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Abstract Parkinsonian tremor (PT) and Essential tremor (ET) exist as upper limb tremors in clinical practice. Notably, their types of trembling share similar presentations and overlapping frequencies. To enhance objectivity and efficiency in the diagnosis of these two diseases, there is a pressing need for more objective tremor classification procedures. This study proposes a novel multimodal fusion network based on a cross-attention mechanism (MFCA-Net) to automate the classification of upper limb tremors between PTs and ETs. To this end, 140 patients with PTs and ETs were recruited, and acceleration and surface electromyography (sEMG) signals were collected from the forearm during tremor episodes. To comprehensively capture the global and local features of input signals, a multiscale convolution in MFCA-Net was designed. Furthermore, the cross-attention mechanism was applied to fuse the features of the two input signals. The results demonstrate that the final classification accuracy exceeded 97.18% when MFCA-Net was used. Compared with the single acceleration signal and single sEMG signal inputs, the recognition accuracies increased by 18.91% and 10.04%, respectively. Therefore, the proposed MFCA-Net in this study serves as an objective and potential tool for assisting clinicians in the diagnosis of PT and ET patients.

Abstract Sports records play a crucial role in understanding the limits of human achievement in sports. However, a thorough exploration of a comprehensive analysis of various sports records utilizing the existing statistical model has been lacking. This study introduces a framework for analyzing the integrated features and evolutionary trends of 23 sports records for men and women. It includes world records and intercontinental records from six continents, covering 6440 athletes from 2001 to 2020. Our findings indicate that human beings have not yet reached sports limits in athletic performance, suggesting a continuous improvement over time. Furthermore, we have investigated the contributions of our model’s parameters to the integrated features, emphasizing their robustness and convergence in handling data flow and information entropy. Additionally, our model underscores the significance of integrating various sports for ongoing advancement, in line with the Olympic motto “Together,” thereby promoting coordinated development.

Abstract People with lower extremity peripheral artery disease (PAD) have increased oxidative stress, impaired mitochondrial activity, and poor walking performance. NAD+ reduces oxidative stress and is an essential cofactor for mitochondrial respiration. Oral nicotinamide riboside (NR) increases bioavailability of NAD+ in humans. Among 90 people with PAD, this randomized double-blind clinical trial assessed whether 6-months of NR, with and without resveratrol, improves 6-min walk distance, compared to placebo, at 6-month follow-up. At 6-month follow-up, compared to placebo, NR significantly improved 6-min walk (+7.0 vs. −10.6 meters, between group difference: +17.6 (90% CI: + 1.8,+∞). Among participants who took at least 75% of study pills, compared to placebo, NR improved 6-min walk by 31.0 meters and NR + resveratrol improved 6-min walk by 26.9 meters. In this work, NR meaningfully improved 6-min walk, and resveratrol did not add benefit to NR alone in PAD. A larger clinical trial to confirm these findings is needed. Clinical Trials.gov registration: NCT03743636.

Abstract Excessive fat accumulation and chronic inflammation are two typical characteristics of obesity. AMP‐activated protein kinase (AMPK), a master regulator of energy metabolism, is involved in adipogenesis, lipogenesis, and inflammation modulation in adipose tissue (AT). Thus, effective lipid reduction and anti‐inflammation through AMPK regulation play vital roles in treating obesity. Herein, an anti‐obesity nanosandwich is fabricated through attaching polymetformin (PolyMet) onto photothermal agent black phosphorus nanosheets (BP). PolyMet activates AMPK to inhibit adipogenesis, promote browning, and mitigate AT inflammation by decreasing macrophage infiltration, repolarizing macrophage phenotype, and downregulating pro‐inflammatory cytokines. Additionally, BP induces lipolysis and apoptosis of adipocytes and macrophages through a photothermal effect. By further functionalization using hyaluronic acid (HA) and MMP2 substrate‐linking P3 peptide‐modified HA (P3‐HA), an enhanced anti‐obesity effect is obtained by dual‐targeting of P3 and HA, and HA‐mediated CD44 poly‐clustering after MMP2 cleavage. Upon laser irradiation, the designed nanosandwich (P3‐HA/PM@BP) effectively inhibits obesity development in obese mice, increases M2/M1 ratio in AT, reduces the serum levels of cholesterol/triglyceride and improves insulin sensitivity, exhibiting promising research potential to facilitate the clinical development of modern anti‐obesity therapies.

Abstract Exploring effective antibacterial approaches for targeted treatment of pathogenic bacterial infections with reduced drug resistance is of great significance. Combinational treatment modality that leverages different therapeutic components can improve the overall effectiveness and minimize adverse effects, thus displaying considerable potential against bacterial infections. Herein, red blood cell membrane fuses with macrophage membrane to develop hybrid cell membrane shell, which further camouflages around drug‐loaded liposome to fabricate biomimetic liposome (AB@LRM) for precise antibacterial therapy. Specifically, photoactive agent black phosphorus quantum dots (BPQDs) and classical antibiotics amikacin (AM) are loaded in AB@LRM to accurately target the inflammatory sites through the guidance of macrophage membrane and long residence capability of red blood cell membrane, eventually exerting efficacious antibacterial activities. Besides, due to the excellent photothermal and photodynamic properties, BPQDs act as an efficient antibacterial agent when exposed to near‐infrared laser irradiation, dramatically increasing the sensitivity of bacteria to antibiotics. Consequently, the synergistic sterilizing effect produced by AB@LRM further restricts bacterial resistance. Upon laser irradiation, AB@LRM shows superior anti‐inflammatory and antibacterial properties in models of P. aeruginosa‐infected pneumonia and wounds. Hence, this light‐activatable antibacterial nanoplatform with good biocompatibility presents great potential to advance the clinical development in the treatment of bacterial infections.

Abstract Post-translational modifications of proteins in malignant transformation and tumor maintenance of pancreatic ductal adenocarcinoma (PDAC) in the context of KRAS signaling remain poorly understood. Here, we use the KPC mouse model to examine the effect of palmitoylation on pancreatic cancer progression. ZDHHC20, upregulated by KRAS, is abnormally overexpressed and associated with poor prognosis in patients with pancreatic cancer. Dysregulation of ZDHHC20 promotes pancreatic cancer progression in a palmitoylation-dependent manner. ZDHHC20 inhibits the chaperone-mediated autophagic degradation of YTHDF3 through S-palmitoylation of Cys474, which can result in abnormal accumulation of the oncogenic product MYC and thereby promote the malignant phenotypes of cancer cells. Further, we design a biologically active YTHDF3-derived peptide to competitively inhibit YTHDF3 palmitoylation mediated by ZDHHC20, which in turn downregulates MYC expression and inhibits the progression of KRAS mutant pancreatic cancer. Thus, these findings highlight the therapeutic potential of targeting the ZDHHC20–YTHDF3–MYC signaling axis in pancreatic cancer.

Abstract Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash–Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling.

Gel của Ca-alginate trong nước được ứng dụng nhiều trong vật liệu màng composite với nhiều ứng dụng linh hoạt đã được nhiều nhà khoa học phát hiện ra. Bên cạnh đó, xu hướng sử dụng vật liệu da thân thiện môi trường đã phát triển rất nhiều ở các nước trên thế giới. Bài báo này nhằm cung cấp những thông tin về vật liệu màng composite và vật liệu giả da Bioleather dựa trên liên kết ion gel của Ca- Alginate trong môi trường nước.

Abstract An optimized millimeter-wave digital controlled oscillator (DCO) in a 40-nm CMOS process is presented in this work. The coarse-tuning modules and medium-tuning modules of the DCO utilize modified binary-weighted digitally controlled transmission lines (DCTLs) to achieve a better compromise among smaller chip size, higher resonant frequency, better tuning resolution and lower phase noise. The tuning precision and die size of the medium tuning bank are improved without changing the binary coding rules by replacing the lowest-weight bit of the DCTLs with switched capacitors. In comparison with traditional DCTLs, the control bits of the coarse and medium tuning modules have been changed from 30 to 8, resulting in a 34.4% reduction in overall length (from 122 $$\upmu$$ μ m to 80 $$\upmu$$ μ m). In addition, the DCO’s fine-tuning modules are achieved using a binary-weighted switched capacitors array connected to the secondary winding of a low-coupling transformer, which enhances the DCO’s fine-tuning bank for better frequency resolution with less circuit complexity. The measured tuning range of the optimized DCO is 76-81GHz with a smaller die size of 0.12mm $$^2$$ 2 . This results in an outstanding figure of merit ( $$FoM_A$$ F o M A ) of − 190.52dBc/Hz.

Abstract Deep neural networks (DNNs) extract thousands to millions of task-specific features during model training for inference and decision-making. While visualizing these features is critical for comprehending the learning process and improving the performance of the DNNs, existing visualization techniques work only for classification tasks. For regressions, the feature points lie on a high dimensional continuum having an inherently complex shape, making a meaningful visualization of the features intractable. Given that the majority of deep learning applications are regression-oriented, developing a conceptual framework and computational method to reliably visualize the regression features is of great significance. Here, we introduce a manifold discovery and analysis (MDA) method for DNN feature visualization, which involves learning the manifold topology associated with the output and target labels of a DNN. MDA leverages the acquired topological information to preserve the local geometry of the feature space manifold and provides insightful visualizations of the DNN features, highlighting the appropriateness, generalizability, and adversarial robustness of a DNN. The performance and advantages of the MDA approach compared to the existing methods are demonstrated in different deep learning applications.

This paper addresses RadhakrishnanâKunduâLakshmanan equation that arises in the study of soliton dynamics in optical fibers. The bifurcation analysis is carried out and the phase portraits are displayed. The complete discriminant analysis also leads to solitons and other solutions to the model.

Abstract Ligand effect, induced by charge transfer between catalytic surface and substrate in core/shell structure, was widely proved to benefit Pt-catalyzed oxygen reduction reaction by tuning the position of d-band center of Pt theoretically. However, ligand effect is always convoluted by strain effect in real core/shell nanostructure; therefore, it remains experimentally unknown whether and how much the ligand effect solely contributes electrocatalytic activity improvements. Herein, we report precise synthesis of a kind of Pd3Ru1/Pt core/shell nanoplates with exclusive ligand effect for oxygen reduction reaction. Layer-by-layer growth of Pt overlayers onto Pd3Ru1 nanoplates can guarantee no lattice mismatch between core and shell because the well-designed Pd3Ru1 has the same lattice parameters as Pt. Electron transfer, due to the exclusive ligand effect, from Pd3Ru1 to Pt leads to a downshift of d-band center of Pt. The optimal Pd3Ru1/Pt1-2L nanoplates achieve excellent activity and stability for oxygen reduction reaction in alkaline/acid electrolyte.

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios.

Abstract MRGPRX1, a Mas-related GPCR (MRGPR), is a key receptor for itch perception and targeting MRGPRX1 may have potential to treat both chronic itch and pain. Here we report cryo-EM structures of the MRGPRX1-Gi1 and MRGPRX1-Gq trimers in complex with two peptide ligands, BAM8-22 and CNF-Tx2. These structures reveal a shallow orthosteric pocket and its conformational plasticity for sensing multiple different peptidic itch allergens. Distinct from MRGPRX2, MRGPRX1 contains a unique pocket feature at the extracellular ends of TM3 and TM4 to accommodate the peptide C-terminal “RF/RY” motif, which could serve as key mechanisms for peptidic allergen recognition. Below the ligand binding pocket, the G6.48XP6.50F6.51G6.52X(2)F/W6.55 motif is essential for the inward tilting of the upper end of TM6 to induce receptor activation. Moreover, structural features inside the ligand pocket and on the cytoplasmic side of MRGPRX1 are identified as key elements for both Gi and Gq signaling. Collectively, our studies provide structural insights into understanding itch sensation, MRGPRX1 activation, and downstream G protein signaling.

Continuous adductor canal block (CACB) is almost a pure sensory nerve block and can provide effective analgesia without blocking the motor branch of the femoral nerve. Thus, the objective of this study was to systematically evaluate the efficacy of CACB versus continuous femoral nerve block (CFNB) on analgesia and functional activities in patients undergoing knee arthroplasty. PubMed, Embase and the Cochrane Central Register of Controlled Trials (from inception to 3 October 2023) were searched for randomized controlled trials (RCTs) that compared CACB with CFNB in patients undergoing knee arthroplasty. Registration in the PROSPERO International prospective register of the meta-analysis was completed, prior to initiation of the study (registration number: CRD42022363756). Two independent reviewers selected the studies, extracted data and evaluated risk of bias by quality assessment. Revman 5.4 software was used for meta-analysis and the summary effect measure were calculated by mean differences and 95% confidence intervals. Eleven studies with a total of 748 patients were finally included. Pooled analysis suggested that both CACB and CFNB showed the same degree of pain relief at rest and at motion at 12 h, 24 h and 48 h in patients undergoing knee arthroplasty. Compared with CFNB, CACB preserved the quadriceps muscle strength better (P

Since 2016, the Argo (Array for Real-Time Geostrophic Oceanography) ocean salinity data has exhibited significant drift, directly affecting the accurate quantification of the global steric sea level (SSL) rates. To further investigate how salinity drift affects the estimation of SSL rates in different depths and regions, we divide the 0–2000 m into three layers (0–300 m, 300–1000 m and 1000–2000 m) and select five open oceans (the South and North Pacific, the South and North Atlantic, and the Indian Ocean) for discussion. By comparing the SSL rates between the periods of 2005–2015 and 2005–2019, we can evaluate the impact of salinity drift. Taking the estimated results from the IPRC (provided by the International Pacific Research Center at the University of Hawaii) and BOA (provided by the Second Institute of Oceanography, China) data as examples, we find that the effect of salinity drift is the largest at the depth of 1000–2000 m, about 29% for IPRC data and about 18% for BOA data. Moreover, the South Atlantic is susceptible to the effects of salinity drift, with an approximately 13% impact for IPRC data and 21% for BOA data.

Abstract This study investigated the mechanism of membrane damage by protocatechualdehyde (PCA) against Micrococcus luteus and assessed effects of PCA on the sensory and physicochemical properties of pork. The mechanism of PCA inhibition on M. luteus was studied by determining the minimum inhibitory concentration (MIC) based on membrane potential, intracellular ATP concentration, intracellular pH, confocal laser scanning microscopy (CLSM), and field emission gun scanning electron microscopy (FEG-SEM). The results showed that the MIC of PCA against M. luteus was 1.25 mg/mL. Hyperpolarization of the bacterial cell membrane, a decrease in the intracellular ATP concentration, and intracellular pH indicated that PCA damaged the cell membrane of M. luteus. FEG-SEM observation revealed that PCA could cause surface collapse, cell membrane rupture, and content outflow of M. luteus. Additionally, PCA was found to inhibit increases in the total number of colonies, the thiobarbituric acid reactive substances (TBARS) value growth rate, and moisture mobility in raw pork. Additionally, it improved the color and texture of raw pork, all of which effectively prolonged its shelf life. This study will encourage the application of PCA as a natural antibacterial agent in the food industry.

Abstract Superoxide dismutase (SOD1) gene variants may cause amyotrophic lateral sclerosis, some of which are associated with a distinct phenotype. Most studies assess limited variants or sample sizes. In this international, retrospective observational study, we compare phenotypic and demographic characteristics between people with SOD1-ALS and people with ALS and no recorded SOD1 variant. We investigate which variants are associated with age at symptom onset and time from onset to death or censoring using Cox proportional-hazards regression. The SOD1-ALS dataset reports age of onset for 1122 and disease duration for 883 people; the comparator population includes 10,214 and 9010 people respectively. Eight variants are associated with younger age of onset and distinct survival trajectories; a further eight associated with younger onset only and one with distinct survival only. Here we show that onset and survival are decoupled in SOD1-ALS. Future research should characterise rarer variants and molecular mechanisms causing the observed variability.

Abstract Computed tomography (CT) has been widely used for the diagnosis of pelvic rhabdomyosarcoma (RMS) in children. However, it is difficult to differentiate pelvic RMS from other pelvic malignancies. This study aimed to analyze and select CT features by using least absolute shrinkage and selection operator (LASSO) logistic regression and established a Fisher discriminant analysis (FDA) model for the quantitative diagnosis of pediatric pelvic RMS. A total of 121 pediatric patients who were diagnosed with pelvic neoplasms were included in this study. The patients were assigned to an RMS group (n = 36) and a non-RMS group (n = 85) according to the pathological results. LASSO logistic regression was used to select characteristic features, and an FDA model was constructed for quantitative diagnosis. Leave-one-out cross-validation and receiver operating characteristic (ROC) curve analysis were used to evaluate the diagnostic ability of the FDA model. Six characteristic variables were selected by LASSO logistic regression, all of which were CT morphological features. Using these CT features, the following diagnostic models were established: (RMS group) $${G}_{1}=-14.283+6.613{x}_{1}+5.333{x}_{2}+5.753{x}_{3}+12.361{x}_{4}+8.095{x}_{5}-0.715{x}_{6}$$ G 1 = - 14.283 + 6.613 x 1 + 5.333 x 2 + 5.753 x 3 + 12.361 x 4 + 8.095 x 5 - 0.715 x 6 ; (Non-RMS group) $${G}_{2}=-2.008+3.539{x}_{1}+1.080{x}_{2}+1.154{x}_{3}+2.307{x}_{4}+1.656{x}_{5}+1.380{x}_{6}$$ G 2 = - 2.008 + 3.539 x 1 + 1.080 x 2 + 1.154 x 3 + 2.307 x 4 + 1.656 x 5 + 1.380 x 6 , where $${x}_{1}$$ x 1 , $${x}_{2}$$ x 2 , … and $${x}_{6}$$ x 6 are lower than normal muscle density (1 = yes; 0 = no), multinodular fusion (1 = yes; 0 = no), enhancement at surrounding blood vessels (1 = yes; 0 = no), heterogeneous progressive centripetal enhancement (1 = yes; 0 = no), ring enhancement (1 = yes; 0 = no), and hemorrhage (1 = yes; 0 = no), respectively. The calculated area under the ROC curve (AUC) of the model was 0.992 (0.982–1.000), with a sensitivity of 94.4%, a specificity of 96.5%, and an accuracy of 95.9%. The calculated sensitivity, specificity and accuracy values were consistent with those from cross-validation. An FDA model based on the CT morphological features of pelvic RMS was established and could provide an easy and efficient method for the diagnosis and differential diagnosis of pelvic RMS in children.

Abstract With the serious shortage of water resources and the development of water-saving agriculture, the application of drip irrigation has been paid more and more attention. But there was lack of oat planting methods suitable for drip irrigation, currently. In order to establish an efficient oat planting method for drip irrigation, a study was conducted at Agriculture and Forestry Sciences of Ulanqab, Inner Mongolia during the season (2019–2020) to evaluate the effect of strip cropping with reducing row spacing and super absorbent polymer on the yield and water use efficiency of oat. To conduct the field trials, a split plot system in three replications was established. Three planting patterns were in the main plots, including conventional cropping with 20 cm equal row spacing (PA), strip cropping with the 15 cm row spacing (PB) and strip cropping with the 10 cm row spacing (PC), and two super absorbent polymer levels were in the subplots, including 22.5 kg ha−2 (Y) and 0 (N). The results showed that, compared with PA, PB and PC both decreased the irrigation volumes by 4.5–18.4 mm, and the irrigation volumes of PB was lower than that of PC. When super absorbent polymers were applied, compared with PA, PB significantly increased grain yield and above-ground biomass, but PC had the opposite effects. The grain yield and above-ground biomass of PB significantly increased by 16.65% and 7.31% on average in two years, respectively. And the increasing of grain yield was attributed by the significant increasing of pike number and kernel number per spike. But when super absorbent polymers were not applied, PB had no significant effects on grain yield and above-ground biomass. PB also had the significant effects on regulating water use of oats weather or not super absorbent polymers were applied, it significantly increased the precipitation ratio by 2.64% (PBY) and 2.13% (PBN) and decreased irrigation ration by 3.32% (PBY) and 5.28% (PBN) on average in two years. Although PB and PC both decreased the total evapotranspiration, but PB increased WUE and PC deceased WUE. The WUE of PB increased by 19.70% (PBY) and 9.87% (PBN) on average in two years. Also PB had the highest economic benefits in all treatments. In conclusion, a drip irrigation oat planting pattern was proposed, which the row spacing is 15 cm, adjusted the equal row spacing planting to 8-row strip planting, with a belt spacing of 30 cm, combined with the application of 22.5 kg ha−2 applying super absorbent polymers. And this oat planting pattern is a viable strategy to improve oat productivity.

Abstract The clinical characteristics of persistent postural-perceptual dizziness (PPPD) vary according to patient age and inducing factors. We aimed to analyze the differences in the clinical characteristics of PPPD with different patient age groups and different etiologies. A total of 122 PPPD patients hospitalized in the vertigo ward of Nanjing Brain Hospital from December 2018 to July 2021 were enrolled. According to whether dizziness symptoms were secondary to organic diseases, PPPD patients were divided into the primary (p-) and secondary (s-) PPPD groups; subgroups were created according to age including youth group, middle-aged group, older adults group 1 and older adults 2. We collected detailed data from each patients, including scores on the Dizziness Handicap Inventory (DHI), mental state and other clinical data. The ratio of males to females was 1:2. The prevalence of emotional disorders in the middle-aged group was the highest (67.57%) and that in the older adults groups was lower (48.08% in older adults group 1 and 8.70% in older adults group 2, P = 0.000). The proportion of p-PPPD patients with emotional disorders was significantly higher than that of s-PPPD patients (53.48% vs. 30.56%, P = 0.028). The average total DHI score in the middle-aged group was significantly higher than that in older adults group 2 (52.86 vs. 35.04, P = 0.032), and the Beck anxiety score in the middle-aged group was higher than that in older adults group 2 (38.89 vs. 27.65, P = 0.000). The middle-aged group had the highest proportion of women, the highest proportion of patients with emotional disorders and the highest vertigo score. The proportion of patients with emotional disorders and the vertigo scores were lower in the older adults groups.

The self-assembly of molecular knots in water is challenging. Here, authors report the self-assembly of a trefoil knot in water via imine condensation, without relying on external templates; the handedness of the trefoil knot is determined by the chirality of the bisamino precursor.

Abstract Type 2 diabetes (T2D) and amyotrophic lateral sclerosis (ALS) are associated consistently. However, it is currently unknown whether this association is causal. We aimed to estimate the unconfounded, causal association between T2D on ALS using a two-sample Mendelian randomization approach both in European and East Asian ancestry. Genetic variants strongly associated with T2D and each T2D markers were used to investigate the effect of T2D on ALS risk in European (involving 20,806 ALS cases and 59,804 controls) and East Asian (involving 1234 ALS cases and 2850 controls) ancestry. We found that the OR of ALS per 1 SD increase in T2D was estimated to be 0.96 [95% confidence interval (CI) 0.92–0.996; p = 0.03] in European populations. Similarly, all 8 SNPs were associated with T2D in East Asian ancestry, the OR of ALS per 1 SD increase in T2D was estimated to be 0.83 [95% CI 0.70–0.992; p = 0.04] in East Asian populations. Examining the intercept estimates from MR-Egger regression also leads to the same conclusion, in that horizontal pleiotropy unlikely influences the results in either population. We found that genetically predicted T2D was associated with significantly lower odds of amyotrophic lateral sclerosis both in European and East Asian populations. It is now critical to identify a clear molecular explanation for this association between T2D and ALS and to focus on its potential therapeutic implications.

The decidualization of endometrial stroma is critical for pregnancy maintenance. Here the authors reveal that Menin ensures the expression of PTX3 through H3K4me3 modification, to balance the BMP and FGF signal in the decidua for normal pregnancy.

BackgroundNitrous oxide (N2O) with rapid analgesic effect is often used to relieve pain induced by diagnostic procedures. This review was conducted to evaluate the efficacy and safety of N2O in patients undergoing puncture biopsy.MethodsWe systematically searched PubMed, Embase, the Cochrane Library, Web of Science, Scopus and the ClinicalTrials.gov up to March, 2022. Randomized controlled trials (RCTs) were included if they investigated the effect of N2O in adults undergoing puncture biopsy. The primary outcome was pain score. Secondary outcomes included anxiety score, patient satisfaction and side effects.ResultsTwelve RCTs with 1070 patients were included in the qualitative review, of which eleven RCTs were included in the meta-analysis. Pooled analysis suggested that compared with the controls (placebo, lidocaine and midazolam), N2O had better analgesic effect (MD -1.12, 95% CI -2.12 to -0.13, P = 0.03; I2 = 94%). In addition, N2O significantly alleviated patient anxiety (MD = -1.79, 95% CI -2.41 to -1.18, PConclusionThe present review suggested that N2O might be effective for pain management in patients undergoing puncture biopsy.

Background Nitrous oxide (N2O) with rapid analgesic effect is often used to relieve pain induced by diagnostic procedures. This review was conducted to evaluate the efficacy and safety of N2O in patients undergoing puncture biopsy. Methods We systematically searched PubMed, Embase, the Cochrane Library, Web of Science, Scopus and the ClinicalTrials.gov up to March, 2022. Randomized controlled trials (RCTs) were included if they investigated the effect of N2O in adults undergoing puncture biopsy. The primary outcome was pain score. Secondary outcomes included anxiety score, patient satisfaction and side effects. Results Twelve RCTs with 1070 patients were included in the qualitative review, of which eleven RCTs were included in the meta-analysis. Pooled analysis suggested that compared with the controls (placebo, lidocaine and midazolam), N2O had better analgesic effect (MD -1.12, 95% CI -2.12 to -0.13, P = 0.03; I2 = 94%). In addition, N2O significantly alleviated patient anxiety (MD = -1.79, 95% CI -2.41 to -1.18, PConclusion The present review suggested that N2O might be effective for pain management in patients undergoing puncture biopsy.

Abstract Carbon trading mechanism is an effective means to control greenhouse gas emissions. This paper focuses on the low‐carbon economic operation of the integrated energy system under carbon trading mechanism in China. The integrated energy system includes the energy storage, ground source heat pump, and other equipment. The objective of this paper was to minimize the annual total cost of the system considering the carbon trading cost and study the operation modes under different carbon trading prices by commercial optimization software. The simulation results show the operation modes in summer are changed obviously with the increase of the carbon trading prices, while the operation modes in winter basically are not changed with the fluctuation of the carbon trading prices; under the carbon trading mechanism, the integrated energy system can not only reduce the carbon emissions, but also reduce the annual total cost through carbon trading, which shows its advantages and good development prospect. In addition, the setting of the carbon emission quota has a direct impact on the economy of the enterprises. Furthermore, the ground source heat pump and the electric refrigeration units can stabilize the fluctuation of the equipment output caused by the change of natural gas prices in this integrated energy system.

Measurement(s) DNA • genome • sequence_assembly • sequence feature annotation Technology Type(s) DNA sequencing • sequence assembly process • sequence annotation Sample Characteristic - Organism Nibea albiflora Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16528335

Abstract The three-stage super-efficiency slack-based measure and data envelopment analysis (SBM-DEA) model with undesirable outputs is used to calculate carbon emissions efficiency of industrial energy (CEEIE) of 30 provinces in China from 2000 to 2017. Then ArcGIS software is used to illustrate the spatial distribution of CEEIE, and Dagum Gini ratio is calculated to decompose the regional difference. The results show that the spatial distribution of CEEIE changes from disorder to order and provinces characterized with high or low CEEIE cluster in space over time. The total Dagum Gini coefficient indicates that the interprovincial difference in CEEIE across China is gradually expanding, which is mainly induced by the difference between regions. Our findings attach more importance to interregional integration policies for carbon emissions reduction in China.

Bài báo này được thực hiện nhằm nghiên cứu phát hiện và kiểm tra việc tuân thủ các quy định về đeo khẩu trang, giữ khoảng cách xã hội ở các địa điểm đông đúc. Mô hình YOLO được sử dụng để xây dựng thuật toán phát hiện đeo khẩu trang đúng hay không đúng quy định, đồng thời kiểm tra việc giữ khoảng cách xã hội bằng việc sử dụng thuật toán tính khoảng cách Euclid giữa các khung bao quanh người được phát hiện trong hình ảnh, kết hợp thuật toán chuyển đổi Bird’s-eye view. Tập dữ liệu được sử dụng bao gồm 40 hình ảnh với hai đối tượng người được phân loại thực tế theo khoảng cách đứng với nhau: lớn hơn hoặc bằng 2 m và nhỏ hơn 2 m. Đồng thời, mỗi đối tượng người trong hình ảnh được phân loại thành ba lớp: đeo khẩu trang đúng hay không đúng và không đeo khẩu trang. Kết quả thử nghiệm khoảng cách đối tượng đạt 90% và nhận diện đối tượng đeo khẩu trang có kết quả như sau: 86,67% đeo khẩu trang đúng, 76,67% không đeo khẩu trang và 65% đeo khẩu trang...

Tunneling work, including the construction of municipal tunnels and metro lines, may disturb the structural health of aging buildings in densely built urban areas. Deformation monitoring and risk assessments of aging buildings are crucial to mitigate incidents and prevent losses of people’s lives and properties. Time-series InSAR reveals spatio-temporal information about observed targets by extracting persistent scatterers of the structures, which can achieve the wide-range monitoring of buildings and infrastructure. However, solely relying on InSAR-derived general parameters (deformation rates and time series of specific points) cannot objectively assess the safety conditions of buildings. To address this issue, this study proposes an InSAR Nonuniform Settlement Index. First, the point targets of buildings are extracted through time-series InSAR processing. Then, using the points as inputs, the Nonuniform Settlement Index calculates the 3D settlement plane and the inclination angle of the plane corresponding to each building. In this way, the proposed Nonuniform Settlement Index acts as a subsequent analysis method of time-series InSAR to characterize the safety statuses of buildings. In our study, 147 scenes of COSMO-SkyMed images from 2013 to 2022 were used to inverse the nine-year deformation evolution of the tested area. After time-series InSAR processing and index analysis based on the above SAR datasets, cross-validation was implemented with static-level and manual investigation data. The approach was to use one aging, collapsed building affected by tunneling work, as well as the eight adjacent aging buildings. The results showed high consistency with the in situ data, which proves the efficiency of the proposed approach.

The underlying mechanisms by which Smad4 loss-of-function accelerates lung cancer metastasis is unclear. Here, the authors show PAK3 as a downstream effector of Smad4 promoting metastatic signalling and suggest Smad4-PAK3 regulation as a point of potential therapy in metastatic lung cancer.

Abstract Weight loss is common in patients with Amyotrophic lateral sclerosis (ALS), and associated with disease progression. Loss of appetite has been shown to be a contributor to weight loss in patients with amyotrophic lateral sclerosis (ALS). However, the reason of loss of appetite is not clear. The Council on Nutrition appetite questionnaire (CNAQ) and the simplified nutritional appetite questionnaire (SNAQ) are short and simple appetite assessment tools, which were using in ALS patients. In our study, the CNAQ and SNAQ were translated into Chinese, and their reliability and validity were tested. The Chinese version of the CNAQ (CNAQ-C) presented more appropriate reliability and validity than the SNAQ. Among the 94 ALS patients, 50 patients (53.2%) had loss of appetite, and we found that anxiety and/or depression contributed to the loss of appetite in the ALS patients. We reconfirmed that loss of appetite was associated with greater weight loss but not with clinical features of ALS. The loss of appetite caused by emotional problems in ALS patients should be taken seriously, and early intervention should be implemented to reduce weight loss.

Abstract Acute traumatic intraparenchymal hematoma (tICH) expansion is a devastating neurological complication that is associated with poor outcome after cerebral contusion. This study aimed to develop and validate a novel noncontrast computed tomography (CT) (NCCT) multihematoma fuzzy sign to predict acute tICH expansion. In this multicenter, prospective cohort study, multihematoma fuzzy signs on baseline CT were found in 212 (43.89%) of total 482 patients. Patients with the multihematoma fuzzy sign had a higher frequency of tICH expansion than those without (90.79% (138) vs. 46.71% (71)). The presence of multihematoma fuzzy sign was associated with increased risk for acute tICH expansion in entire cohort (odds ratio [OR]: 16.15; 95% confidence interval (CI) 8.85–29.47; P

Designing reliable, scalable and high speed computing systems remains a challenge. Here, the authors identify noncentrosymmetric orthorhombic phase in HZO film and demonstrate a CMOS compatible 3D Vertical HZO-based ferroelectric diode array with self-selective property and 20 ns of speed operation.

Leakage errors derived from spatial filters are the major limitation for estimating terrestrial water storage via the Gravity Recovery and Climate Experiment (GRACE) mission and the recently launched GRACE Follow-On mission. Here we develop an improved forward modeling method and assess its performance of reducing leakage errors over Africa. In noise-free condition, the forward modeling method shows its outperformance in restoring signals, and the improved forward modeling method can further reduce the leakage errors along the coastline of Africa. In noise-contaminated condition, the simulated environment is set as real as possible to GRACE mission and GRACE Follow-On mission. The results based on the simulated GRACE and GRACE Follow-On solutions demonstrate the capacity of improved forward modeling method in reducing leakage errors. In the case of simulated GRACE data, the average improvements of 24 basins over Africa are respectively 37% for annual amplitudes and 36% for trends. When compared with these simulated GRACE data, the improvements via simulated GRACE Follow-On solutions are minor over large and medium size river basins, but they are significant over small size river basins. In the case of simulated GRACE Follow-On solutions, the average improvements over Africa are 39% for annual amplitudes and 41% for trends. Eventually, the improved forward modeling method is used to process GRACE spherical harmonic datasets from the Center for Space Research (CSR). The results present better agreement with those derived from the newly released mascon solutions from Jet Propulsion Laboratory (JPL) and CSR, when compared with those derived from CSR Tellus grids with scale factors. The better consistency between these model-independent approaches indicates the good performance of our improved forward modeling method and the further necessity of careful evaluation of model-dependent approaches when using different prior hydrological models. Overall, experiments based on noise-free observations, noise-contaminated observations, and GRACE datasets indicate that improved forward modeling method is capable of restoring temporal signals.

Currently, JavaScript malicious code detection methods are becoming more and more effective. Still, the existing methods based on deep learning are poor at detecting too long or too short JavaScript code. Based on this, this paper proposes an adaptive code length deep learning network JACLNet, composed of convolutional block RDCNet, BiLSTM and Transfrom, to capture the association features of the variable distance between codes. Firstly, an abstract syntax tree recombination algorithm is designed to provide rich syntax information for feature extraction. Secondly, a deep residual convolution block network (RDCNet) is designed to capture short-distance association features between codes. Finally, this paper proposes a JACLNet network for JavaScript malicious code detection. To verify that the model presented in this paper can effectively detect variable JavaScript code, we divide the datasets used in this paper into long text dataset DB_Long; short text dataset DB_Short, original dataset DB_Or and enhanced dataset DB_Re. In DB_Long, our method's F1 - score is 98.87%, higher than that of JSContana by 2.52%. In DB_Short, our method's F1-score is 97.32%, higher than that of JSContana by 7.79%. To verify that the abstract syntax tree recombination algorithm proposed in this paper can provide rich syntax information for subsequent models, we conduct comparative experiments on DB_Or and DB_Re. In DPCNN+BiLSTM, F1-score with abstract syntax tree recombination increased by 1.72%, and in JSContana, F1-score with abstract syntax tree recombination increased by 1.50%. F1-score with abstract syntax tree recombination in JACNet improved by 1.00% otherwise unused.

The purpose of this study was to investigate the factors that influence beef meat exports in Tanzania, with a particular focus on the years 1985 to 2020, in enhancing the development of beef meat export-oriented policy in Tanzania, thereby enhancing beef exports in Tanzania. A time-series panel dataset was analyzed using both descriptive statistics and ordinary least squares (OLS) linear regression analyses models. As per the descriptive analyses, beef meat exports reached the highest pick of 4,300 tons per year in 1990, whereas from 1991 to date, beef meat exportation in Tanzania has been in declining trends despite an increase in beef meat output and trade openness from 162,500 to 486,736 tons and 7.6 to 98.7%, respectively. Nevertheless, while the prospect of Tanzanian beef meat exports appears bright and promising, the industry will continue to encounter trade barriers and must stay competitive to produce enough volume and quality beef meat to meet the needs of its existing and expanding markets. This is because, Tanzanian beef meat competes for market share with beef meat from other countries in the global markets, where customers pay a premium for lines of beef meat that meet quality standards while discarding those that do not. This indicates that the quantity of beef meat produced has no relevance to its world market share, but its quality standards do. Furthermore, the econometric results revealed that the coefficients of the terms of trade, Tanzania GDP per capita, global beef meat consumption, trade openness, and beef meat outputs were found to be significantly positive (P < 0.05) influencing beef meat exports in Tanzania, whereas the trading partners' GDP per capita and exchange rate were not. The findings have varying implications as to what factors need to be addressed to further improve beef meat exports. From the farmer's perspective, better access to adequate funds as a result of increased income benefit from export will assist in improving beef cattle productivity and quality to compete effectively in the global markets. From the government's perspective, because trade openness promotes economic growth through export benefits, the Tanzania government and policymakers need to establish balanced policies to strengthen bilateral trade relationships to generate more opportunities in global markets.

The purpose of this study was to investigate the factors that influence beef meat exports in Tanzania, with a particular focus on the years 1985 to 2020, in enhancing the development of beef meat export-oriented policy in Tanzania, thereby enhancing beef exports in Tanzania. A time-series panel dataset was analyzed using both descriptive statistics and ordinary least squares (OLS) linear regression analyses models. As per the descriptive analyses, beef meat exports reached the highest pick of 4,300 tons per year in 1990, whereas from 1991 to date, beef meat exportation in Tanzania has been in declining trends despite an increase in beef meat output and trade openness from 162,500 to 486,736 tons and 7.6 to 98.7%, respectively. Nevertheless, while the prospect of Tanzanian beef meat exports appears bright and promising, the industry will continue to encounter trade barriers and must stay competitive to produce enough volume and quality beef meat to meet the needs of its existing and expanding markets. This is because, Tanzanian beef meat competes for market share with beef meat from other countries in the global markets, where customers pay a premium for lines of beef meat that meet quality standards while discarding those that do not. This indicates that the quantity of beef meat produced has no relevance to its world market share, but its quality standards do. Furthermore, the econometric results revealed that the coefficients of the terms of trade, Tanzania GDP per capita, global beef meat consumption, trade openness, and beef meat outputs were found to be significantly positive (P < 0.05) influencing beef meat exports in Tanzania, whereas the trading partners’ GDP per capita and exchange rate were not. The findings have varying implications as to what factors need to be addressed to further improve beef meat exports. From the farmer’s perspective, better access to adequate funds as a result of increased income benefit from export will assist in improving beef cattle productivity and quality to compete effectively in the global markets. From the government’s perspective, because trade openness promotes economic growth through export benefits, the Tanzania government and policymakers need to establish balanced policies to strengthen bilateral trade relationships to generate more opportunities in global markets.

Low-level features contain spatial detail information, and high-level features contain rich semantic information. Semantic segmentation research focuses on fully acquiring and effectively fusing spatial detail with semantic information. This paper proposes a multiscale feature-enhanced adaptive fusion network named MFEAFN to improve semantic segmentation performance. First, we designed a Double Spatial Pyramid Module named DSPM to extract more high-level semantic information. Second, we designed a Focusing Selective Fusion Module named FSFM to fuse different scales and levels of feature maps. Specifically, the feature maps are enhanced to adaptively fuse these features by generating attention weights through a spatial attention mechanism and a two-dimensional discrete cosine transform, respectively. To validate the effectiveness of FSFM, we designed different fusion modules for comparison and ablation experiments. MFEAFN achieved 82.64% and 78.46% mIoU on the PASCAL VOC2012 and Cityscapes datasets. In addition, our method has better segmentation results than state-of-the-art methods.

Low-level features contain spatial detail information, and high-level features contain rich semantic information. Semantic segmentation research focuses on fully acquiring and effectively fusing spatial detail with semantic information. This paper proposes a multiscale feature-enhanced adaptive fusion network named MFEAFN to improve semantic segmentation performance. First, we designed a Double Spatial Pyramid Module named DSPM to extract more high-level semantic information. Second, we designed a Focusing Selective Fusion Module named FSFM to fuse different scales and levels of feature maps. Specifically, the feature maps are enhanced to adaptively fuse these features by generating attention weights through a spatial attention mechanism and a two-dimensional discrete cosine transform, respectively. To validate the effectiveness of FSFM, we designed different fusion modules for comparison and ablation experiments. MFEAFN achieved 82.64% and 78.46% mIoU on the PASCAL VOC2012 and Cityscapes datasets. In addition, our method has better segmentation results than state-of-the-art methods.

BackgroundPrenatal phthalates exposures have been related to adiposity in peripuberty in a sex-specific fashion. Untargeted metabolomics analysis to assess circulating metabolites offers the potential to characterize biochemical pathways by which early life exposures influence the development of cardiometabolic risk during childhood and adolescence, prior to becoming evident in clinical markers.MethodsAmong mother-child dyads from the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) birth cohort, we measured 9 phthalate metabolites and bisphenol A in maternal spot urine samples obtained during each trimester of pregnancy, corrected for urinary specific gravity and natural log-transformed. In 110 boys and 124 girls aged 8-14 years, we used a mass-spectrometry based untargeted metabolomics platform to measure fasting serum metabolites, yielding 572 annotated metabolites. We estimated the associations between trimester-specific urinary toxicants and each serum metabolite, among all children or stratified by sex and adjusting for child age, BMI z-score, and pubertal onset. We accounted for multiple comparisons using a 10% false discovery rate (qResultsAssociations between exposures and metabolites were observed among all children and in sex-stratified analyses (qConclusionsMetabolomics biomarkers may reflect sex- and exposure timing-specific responses to prenatal phthalate exposures manifesting in childhood that may not be detected using standard clinical markers of cardiometabolic risk.

Beef meat production is the key to reducing poverty, achieving food security and nutrition, promoting exports, economic growth, and industrialization. Despite a large number of beef cattle, Tanzania continues to import beef meat and its contribution to GDP is low. Thus, this study used time-series panel data to analyze the beef meat industry in Tanzania from 1990 to 2019, with a particular focus on identifying the reasons and direction of the correlation between beef meat output and its determinants in the production processes. The study applied both descriptive statistics and the Cobb-Douglas production function model, using the Ordinary Least Squares (OLS) based estimator to analyze the data. Descriptive analyses revealed that Tanzania's beef meat production increased by 283,871 tons (59.3%-a positive trend) between 1990 and 2019. This increase was accompanied by approximately 29.75%, 53.05%, and 42.42% increases in beef cattle yield (carcass weight (hg) per head, beef cattle inventory, and the number of beef cattle slaughtered, respectively). However, the analysis further revealed that a 2.8% decrease in beef cattle extraction (low harvesting) rate due to low market participation, continues to be a critical barrier to increasing beef meat production in Tanzania. Furthermore, econometric estimates showed that the key factors that positively influenced beef meat output at a 5% significance level (P < 0.05) included beef cattle population (inventory), beef cattle yield (carcass weight (kg) per head, and the number of beef cattle slaughtered, with elasticity coefficients of 0.146, 0.469, and 0.564, respectively). While the number of beef cattle exported positively influenced beef meat production at the 10% significance level (P < 0.1) with an elasticity coefficient of 0.028. Surprisingly, invested credit to agriculture (farm credits) and imported pure-bred beef cattle had a negative impact on beef meat output but were statistically insignificant at P < 0.05. The results of this study have implications as to what factors need to be addressed to further improve beef meat production, thereby reducing its reliance on imports. We suggest that the Tanzania government and policymakers need to establish balanced policies for beef farmers and appropriately manage them so that beef meat development can be induced, contributing to poverty reduction, food security, and economic development.

Single strand breaks represent the most common form of DNA damage yet no methods to map them in a genome-wide fashion at single nucleotide resolution exist. Here the authors develop such a method and apply to uncover patterns of single-strand DNA “breakome” in different biological conditions.

Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae) is a serious invasive pest in China. In this study, we determined whether exogenous jasmonic acid (JA) and ethylene (ET) treatments could induce resistance against F. occidentalis in faba bean plants. First, we investigated the effects of different concentrations of JA or ET alone on F. occidentalis and then assessed the effects of optimal concentrations of JA and ET combined. Our results showed that the optimal concertation of JA was 2 mmol/L and ET was 0.5 mmol/L. JA + ET mixture showed the greatest inhibitory effect in terms of oviposition and feeding. JA with ET was found to induce changes in the activities of lipoxygenase (LOX), allene oxide synthase (AOS), polyphenol oxidase (PPO), 1-aminocyclopropane 1-carboxylic acid synthase (ACS), and trypsin inhibitor (TI). This treatment also activated or inhibited the relative expression levels of LOX1, ACO2, ACS2, and AP2/ERF. Treatment of faba bean plants with JA and ET significantly prolonged F. occidentalis development and adult preoviposition period, significantly reduced per-female oviposition, and altered male longevity and offspring demographic parameters. These results indicate that JA with ET can induce defenses against the growth and development of F. occidentalis in faba bean plants.

Traditional wake detection methods have been successfully applied to the detection of a simple linear ship wake. However, they cannot effectively detect nonlinear wake and weak wake under high sea state conditions, whereas the deep-learning-based detection method could play to its strengths in this respect. Due to the lack of sufficient measured SAR images of ship wake to meet the training requirement for deep learning method, this paper explores the method to detect the nonlinear ship wake by combining electromagnetic scattering model with deep learning technique. The composite scene model of the sea surface and its wake is established first, then the facet scattering distribution of the ship wake and the sea background is analyzed with the help of the electromagnetic scattering model, and the simulation of the wake SAR images under the sea background is finally accomplished based on the modulation model. Combined with the simulation results and measured wake SAR images, the sample database is constructed. The You Only Look Once Version five algorithm (YOLOv5) based on deep learning techniques is applied to detect the wake target in complex conditions such as different sea states, multiple targets, curvilinear wakes, and weak wakes. The result show that the YOLOv5 leads to an obvious higher detection efficiency with satisfactory accuracy. Moreover, the comparison between YOLOv5 and the traditional Radon transform method for detecting nonlinear wakes in a strong noise background shows that the proposed method is better than the traditional object detection model. Thus, the proposed scheme would be a practical tool to deal with the detection of nonlinear ship wake and weak wake in complex scenarios, which will be helpful to the further remote sensing investigation of the ship.

T cells from narcolepsy patients were recently reported to recognize hypocretin, a wakefulness-promoting neurohormone, suggesting autoimmune origin of the disease. Here the authors show that hypocretin-specific T cells expand both in healthy controls and in narcolepsy patients, and identify preliminary features that may distinguish them.

The spatial resolution of remote sensing images directly affects the accuracy, efficiency, and computational cost of extracting the fractional vegetation cover (FVC). Taking the Liyang woodland region, Jiangsu Province, as the study area, FVCs with varying spatial resolutions were extracted separately from Sentinel-2, Landsat-8, MOD13Q1, and MOD13A1. The variations in FVCs extracted from remote sensing images with varying spatial resolutions were analyzed at one specific time and time series within a year. The results show that (i) the overall mean FVC values of the four spatial resolution images did not differ substantially; however, FVCs with varying spatial resolutions present with a regular pattern of overestimation or underestimation at different vegetation levels. (ii) Taking the 10 m spatial resolution FVC as the reference, the accuracy values of FVC extraction at 30 m, 250 m, and 500 m resolutions were 91.0%, 76.3%, and 76.7%, respectively. The differences in the spatial distribution of FVCs are the most obvious at water–land interfaces and at the edge of each woodland patch. (iii) The highest accuracy of time-series FVC extraction from lower-resolution images is in the range of 0.6~0.7 for FVC. The degree of variation in FVC of time series varying spatial resolutions depends on the season and vegetation cover conditions. In summary, there are considerable differences in the need to monitor high-resolution images depending on the FVC level of the land surface. This study provides a reference for selection and accuracy research of remote sensing images for FVC extraction.