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Subglacial water bodies are critical components in analyzing the instability of the Antarctic ice sheet. Their detection and identification normally rely on geophysical and remote sensing methods such as airborne radar echo sounding (RES), ground seismic, and satellite/airborne altimetry and gravity surveys. In particular, RES surveys are able to detect basal terrain with a relatively high accuracy that can assist with the mapping of subglacial hydrology systems. Traditional RES processing methods for the identification of subglacial water bodies mostly rely on their brightness in radargrams and hydraulic flatness. In this study, we propose an automatic method with the main objective to differentiate the basal materials by quantitatively evaluating the shape of the A-scope waveform near the basal interface in RES radargrams, which has been seldom investigated. We develop an automatic algorithm mainly based on the traditional short-time Fourier transform (STFT) to quantify the shape of the A-scope waveform in radargrams. Specifically, with an appropriate window width applied on the main peak of each A-scope waveform in the RES radargram, STFT shows distinct and contrasting frequency responses at the ice-water interface and ice-rock interface, which is largely dependent upon their different reflection characteristics at the basal interface. We apply this method on 882 RES radargrams collected in the Antarctic’s Gamburtsev Province (AGAP) in East Antarctica. There are 8822 identified A-scopes with the calculated detection value larger than the set threshold, out of the overall 1,515,065 valid A-scopes in these 882 RES radargrams. Although these identified A-scopes only takes 0.58% of the overall A-scope population, they show exceptionally continuous distribution to represent the subglacial water bodies. Through a comprehensive comparison with existing inventories of subglacial lakes, we successfully verify the validity and advantages of our method in identifying subglacial water bodies using the detection probability for other basal materials of theoretically the highest along-track resolution. The frequency signature obtained by the proposed joint time–frequency analysis provides a new corridor of investigation that can be further expanded to multivariable deep learning approaches for subglacial and englacial material characterization, as well as subglacial hydrology mapping.

This paper proposes a method for metering natural gas energy based on the correlation between density and sound velocity. The technique integrates physical property correlation models with measured parameters, such as temperature, pressure, sound velocity, and density, to accurately predict the compression factor and the ideal volume calorific value of natural gas under operating conditions. The volume flow is corrected using the compression factor, which enables precise metering of natural gas energy through the adjusted volume flow and calorific value. To develop a high-precision physical property correlation model, a natural gas dataset comprising 10,000 sample sets is first constructed for model training and testing. Multiple machine learning algorithms are then employed to build predictive models. Analysis of the experimental results led to the development of a model-switching strategy based on the ranges of input features, which substantially enhanced prediction accuracy. For the compression factor model, the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were 0.00118, 0.0030, 0.14%, and 0.9987, respectively. The corresponding indicators for the calorific value model were 0.1583, 0.331, 0.44%, and 0.9736. The proposed method is finally validated using a natural gas real-flow test bench. The results demonstrated maximum prediction errors of 0.061% and 1.19% for the compression factor and calorific value, respectively, while the maximum relative energy error across four gas samples was 1.21%. These results indicate that the method can effectively achieve accurate natural gas energy metering in practical operating conditions.

Protein-dominant cellular processes cannot be fully decoded without precise manipulation of their activity and localization in living cells. Advances in optogenetics have allowed spatiotemporal control over cellular proteins with molecular specificity; however, these methods require recombinant expression of fusion proteins, possibly leading to conflicting results. Instead of modifying proteins of interest, in this work, we focus on design of a tunable recognition unit and develop an aptamer-based near-infrared (NIR) light-responsive nanoplatform for manipulating the subcellular localization of specific proteins in their native states. Our results demonstrate that this nanoplatform allows photocontrol over the cytoplasmic-nuclear shuttling behavior of the target RelA protein (a member of the NF-κβ family), enabling regulation of RelA-related signaling pathways. With a modular design, this aptamer-based nanoplatform can be readily extended for the manipulation of different proteins (e.g., lysozyme and p53), holding great potential to develop a variety of label-free protein photoregulation strategies for studying complex biological events. Optogenetic manipulation of protein localisation in cells involves the creation of fusions that can influence activity. Here the authors develop a near-infrared light-responsive aptamer-based system to regulate the nuclear-cytoplasmic shuttling of NF-κB subunit RelA.

Accurate predictions of wheat yields are essential to farmers’production plans and to the international trade in wheat. However, only poor approximations of the productivity of wheat crops in China can be obtained using traditional linear regression models based on vegetation indices and observations of the yield. In this study, Sentinel-2 (multispectral data) and ZY-1 02D (hyperspectral data) were used together with 15709 gridded yield data (with a resolution of 5 m × 5 m) to predict the winter wheat yield. These estimates were based on four mainstream data-driven approaches: Long Short-Term Memory (LSTM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR). The method that gave the best estimate of the winter wheat yield was determined, and the accuracy of the estimates based on multispectral and hyperspectral data were compared. The results showed that the LSTM model, for which the RMSE of the estimates was 0.201 t/ha, performed better than the RF (RMSE = 0.260 t/ha), GBDT (RMSE = 0.306 t/ha), and SVR (RMSE = 0.489 t/ha) methods. The estimates based on the ZY-1 02D hyperspectral data were more accurate than those based on the 30-m Sentinel-2 data: RMSE = 0.237 t/ha for the ZY-1 02D data, which is about a 5% improvement on the RSME of 0.307 t/ha for the 30-m Sentinel-2 data. However, the 10-m Sentinel-2 data performed even better, giving an RMSE of 0.219 t/ha. In addition, it was found that the greenness vegetation index SR (simple ratio index) outperformed the traditional vegetation indices. The results highlight the potential of the shortwave infrared bands to replace the visible and near-infrared bands for predicting crop yields Our study demonstrates the advantages of the deep learning method LSTM over machine learning methods in terms of its ability to make accurate estimates of the winter wheat yield.

Precipitation is an important factor influencing the date of foliar senescence, which in turn affects carbon uptake of terrestrial ecosystems. However, the temporal patterns of precipitation frequency and its impact on foliar senescence date remain largely unknown. Using both long-term carbon flux data and satellite observations across the Northern Hemisphere, we show that, after excluding impacts from of temperature, radiation and total precipitation by partial correlation analysis, declining precipitation frequency may drive earlier foliar senescence date from 1982 to 2022. A decrease in precipitation frequency intensifies drought stress by reducing root-zone soil moisture and increasing atmospheric dryness, and limit the photosynthesis necessary for sustained growth. The enhanced drought acclimation, showing a more rapid response to drought, also explains the positive relationship between precipitation frequency and foliar senescence date. Finally, we find 30 current state-of-art Earth system models largely fail to capture the sensitivity of DFS to changes in precipitation frequency and incorrectly predict the direction of correlations for approximately half of the northern global lands, in both historical simulations and future predictions. Our results therefore highlight the critical need to include precipitation frequency, rather than just total precipitation, into models to accurately forecast plant phenology under future climate change. Precipitation impacts leaf senescence. Here, the authors use carbon flux and satellite data to demonstrate that reduced precipitation frequency is associated with a faster drought response in trees and show that Earth system models don’t capture the impact of reduced precipitation.

Objective To investigate the risk factors influencing 1-year mortality after intramedullary nail fixation for fragile intertrochanteric fracture in elderly individuals. Methods The medical records of 622 consecutive elderly patients (aged ≥ 65 years) with fragile intertrochanteric fractures treated with proximal femoral nail anti-rotation (PFNA) and followed-up were retrospectively analyzed. The patients were divided into death and survival groups according to their survival status within 1 year after surgery, and the differences in age, sex, region of residence, tobacco use, alcohol use, body mass index (BMI), comorbidities (hypertension, diabetes mellitus, coronary heart disease, stroke, dementia, chronic obstructive pulmonary disease, pneumonia), preoperative hemoglobin, preoperative albumin, deep vein thrombosis, fracture type (AO classification), injury-to-surgery time, American Society of Anesthesiologists (ASA) score, anesthesia modality, duration of surgery, intraoperative blood loss, and blood transfusion were compared. The Kaplan–Meier method was used for univariate analysis to screen for statistically significant differences between the two groups, and the data were entered into the Cox proportional hazards model for multivariate analysis to determine independent risk factors affecting 1-year postoperative mortality. For subgroup analysis, we explored the varying effects of hypoproteinemia and being underweight in patients of different genders, as well as the effects of different age ranges, different injury-to-surgery times, and different blood transfusion volumes on 1-year postoperative mortality. Results The mortality rates at 1, 3, and 6 months, and 1 year after surgery were 3.9%, 7.2%, 10.1%, and 15.3%, respectively. Univariate analysis showed that advanced age, male sex, tobacco use, underweight (BMI < 18.5), coronary heart disease, stroke, dementia, pneumonia, number of comorbidities ≥ 3, hypoproteinemia and injury-to-surgery time ≤ 2 days were associated with the 1-year postoperative survival status ( P  < 0.1). Multivariate analysis revealed that advanced age, male sex, dementia, number of comorbidities ≥ 3, hypoalbuminemia, and being underweight were independent risk factors for 1-year postoperative mortality. Subgroup analysis showed that being underweight was associated with 1-year postoperative mortality only in male patients but not in female patients, whereas hypoproteinemia was associated with 1-year postoperative mortality in both male and female patients. Furthermore, an injury-to-surgery time of less than 2 days improved patient survival, and patients more than 80 years old showed an elevated risk of postoperative mortality. Conclusions Preoperative health status is a critical predictor of postoperative outcomes in elderly patients with fragile intertrochanteric fractures. Priority care should be given to the patients who are elderly, male, have dementia, have comorbidities, or are malnourished. Prompt nutritional reinforcement should be provided to patients with intertrochanteric fractures with comorbid hypoproteinemia and underweight. Furthermore, surgery should be performed as early as possible in patients with fewer comorbidities.

Northwest China has undergone notable alterations in climate and vegetation growth in recent decades. Nevertheless, uncertainties persist concerning the response of different vegetation types to climate change and the underlying mechanisms. This study utilized the Normalized Difference Vegetation Index (NDVI) and three sets of meteorological data to investigate the interannual variations in the association between vegetation and climate (specifically precipitation and temperature) from 1982 to 2015. Several conclusions were drawn. (1) R NDVI-GP (relationship between Growing Season NDVI and precipitation) decreased significantly across all vegetation, while R NDVI-GT (relationship between Growing Season NDVI and temperature) showed an insignificant increase. (2) Trends of R NDVI-GP and R NDVI-GT exhibited great variations across various types of vegetation, with forests displaying notable downward trends in both indices. The grassland exhibited a declining trend in R NDVI-GP but an insignificant increase in R NDVI-GT , while no significant temporal changes in R NDVI-GP or R NDVI-GT were observed in the barren land. (3) The fluctuations in R NDVI-GP and R NDVI-GT closely aligned with variations in drought conditions. Specifically, in regions characterized by VPD (vapor pressure deficit) trends less than 0.02 hpa/yr, which are predominantly grasslands, a rise in SWV (soil water volume) tended to cause a reduction in R NDVI-GP but an increase in R NDVI-GT. However, a more negative trend in SWV was associated with a more negative trend in both R NDVI-GP and R NDVI-GT when the VPD trend exceeded 0.02 hPa/yr, primarily in forests. Our results underscore the variability in the relationship between climate change and vegetation across different vegetation types, as well as the role of drought in modulating these associations.

Gas–Liquid two-phase flows are a common flow in industrial production processes. Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L–K) optical flow method with the Split Comparison (SC) model measurement method. In the proposed approach, videos of gas–liquid two-phase flows are captured using a camera, and optical flow data are acquired from the flow videos using the pyramid L–K optical flow detection method. To address the issue of data clutter in optical flow extraction, a dynamic median value screening method is introduced to optimize the corner point for optical flow calculations. Machine learning algorithms are employed for the prediction model, yielding high flow prediction accuracy in experimental tests. Results demonstrate that the gradient boosted regression (GBR) model is the most effective among the five preset models, and the optimized SC model significantly improves measurement accuracy compared to the GBR model, achieving an R2 value of 0.97, RMSE of 0.74 m3/h, MAE of 0.52 m3/h, and MAPE of 8.0%. This method offers a new approach for monitoring flows in industrial production processes such as oil and gas.

Accurate micro gas flow measurement is critical for medical ventilator calibration, environmental gas monitoring, and semiconductor manufacturing. Laminar flowmeters are widely employed in micro gas flow measurement applications owing to their inherent advantages of high linearity, the absence of moving components, and a broad measurement range. Nevertheless, due to the low measurement accuracy under micro gas flow caused by nonlinear errors and a relatively complex structure, traditional laminar flow measurement devices exhibit limitations in micro gas flow measurement scenarios. This study proposes a novel micro gas flow measurement method based on a single capillary laminar flow element, which simplifies the structure and enhances applicability in the field of micro gas flow. Through structural optimization with precise control of the capillary length–diameter ratios and theoretical error correction based on computational analysis, nonlinear errors were effectively reduced while improving the measurement accuracy in the field of micro gas flow. The proposed methodology was systematically validated through computational fluid dynamics simulations (ANSYS Fluent 2021 R1) and experimental investigations using a dedicated test platform. The experimental results show that the relative error of the measurement system within the full measurement range is less than ±0.6% (1–10 cm3/min; cm3/min means cubic centimeter per minute), and its accuracy is superior to 1% of reading (1% Rd) or 1.5% of reading (1.5% Rd) of conventional laminar flowmeters. The fitting curve of the flow rate versus the pressure difference derived from the measurement results maintains an excellent linear correlation (R2 > 0.99), thus confirming that this method has practical application value in the field of micro gas flow measurement.

Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is one of the severe complications that can occur after acute carbon monoxide poisoning (ACOP). The pathogenesis of DEACMP is complex, featuring a delitescence onset and poor prognosis. As a result, many scholars are concentrating on identifying predictors of DEACMP and evaluating their effects, including clinical characteristics, laboratory indicators, neuroelectrophysiology, imaging examination, and genetic susceptibility. However, current identified predictors lack consensus and their clinical application is limited. Therefore, we need to explore new predictors. Exosomes, the smallest extracellular vesicles (EVs) with nano-size, participate in both the physiological and pathological processes of the brain, and the changes in their content can provide valuable information for clinical diagnosis and evaluation of neurodegenerative diseases, suggesting that they may serve as a potential biomarker. However, the practicability of exosomes as biomarkers of DEACMP remains unclear. In the present review, we first introduced the pathogenesis of DEACMP and the currently identified predictors. Then, we also discussed the possibility of exosomes as the biomarkers of DEACMP, aiming to stimulate more attention and discussion on this topic, thereby providing meaningful insights for future research.