream product of FAT-7 activity, restores fat storage and completely rescues mating-induced death, while other fatty acids cannot. OA-mediated lifespan restoration is also observed in C. elegans mutants suffering increased death from short-term mating, and in mated C. remanei females, indicating a conserved role of oleic acid in post-mating lifespan regulation. Our results suggest that increased reproduction can be uncoupled from the costs of reproduction from somatic longevity regulation if provided with the limiting lipid, oleic acid.

Land cover change detection (LCCD) based on bitemporal remote sensing images has become a popular topic in the field of remote sensing. Despite numerous methods promoted in recent decades, an improvement on the usability and performance of these methods has remained necessary. In this paper, a novel LCCD approach based on the integration of k-means clustering and adaptive majority voting (kmeans_AMV) techniques have been developed. The proposed k-means_AMV method consists of three major techniques. First, to utilize the contextual information in an adaptive manner, an adaptive region around a central pixel is constructed by detecting the spectral similarity between the central pixel and its eight neighboring pixels. Second, when the extension for the adaptive region is terminated, the k-means clustering method is applied to determine the label of each pixel within the adaptive region. Finally, an existing AMV technique is used to refine the label of the central pixel of the adaptive region. When change magnitude image (CMI) is scanned and processed in this manner, the label of each pixel in the CMI can be refined and the binary change detection map can be generated. Three image scenes related to different land cover change events are adapted to test the effectiveness and performance of the proposed k-means_AMV approach. The results show that the proposed k-means_AMV approach demonstrates better detection accuracies and visual performance than that of the several extensively used methods.

Ultra-wideband (UWB) impulse radar is widely used for through-wall human detection due to its high-range resolution and high penetration capability. UWB impulse radar can detect human targets in non-line-of-sight (NLOS) conditions, mainly based on the chest motion caused by human respiration. The automatic detection and extraction of multiple stationary human targets is still a challenge. Missed alarms often exist if the detection method is based on the energy of the human target. This is mainly because factors such as the range of the target, the intensity of the respiratory movement, and the shadow effect will make a difference between the energy scattered by targets. Weak targets are easily masked by strong targets and thus cannot be detected. Therefore, in this paper, a multiple stationary human targets detection method based on convolutional neural network (CNN) in through-wall UWB impulse radar is proposed. After performing the signal-to-clutter-and-noise ratio (SCNR) enhancement method on the raw radar data, the range-slow-time matrix is fed into a six-layer CNN. Benefiting from the powerful feature extraction capability of CNN, the target point of interest (TPOI) can be extracted from the data matrix. The clustering algorithm is used to simplify the TPOIs to achieve accurate detection of multiple targets behind the wall. The effectiveness of the proposed method is verified by the experimental data.

Background. Abnormal endometrial repair after injury results in the formation of intrauterine adhesions (IUA) and a thin endometrium, which are key causes for implantation failure and infertility. Stem cell transplantation offers a potential alternative for some cases of severe Asherman’s syndrome that cannot be treated with surgery or hormonal therapy. Umbilical cord-derived mesenchymal stem cells (UCMSCs) have been reported to repair the damaged endometrium. However, there is no report on the effects of UCMSCs previously seeded on human acellular amniotic matrix (AAM) on endometrial injury. Methods. Absolute ethanol was injected into rat uteri to damage the endometrium. UCMSCs previously seeded on AAM were surgically transplanted. Using a variety of methods, the treatment response was assessed by endometrial thickness, endometrial biomarker expression, endometrial receptivity, cell proliferation, and inflammatory factors. Results. Endometrial thickness was markedly improved after UCMSC-AAM transplantation. The expression of endometrial biomarkers, namely, vimentin, cytokeratin, and integrin β3, in treated rats increased compared with untreated rats. In the UCMSC-AAM group, the VEGF expression decreased, whereas that of MMP9 increased compared with the injury group. Moreover, in the AAM group, the MMP9 expression increased. The expression of proinflammatory factors (IL-2, TNFα, and IFN-γ) in the UCMSC-AAM group decreased compared with the untreated group, whereas the expression of anti-inflammatory factors (IL-4, IL-10) increased significantly. Conclusions. UCMSC transplantation using AAM as the carrier can be applied to treat endometrial injury in rats. The successful preparation of lyophilized AAM provides the possibility of secondary infectious disease screening and amniotic matrix quality detection, followed by retrospective analysis. The UCMSC-AAM complex may promote the better application of UCMSCs on the treatment of injured endometrium.

Physical activity brings multiple health benefits to seniors. Neighborhood parks provide seniors with accessible spaces and opportunities to engage in physical activity. This study investigated the associations between neighborhood park design characteristics and seniors' total walking step and energy expenditure during the park visit. Seniors' total step was measured by pedometer, and energy expenditure was calculated based on self-reported activities in the park. The study was conducted in 15 neighborhood parks with an area

Background. This study is aimed at investigating the repairing effect of mesenchymal stem cells and their exosomes from different sources on ovarian granulosa cells damaged by chemotherapy drugs—phosphoramide mustard (PM). Methods. In this study, we choose bone marrow mesenchymal stem cells (BMSCs) and human placental mesenchymal stem cells (HPMSCs) for research. Then, they were cocultured with human ovarian granulosa cells (SVOG) injured by phosphoramide mustard (PM), respectively. β-Galactosidase staining, flow cytometry, and Western blot were used to detect the changes in the senescence and apoptosis of SVOG cells before and after their coculture with the above two types of MSCs. Subsequently, exosomes from these two types of MSCs were extracted and added to the culture medium of SVOG cells after PM injury to test whether these two types of exosomes played a role similar to that of MSCs in repairing damaged SVOG cells. Results. PM treatment-induced apoptotic SVOG cells were significantly decreased after HPMSCs and BMSCs as compared with control group. After coculturing with these two types of MSCs, PM-treated SVOG cells showed significantly reduced senescence and apoptosis proportions as well as cleaved-Caspase 3 expression, and HPMSCs played a slightly stronger role than BMSCs in repairing SVOG cells in terms of the above three indicators. In addition, the ratios of senescent and apoptotic SVOG cells were also significantly reduced by the two types of exosomes, which played a role similar to that of MSCs in repairing cell damages. Conclusions. The results indicated that BMSCs, HPMSCs, and their exosomes all exerted a certain repair effect on SVOG cells damaged by PM, and consistent repair effect was observed between exosomes and MSCs. The repair effect of exosomes secreted from BMSCs and HPMSCs on the SVOG cells was studied for the first time, and the results fully demonstrated that exosomes are the key carriers for MSCs to play their role.

Background: The N-acetyltransferase 1 (NAT1) gene is downregulated in several cancers and associated with patient survival. In this study, we sought to examine the prognostic value and clinical significance of NAT1 methylation in colon adenocarcinoma (COAD).Methods: Data relating to NAT1 mRNA expression and methylation and clinicopathological features of COAD were extracted from the database of The Cancer Genome Atlas. We compared the mRNA expression and methylation of NAT1 between COAD and normal tissues and performed correlation analysis to assess the association between NAT1 mRNA expression and methylation. Furthermore, we assessed patient survival based on CpG sites in the promoter region of NAT1 and analyzed the association between the NAT1 mRNA expression and CpG site methylation and clinicopathological features. An independent Gene Expression Omnibus (GEO) dataset was used to validate the results.Results: We found that the expression of NAT1 mRNA was reduced in COAD compared with normal tissues and that mean methylation of the eight CpG sites in the promoter region of NAT1 was higher in COAD tissues than in normal tissues. Furthermore, five CpG sites were demonstrated to be significantly negatively correlated with NAT1 mRNA expression in COAD. Survival analysis indicated that NAT1 mRNA expression and the cg15797286 and cg18509990 sites were associated with the overall survival of COAD patients. Combined survival analysis revealed that combinations of NAT1 mRNA expression with five CpG sites were significantly associated with the overall survival of COAD patients. Both NAT1 mRNA and cg15797286 were associated with the T, N, and clinical stages of COAD. The GEO data indicated that cg15797286 was hypermethylated in recurrent colorectal adenomas.Conclusions: Methylation of NAT1 is associated with the development of COAD, and may serve as prognostic and treatment biomarkers for COAD.

Low field-nuclear magnetic resonance (LF-NMR) relaxation characteristics of glyceride mixture can be used in rapid monitoring and product analysis of lipid remodeling processes. In this study, the LF-NMR relaxation properties of single or mixed glyceryl oleate with different esterification degrees were studied. The results indicated that three peaks appeared in the T2 relaxation map of glyceryl oleate. With increasing esterification degree or temperature, both the single component and multi-component relaxation time increased, and the ratios of peak area changed as well. The higher the esterification degree was, the larger the relaxation time changed. For the binary or ternary mixed glyceryl oleate systems, with the glycerol trioleate (GTO) ratio increased to 40%, the single and multi-component relaxation time, the peak area proportion S22 all increased, while the peak area proportion S23 decreased. Principal components analysis (PCA) showed that the LF-NMR relaxation characteristics of ternary system had regular distribution on the score plot with changing GTO ratio and glycerol dioleate (GDO)/glycerol monooleate(GMO) ratio.

Multi-sensor image can provide supplementary information, usually leading to better performance in classification tasks. However, the general deep neural network-based multi-sensor classification method learns each sensor image separately, followed by a stacked concentrate for feature fusion. This way requires a large time cost for network training, and insufficient feature fusion may cause. Considering efficient multi-sensor feature extraction and fusion with a lightweight network, this paper proposes an attention-guided classification method (AGCNet), especially for multispectral (MS) and panchromatic (PAN) image classification. In the proposed method, a share-split network (SSNet) including a shared branch and multiple split branches performs feature extraction for each sensor image, where the shared branch learns basis features of MS and PAN images with fewer learn-able parameters, and the split branch extracts the privileged features of each sensor image via multiple task-specific attention units. Furthermore, a selective classification network (SCNet) with a selective kernel unit is used for adaptive feature fusion. The proposed AGCNet can be trained by an end-to-end fashion without manual intervention. The experimental results are reported on four MS and PAN datasets, and compared with state-of-the-art methods. The classification maps and accuracies show the superiority of the proposed AGCNet model.

We establish dispersion engineering rules that allow us to control the thermalization process and the thermal state of an initial beam propagating in a multimode nonlinear photonic circuit. To this end, we have implemented a kinetic equation (KE) approach in systems whose Bloch dispersion relation exhibits bands and gaps. When the ratio of the gap width to the band width is larger than a critical value, the KE has stationary solutions which differ from the standard Rayleigh-Jeans distribution. The theory also predicts the relaxation times above which such nonconventional thermal states occur. We have tested the validity of our results for the prototype Su-Schrieffer-Heeger model whose connectivity between the composite elements allows control of the band-gap structure. These spectral engineering rules can be extended to more complex photonic networks that lack periodicity but their spectra consist of groups of modes that are separated by spectral gaps.

Ultra-wideband (UWB) impulse radar is widely used for through-wall human respiration detection due to its high range resolution and high penetration capability. UWB impulse radar emits very narrow time pulses, which can directly obtain the impulse response of the target. However, the time interval between successive pulses emitted is not ideally fixed because of timing jitter. This results in the impulse response position of the same target not being fixed, but it is related to slow-time. The clutter scattered by the stationary target becomes non-stationary clutter, which affects the accurate extraction of the human respiration signal. In this paper, we propose a method for reducing timing jitter’s impact in through-wall human detection by UWB impulse radar. After the received signal is processed by the Fast Fourier transform (FFT) in slow-time, we model the range-frequency matrix in the frequency domain as a superposition of the low-rank representation of jitter-induced clutter data and the sparse representation of human respiratory data. By only extracting the sparse component, the impact of timing jitter in human respiration detection can be reduced. Both numerical simulated data and experimental data demonstrate that our proposed method can effectively remove non-stationary clutter induced by timing jitter and improve the accuracy of the human target signal extraction.

Background: At present, a diagnostic tool with high specificity for impaired endometrial receptivity, which may lead to implantation failure, remains to be developed. We aimed to assess the different endometrial microRNA (miRNA) signatures for impaired endometrial receptivity by microarray analysis. Methods: A total of 12 repeated implantation failure (RIF) patients and 10 infertile patients, who conceived and delivered after one embryo transfer attempt, were recruited as RIF and control groups, respectively. Endometrial specimens from the window of implantation (WOI) were collected from these two groups. MiRNA microarray was conducted on seven and five samples from the RIF and control groups, respectively. Comparative, functional, and network analyses were performed for the microarray results. Quantitative real-time polymerase chain reaction (PCR) was performed on other samples to validate the expression of specific miRNAs. Results: Compared with those in the control group, the expression levels of 105 miRNAs in the RIF group were found to be significantly up- or down-regulated (at least 2-fold) by microarray analysis. The most relevant miRNA functional sets of these dysregulated miRNAs were miR-30 family, human embryonic stem cell regulation, epithelial-mesenchymal transition, and miRNA tumor suppressors by tool for annotations of microRNA analysis. Network regulatory analysis found 176 miRNA-mRNA interactions, and the top 3 core miRNAs were has-miR-4668-5p, has-miR-429, and has-miR-5088. Expression levels of the 18 selected miRNAs in new samples by real-time PCR were found to be regulated with the same trend, as the result of microarray analysis. Conclusions: There is a significant different expression of certain miRNAs in the WOI endometrium for RIF patients. These miRNAs may contribute to impaired endometrial receptivity.

Overexpression of receptor-interacting protein 140 (RIP140) promotes neuronal differentiation of N2a cells via extracellular regulated kinase 1/2 (ERK1/2) signaling. However, involvement of RIP140 in human neural differentiation remains unclear. We found both RIP140 and ERK1/2 expression increased during neural differentiation of H1 human embryonic stem cells. Moreover, RIP140 negatively correlated with stem cell markers Oct4 and Sox2 during early stages of neural differentiation, and positively correlated with the neural stem cell marker Nestin during later stages. Thus, ERK1/2 signaling may provide the molecular mechanism by which RIP140 takes part in neural differentiation to eventually affect the number of neurons produced.

Lifespan is shortened by mating, but these deleterious effects must be delayed long enough for successful reproduction. Susceptibility to brief mating-induced death is caused by the loss of protection upon self-sperm depletion. Self-sperm maintains the expression of a DAF-2 insulin-like antagonist, INS-37, which promotes the nuclear localization of intestinal HLH-30/TFEB, a key pro-longevity regulator. Mating induces the agonist INS-8, promoting HLH-30 nuclear exit and subsequent death. In opposition to the protective role of HLH-30 and DAF-16/FOXO, TOR/LET-363 and the IIS-regulated Zn-finger transcription factor PQM-1 promote seminal-fluid-induced killing. Self-sperm maintenance of nuclear HLH-30/TFEB allows hermaphrodites to resist mating-induced death until self-sperm are exhausted, increasing the chances that mothers will survive through reproduction. Mothers combat males’ hijacking of their IIS pathway by expressing an insulin antagonist that keeps her healthy through the activity of pro-longevity factors, as long as she has her own sperm to utilize.