Objective sleep characteristics and hypertension: a community-based cohort study

ObjectiveThe link between sleep quality and hypertension risk is well-established. However, research on the specific dose-relationship between objective sleep characteristics and hypertension incidence remains limited. This study aims to explore the dose-relationship association between objective sleep characteristics and hypertension incidence.MethodsA community-based prospective cohort study design was employed using data from the Sleep Heart Health Study (SHHS). A total of 2,460 individuals were included in the study, of which 780 had hypertension. Baseline personal characteristics and medical history were collected. Objective sleep characteristics were obtained through polysomnography (PSG). Multivariate logistic regression models were utilized for analysis. Restricted cubic splines (RCS) were used to examine dose-relationship associations.ResultsAfter adjusting for covariates, the percentage of total sleep duration in stage 2 (N2%) was positively associated with hypertension incidence, while the N3% was negatively associated with hypertension incidence Odds ratio (OR) = 1.009, 95% confidence interval (CI) [1.001, 1.018], P = 0.037; OR = 0.987, 95% CI: [0.979, 0.995], P = 0.028, respectively. For every 10% increase in N2 sleep, the risk of developing hypertension increases by 9%, while a 3% decrease in N3 sleep corresponds to a 0.1% increase in the incidence of hypertension. In the subgroup of non-depression, a positive association between N2% and hypertension was significant statistically (OR = 1.012, 95%CI, 1.002, 1.021, P = 0.013, Pinteraction = 0.013). RCS demonstrated that the risk of developing hypertension was lower when N2% ranged from 38% to 58% and rapidly increased thereafter (P = 0.002, non-linear P = 0.040). The lowest risk for hypertension incidence risk of N3% occurring at 25%, and a significant increase below 15% or above 40% (P = 0.001, non-linear P = 0.008).ConclusionsThere's a negative association between N3% and the incidence of hypertension, and a positive association between N2% and the incidence of hypertension, particularly among non-depression individuals. These associations exhibit strong non-linear dose-response relationships

Oriented ice eddy detection network based on the Sentinel-1 dual-polarization data

The complex convergence of cold and warm ocean currents in the Nordic Seas provides suitable conditions for the formation and development of eddies. In the Marginal Ice Zones (MIZs), ice eddies contribute to the accelerated melting of surface sea ice by facilitating vertical heat transfer, which influences the evolution of the marginal ice zone and plays an indirect role in regulating global climate. In this paper, we employed high-resolution synthetic aperture radar (SAR) satellite imagery and proposed an oriented ice eddy detection network (OIEDNet) framework to conduct automated detection and spatiotemporal analysis of ice eddies in the Nordic Seas. Firstly, a high-quality RGB false-color imaging method was developed based on Sentinel-1 dual-polarization (HH+HV) Extra-Wide Swath (EW) mode products, effectively integrating denoising algorithms and image processing techniques. Secondly, an automatic ice eddy detection method based on oriented bounding boxes (OBB) was constructed to identify the ice eddy and output features such as horizontal scales, eddy centers and rotation angles. Finally, the characteristics of the detected ice eddies in the Nordic Seas during 2022-2023 were systematically analyzed. The results demonstrate that the proposed OIEDNet exhibits significant performance in ice eddy detection

Exploring Siamese network to estimate sea state bias of synthetic aperture radar altimeter

Sea state bias (SSB) is a crucial error of satellite radar altimetry over the ocean surface. For operational nonparametric SSB (NPSSB) models, such as two-dimensional (2D) or three-dimensional (3D) NPSSB, the solution process becomes increasingly complex and the construction of their regression functions pose challenges as the dimensionality of relevant variables increases. And most current SSB correction models for altimeters still follow those of traditional nadir radar altimeters, which limits their applicability to Synthetic Aperture Radar altimeters. Therefore, to improve this situation, this study has explored the influence of multi-dimensional SSB models on Synthetic Aperture Radar altimeters. This paper proposes a deep learning-based SSB estimation model called SNSSB, which employs a Siamese network framework, takes various multi-dimensional variables related to sea state as inputs, and uses the difference in sea surface height (SSH) at self-crossover points as the label. Experiments were conducted using Sentinel-6 self-crossover data from 2021 to 2023, and the model is evaluated using three main metrics: the variance of the SSH difference, the explained variance, and the SSH difference variance index (SVDI). The experimental results demonstrate that the proposed SNSSB model can further improve the accuracy of SSB estimation. On a global scale, compared to the traditional NPSSB, the multi-dimensional SNSSB not only decreases the variance of the SSH difference by over 11%, but also improves the explained variance by 5-10 cm2 in mid- and low-latitude regions. And the regional SNSSB also performs well, reducing the variance of the SSH difference by over 10% compared to the NPSSB. Additionally, the SNSSB model improves the computational efficiency by approximately 100 times. The favorable results highlight the potential of the multi-dimensional SNSSB in constructing SSB models, particularly the five-dimensional (5D) SNSSB, representing a breakthrough in overcoming the limitations of traditional NPSSB for constructing high-dimensional models. This study provides a novel approach to exploring the multiple influencing factors of SSB

FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Objection

Multi-sensor modal fusion has demonstrated strong advantages in 3D object detection tasks. However, existing methods that fuse multi-modal features through a simple channel concatenation require transformation features into bird's eye view space and may lose the information on Z-axis thus leads to inferior performance. To this end, we propose FusionFormer, an end-to-end multi-modal fusion framework that leverages transformers to fuse multi-modal features and obtain fused BEV features. And based on the flexible adaptability of FusionFormer to the input modality representation, we propose a depth prediction branch that can be added to the framework to improve detection performance in camera-based detection tasks. In addition, we propose a plug-and-play temporal fusion module based on transformers that can fuse historical frame BEV features for more stable and reliable detection results. We evaluate our method on the nuScenes dataset and achieve 72.6% mAP and 75.1% NDS for 3D object detection tasks, outperforming state-of-the-art methods

Concept Design of the “Guanlan” Science Mission: China’s Novel Contribution to Space Oceanography

Among the various challenges that spaceborne radar observations of the ocean face, the following two issues are probably of a higher priority: inadequate dynamic resolution, and ineffective vertical penetration. It is therefore the vision of the National Laboratory for Marine Science and Technology of China that two highly anticipated breakthroughs in the coming decade are likely to be associated with radar interferometry and ocean lidar (OL) technology, which are expected to make a substantial contribution to a submesoscale-resolving and depth-resolving observation of the ocean. As an expanded follow-up of SWOT and an oceanic counterpart of CALIPSO, the planned “Guanlan” science mission comprises a dual-frequency (Ku and Ka) interferometric altimetry (IA), and a near-nadir pointing OL. Such an unprecedented combination of sensor systems has at least three prominent advantages. (i) The dual-frequency IA ensures a wider swath and a shorter repeat cycle which leads to a significantly improved temporal and spatial resolution up to days and kilometers. (ii) The first spaceborne active OL ensures a deeper penetration depth and an all-time detection which leads to a layered characterization of the optical properties of the subsurface ocean, while also serving as a near-nadir altimeter measuring vertical velocities associated with the divergence, and convergence of geostrophic eddy motions in the mixed layer. (iii) The simultaneous functioning of the IA/OL system allows for an enhanced correction of the contamination effects of the atmosphere and the air-sea interface, which in turn considerably reduces the error budgets of the two sensors. As a result, the integrated IA/OL payload is expected to resolve the ocean variability at submeso and sub-week scales with a centimeter-level accuracy, while also partially revealing marine life systems and ecosystems with a 10-m vertical interval in the euphotic layer, moving a significant step forward toward a “transparent ocean” down to the vicinity of the thermocline, both dynamically and bio-optically

FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving

Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such data from multiple sensors to jointly optimize the prediction and planning tasks remains largely unexplored. In this paper, we present FusionAD, to the best of our knowledge, the first unified framework that fuse the information from two most critical sensors, camera and LiDAR, goes beyond perception task. Concretely, we first build a transformer based multi-modality fusion network to effectively produce fusion based features. In constrast to camera-based end-to-end method UniAD, we then establish a fusion aided modality-aware prediction and status-aware planning modules, dubbed FMSPnP that take advantages of multi-modality features. We conduct extensive experiments on commonly used benchmark nuScenes dataset, our FusionAD achieves state-of-the-art performance and surpassing baselines on average 15% on perception tasks like detection and tracking, 10% on occupancy prediction accuracy, reducing prediction error from 0.708 to 0.389 in ADE score and reduces the collision rate from 0.31% to only 0.12%

PD-1 inhibitor-augmented HAIC-TKI therapy in hepatocellular carcinoma with portal vein tumor thrombosis: real-world survival benefits, safety, and subgroup-specific efficacy

BackgroundPD-1/PD-L1 inhibitors have shown efficacy in improving the prognosis of patients with hepatocellular carcinoma (HCC) accompanied by portal vein tumor thrombosis (PVTT) in pivotal clinical trials including the landmark IMbrave150 study. However, not all the patients benefit from the PD-1/PD-L1 blockade immunotherapy. This study aimed to improve the identification of PVTT-associated HCC patients who may benefit from the combination of PD-1 inhibitor and hepatic arterial infusion chemotherapy (HAIC) and tyrosine kinase inhibitor (TKI) treatment under real-world conditions.MethodsFrom 377 HCC-PVTT patients receiving HAIC-TKI ± PD-1 inhibitors (2016-2023), we compared 76 dual-therapy (HT) and 175 triple-therapy (HTP) cases. Median follow-up period was 34.8 months in the HT group and 33.4 months in the HTP group (P=0.175). Propensity score matching (1:1 caliper=0.2) was used to balance baseline characteristics. Overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and safety were evaluated in both groups. Specific subgroups including Vp4 type PVTT, extrahepatic metastases, and patients over 60 years old, were analyzed.ResultsTriple therapy significantly improved median OS (24.6 vs. 13.5 months; HR=0.58, 95%CI:0.42–0.80; P=0.001) and PFS (11.1 vs. 6.4 months; HR=0.56, P<0.001), with a 15% absolute ORR increase (66.3% vs. 51.3%, P=0.034). In subgroup analysis, for patients with Vp4 type PVTT, the addition of PD-1 inhibitor prolonged overall survival by 6.0 months (P=0.04). For patients aged 60 years and above, the addition of PD-1 inhibitor prolonged overall survival by 1.9 months (P=0.363). For patients with extrahepatic metastasis, the addition of PD-1 inhibitor prolonged overall survival by 3.0 months (P=0.913). Grade 3–4 adverse events were comparable (30.9% vs. 19.7%, P=0.09), but two patients experienced immune treatment-related fatalities in the HTP group.ConclusionThe triple therapy (HAIC-TKI-PD-1) demonstrated superior efficacy over HAIC-TKI dual therapy in HCC patients with PVTT, achieving significant improvements in ORR, mOS, and mPFS, with an acceptable safety profile. However, PD-1 inhibitors showed minimal survival benefits in patients aged >60 or with extrahepatic metastases

Effects of maternal dietary heme Fe supplementation on liver iron levels and expression of iron regulatory genes in newborn piglets

Iron deficiency in sows has been demonstrated to have a detrimental effect on porcine fetal growth and development, as well as on the reproductive performance of sows. The placental barrier of sows restricts the transportation of inorganic iron to the fetus, resulting in iron deficiency anemia in neonatal piglets and consequently leading to slow growth. The purpose of this study is to explore the effect of heme Fe on iron metabolism in pregnant sows. Ninety-six multiparous Landrace × Yorkshire (LY) sows (weight 235 ± 15kg) with similar litter size and feeding management were randomly divided into four treatment groups: control group (supplemented with 400 mg/kg), iron deficiency group (with no added FeSO4), heme Fe group (supplemented with 140 mg/kg), and glycine Fe group (supplemented with 470 mg/kg). Iron supplementation lasted from the second trimester (day 30) to day 114 before delivery. In this study, the production performance of sows, the iron content in sow placentas, and in the livers, spleens, placenta and colostrum of newborn piglets, as well as the hemoglobin(HGB) level, the iron regulation parameters in the serum of newborn piglets and the iron regulation genes in the livers and placentas were measured. The results showed that: (1) The number of live births and the average birth weight of piglets in the heme Fe group were 14.8% and 6.33% higher than those in the control group, respectively(P < 0.01). Compared with FeSO4 and glycine Fe, heme Fe improved the production performance of sows. (2) In the heme Fe group, the iron content in colostrum was significantly higher than in the control group (1.27-fold) and glycine Fe group (0.45-fold), while the iron content in the livers of newborn piglets increased by 30.38% and 14.61% compared to the control and glycine Fe groups, respectively (P < 0.01). These results suggest that heme Fe significantly facilitates iron transport in sows, particularly enhancing its deposition in colostrum and neonatal livers. This effect may be attributed to the upregulated expression of heme oxygenase 1(HO-1) gene in the placenta, which enhances the uptake and transport of heme Fe, thereby increasing fetal iron acquisition.(3) In the liver and placentas of sows in the deficiency group, the expression of hepcidin was decreased, while the expressions of transferrin receptor 1 (tfr1), feline leukemia virus subgroup C receptor 1(Flvcr1) and transferrin were increased (P < 0.01). In addition, the gene expression level of HO-1 in the heme Fe group of liver was significantly higher compared to that in the control group (1.85-fold), the iron deficiency group (2.99-fold), and the iron glycinate group (1.67-fold). In conclusion, maternal heme Fe supplements have a significant impact on iron storage in neonatal piglets and are helpful for preventing iron deficiency in newborn piglets

A Rapid Synchronous Determination Method for Soil Inorganic Carbon Content and its Carbon Isotope Ratio

The accumulation and leaching of soil inorganic carbon (SIC) play crucial roles in the global carbon balance and represent a key research focus in carbon cycling studies. Accurate quantification of SIC content and its stable isotope ratio is critical for identifying the current "missing" carbon sink in terrestrial ecosystems. This study developed a rapid，high-throughput method for synchronous measurement of soil inorganic carbon (IC) content and its carbon isotope ratios using cavity ring-down spectroscopy(CRDS) combined with an automated small-volume gas sampler. A synchronous analysis method for inorganic carbon content and isotope ratios in different types of soils was established by analyzing certified reference materials. Results demonstrated that this method has a measurement range of 0.050−0.500 mg (as carbonate)，with a correlation coefficient ≥0.999. The accuracy of SIC analysis was better than 1 g/kg，and the accuracy of carbon isotope analysis was better than 0.5 ‰，with no observed isotope fractionation. The newly developed method was applied to determine inorganic carbon content and isotope ratios in soils with different types and SIC contents. The results showed that all samples achieved good repeatability，and the results were consistent with those measured using the original method. Moreover，the accuracy of SIC content and isotope ratios in soils of 100 mesh is better than that in soils of 60 mesh. The optimized method is simple to operate，offers a low detection limit，requires minimal processing time，and exhibits excellent repeatability，making it highly suitable for rapid and batch analysis of SIC content and its stable carbon isotope ratio