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Little is known about the effect of persistent depressive symptoms on the trajectory of cognitive decline.AimsWe aimed to investigate the longitudinal association between the duration of depressive symptoms and subsequent cognitive decline over a 10-year follow-up period. The English Longitudinal Study of Ageing cohort is a prospective and nationally representative cohort of men and women living in England aged ≥50 years. We examined 7610 participants with two assessments of depressive symptoms at wave 1 (2002-2003) and wave 2 (2004-2005), cognitive data at wave 2 and at least one reassessment of cognitive function (wave 3 to wave 7, 2006-2007 to 2014-2015). The mean age of the 7610 participants was 65.2 ± 10.1 years, and 57.0% were women. Of these, 1157 (15.2%) participants had episodic depressive symptoms and 525 participants (6.9%) had persistent depressive symptoms. Compared with participants without depressive symptoms at wave 1 and wave 2, the multivariable-adjusted rates of global cognitive decline associated with episodic depressive symptoms and persistent depressive symptoms were faster by -0.065 points/year (95% CI -0.129 to -0.000) and -0.141 points/year (95% CI -0.236 to -0.046), respectively (P for trend < 0.001). Similarly, memory, executive and orientation function also declined faster with increasing duration of depressive symptoms (all P for trend < 0.05). Our results demonstrated that depressive symptoms were significantly associated with subsequent cognitive decline over a 10-year follow-up period. Cumulative exposure of long-term depressive symptoms in elderly individuals could predict accelerated subsequent cognitive decline in a dose-response pattern.Declaration of interestNone.

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

Self-reported tiredness or low energy, often referred to as fatigue, has been linked to lifestyle factors, although data from randomized–controlled trials are lacking. We investigate whether modifiable lifestyle factors including smoking and alcohol intake related exposures (SAIEs) are causal factors for fatigue using Mendelian randomization (MR). A two-sample MR study was performed by using genome-wide association summary results from UK Biobank (UKBB), and each of the sample size is more than 100,000. We used the inverse variance weighted method, and sensitivity analyses (MR Egger, weighted median, penalized median estimators, and multivariable MR) to account for pleiotropy. The two-sample MR analyses showed inverse causal effect of never-smoking status and positive effect of current smoking status on the risk of fatigue. Similarly, genetically predicted alcoholic intake was positively associated with fatigue. The results were consistent across the different MR methods. Our Mendelian randomization analyses do support that the cessation of smoking and alcohol can decrease the risk of fatigue, and limit alcohol intake frequency can also reduce the risk.

Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality.

Background Cancer-associated fibroblasts (CAFs), as key cell populations in the tumor microenvironment (TME), play a crucial role in tumor regulation. Previous studies on a prognostic signature of 8 CAF-related genes in head and neck squamous cell carcinoma (HNSCC) revealed that Secreted frizzled-related protein 1 (SFRP1) is one of the hub genes closely related to CAFs. SFRP1 is deficiently expressed in numerous types of cancer and is classified as a tumor suppressor gene. However, the role of SFRP1 in TME regulation in HNSCC remains unclear. This study aimed to explore the role of SFRP1 in the proliferation and migration of HNSCC cells by mediating CAFs and their regulatory mechanisms. Methods The expression differences, prognosis, and immune infiltration of SFRP1 in HNSCC were analyzed using the TIMER and GEPIA2 databases. The expression of SFRP1 in HNSCC tumor tissues, as well as the expression and secretion of SFRP1 in CAFs and tumor cells, were examined. An indirect co-culture system was constructed to detect the proliferation, migration, and apoptosis of HNSCC cells, and to clarify the effect of SFRP1 on tumor cells by mediating CAFs. Furthermore, the expression and secretion of 10 cytokines derived from CAFs that act on immune cells were verified. Results SFRP1 was differently expressed in HNSCC tumor tissues and highly expressed in CAFs. SFRP1 inhibited the proliferation and migration of tumor cells and promoted apoptosis by mediating CAFs. The detection of CAFs-derived factors suggested that the mechanism of action of SFRP1 was associated with the regulation of immune cells. Conclusion SFRP1 inhibits the proliferation and migration of HNSCC cells by mediating CAFs, and the mechanism of action is related to the regulation of immune cells, which may provide new research directions and therapeutic targets for HNSCC.

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

Data scarcity is a key factor impacting the current emphasis on individual indicators and the distribution of large-scale spatial objects in country-level SDG 6 research. An investigation of progress assessments and factors influencing SDG implementation in cities and counties indicates that smaller-scale regions hold greater operational significance for achieving the 2030 Agenda for Sustainable Development from the bottom up; thus, urgent attention should be given to data deficiencies and inadequate analyses related to SDG impact attribution. This study, conducted in the National Innovative Demonstration Zone for Sustainable Development of Lincang City, investigates multisource data sources such as integrated statistics, survey data, and remote sensing data to analyze the progress and status of SDG 6 achievement from 2015–2020, and employs the LMDI decomposition model to identify influential factors. The assessment results demonstrate that the SDG 6 composite index in Lincang increased from 0.47 to 0.61 between 2015 and 2020. The SDG 6 indicators and SDG 6 composite index have significant spatial heterogeneity. The water resources indexes in wealthy countries are high, the water environment and water ecology indexes in developing countries are comparatively high, and the SDG 6 composite index is high in undeveloped counties. Technological and economic advances are the main positive drivers impacting the SDG 6 composite index, and the relative contributions of technology, economy, structure, and population are 61.84%, 54.16%, −4.03%, and −11.96%, respectively. This study shows that integrated multisource data can compensate for the lack of small-scale regional statistical data when quantitative and comprehensive multi-indicator evaluations of the SDGs are conducted. And, policies related to SDG 6.1.1, SDG 6.2.1, and SDG 6.3.1 can be a priority for implementation in undeveloped regions with limited funding.

Leaf chlorophyll content (LCC) provides valuable information about the nutrition and photosynthesis statuses of crops. Vegetation index-based methods have been widely used in crop management studies for the non-destructive estimation of LCC using remote sensing technology. However, many published vegetation indices are sensitive to crop canopy structure, especially the leaf area index (LAI), when crop canopy spectra are used. Herein, to address this issue, we propose four new spectral indices (The red-edge-chlorophyll absorption index (RECAI), the red-edge-chlorophyll absorption index/optimized soil-adjusted vegetation index (RECAI/OSAVI), the red-edge-chlorophyll absorption index/ the triangular vegetation index (RECAI/TVI), and the red-edge-chlorophyll absorption index/the modified triangular vegetation index(RECAI/MTVI2)) and evaluate their performance for LCC retrieval by comparing their results with those of eight published spectral indices that are commonly used to estimate LCC. A total of 456 winter wheat canopy spectral data corresponding to physiological parameters in a wide range of species, growth stages, stress treatments, and growing seasons were collected. Five regression models (linear, power, exponential, polynomial, and logarithmic) were built to estimate LCC in this study. The results indicated that the newly proposed integrated RECAI/TVI exhibited the highest LCC predictive accuracy among all indices, where R2 values increased by more than 13.09% and RMSE values reduced by more than 6.22%. While this index exhibited the best association with LCC (0.708** ≤ r ≤ 0.819**) among all indices, RECAI/TVI exhibited no significant relationship with LAI (0.029 ≤ r ≤ 0.167), making it largely insensitive to LAI changes. In terms of the effects of different field management measures, the LCC predictive accuracy by RECAI/TVI can be influenced by erective winter wheat varieties, low N fertilizer application density, no water application, and early sowing dates. In general, the newly developed integrated RECAI/TVI was sensitive to winter wheat LCC with a reduction in the influence of LAI. This index has strong potential for monitoring winter wheat nitrogen status and precision nitrogen management. However, further studies are required to test this index with more diverse datasets and different crops.

In eukaryotes, end-binding (EB) proteins serve as a hub for orchestrating microtubule dynamics and are essential for cellular dynamics and organelle movements. EB proteins modulate structural transitions at growing microtubule ends by recognizing and promoting an intermediate state generated during GTP hydrolysis. However, the molecular mechanisms and physiochemical properties of the EB1 interaction network remain elusive. Here we show that EB1 formed molecular condensates through liquid–liquid phase separation (LLPS) to constitute the microtubule plus-end machinery. EB1 LLPS is driven by multivalent interactions among different segments, which are modulated by charged residues in the linker region. Phase-separated EB1 provided a compartment for enriching tubulin dimers and other plus-end tracking proteins. Real-time imaging of chromosome segregation in HeLa cells expressing LLPS-deficient EB1 mutants revealed the importance of EB1 LLPS dynamics in mitotic chromosome movements. These findings demonstrate that EB1 forms a distinct physical and biochemical membraneless-organelle via multivalent interactions that guide microtubule dynamics. Maan et al., Meier et al. and Song et al. report that microtubule plus-tip end binding proteins can undergo liquid–liquid phase separation and regulate microtubule dynamics.

The mechanical behavior of unsaturated porous media under non-isothermal conditions plays a vital role in geo-hazards and geo-energy engineering (e.g., landslides triggered by fire and geothermal energy harvest and foundations). Temperature increase can trigger localized failure and cracking in unsaturated porous media. This article investigates the shear banding and cracking in unsaturated porous media under non-isothermal conditions through a thermo–hydro–mechanical (THM) periporomechanics (PPM) paradigm. PPM is a nonlocal formulation of classical poromechanics using integral equations, which is robust in simulating continuous and discontinuous deformation in porous media. As a new contribution, we formulate a nonlocal THM constitutive model for unsaturated porous media in the PPM paradigm in this study. The THM meshfree paradigm is implemented through an explicit Lagrangian meshfree algorithm. The return mapping algorithm is used to implement the nonlocal THM constitutive model numerically. Numerical examples are presented to assess the capability of the proposed THM mesh-free paradigm for modeling shear banding and cracking in unsaturated porous media under non-isothermal conditions. The numerical results are examined to study the effect of temperature variations on the formation of shear banding and cracking in unsaturated porous media.