Light field image super-resolution based on dual learning and deep Fourier channel attention.

Light field (LF) imaging has gained significant attention in the field of computational imaging due to its unique capability to capture both spatial and angular information of a scene. In recent years, super-resolution (SR) techniques based on deep learning have shown considerable advantages in enhancing LF image resolution. However, the inherent challenges of obtaining rich structural information and reconstructing complex texture details persist, particularly in scenarios where spatial and angular information are intricately interwoven. This Letter introduces a novel, to the best of our knowledge, approach for Disentangling LF Image SR Network (DLISN) by leveraging the synergy of dual learning and Fourier channel attention (FCA) mechanisms. Dual learning strategies are employed to enhance reconstruction results, addressing limitations in model generalization caused by the difficulty in acquiring paired datasets in real-world LF scenarios. The integration of FCA facilitates the extraction of high-frequency information associated with different structures, contributing to improved spatial resolution. Experimental results consistently demonstrate superior performance in enhancing the resolution of LF images.

Mapping annual 10-m soybean cropland with spatiotemporal sample migration.

China, as the world's biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices' temporal characteristics. This method uses a feature space of six integrals from the crops' phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.

Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence.

The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. To address this issue, we propose a 3D maize modeling method based on computational intelligence. An initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture. The subsequent model considers the 3D phytomers of maize as intelligent agents. The aim is to maximize the ratio of sunlit leaf area, and by iteratively modifying the azimuth angle of the 3D phytomers, a 3D maize canopy model that maximizes light resource interception can be constructed. Additionally, the method incorporates a reflective approach to optimize the canopy and utilizes a mesh deformation technique for detecting and responding to leaf collisions within the canopy. Six canopy models of 2 varieties plus 3 planting densities was constructed for validation. The average R2 of the difference in azimuth angle between adjacent leaves is 0.71, with a canopy coverage error range of 7% to 17%. Another 3D maize canopy model constructed using 12 distinct density gradients demonstrates the proportion of leaves perpendicular to the row direction increases along with the density. The proportion of these leaves steadily increased after 9 × 104 plants ha-1. This study presents a 3D modeling method for the maize canopy. It is a beneficial exploration of swarm intelligence on crops and generates a new way for exploring efficient resources utilization of crop canopies.

A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data.

Submerged aquatic vegetation (SAV) plays a fundamental ecological role in mediating carbon cycling within lakes, and its biomass is essential to assess the carbon sequestration potential of lake ecosystems. Remote sensing (RS) offers a powerful tool for large-scale SAV biomass retrieval. Given the underwater location of SAV, the spectral signal in RS data often exhibits weakness, capturing primarily horizontal structure rather than volumetric information crucial for biomass assessment. Fortunately, easily-measured SAV coverage can serve as an intermediary variable for difficultly-quantified SAV biomass inversion. Nevertheless, obtaining enough SAV coverage samples matching satellite image pixels for robust model development remains problematic. To overcome this challenge, we employed a UAV to acquire high-precision data, thereby replacing manual SAV coverage sample collection. In this study, we proposed an innovative strategy integrating unmanned aerial vehicle (UAV) and satellite data to invert large-scale SAV coverage, and subsequently estimate the biomass of the dominant SAV population (Potamogeton pectinatus) in Ulansuhai Lake. Firstly, a coverage-biomass model (R2 = 0.93, RMSE = 0.8 kg/m2) depicting the relationship between SAV coverage and biomass was developed. Secondly, in a designed experimental area, a high-precision multispectral image was captured by a UAV. Based on the Normalized Difference Water Index (NDWI), the UAV-based image was classified into non-vegetated and vegetated areas, thereby generating an SAV distribution map. Leveraging spatial correspondence between satellite pixels and the UAV-based SAV distribution map, the proportion of SAV within each satellite pixel, referred to as SAV coverage, was computed, and a coverage sample set matched with satellite pixels was obtained. Subsequently, based on the sample set, a satellite-scale SAV coverage estimation model (R2 = 0.78, RMSE = 14.05 %) was constructed with features from Sentinel-1 and Sentinel-2 data by XGBoost algorithm. Finally, integrating the coverage-biomass model with the obtained coverage inversion results, fresh biomass of SAV in Ulansuhai Lake was successfully estimated to be approximately 574,600 tons.

Effect of Probiotic Supplementation on the Gut Microbiota Composition of Infants Delivered by Cesarean Section: An Exploratory, Randomized, Open-label, Parallel-controlled Trial.

BACKGROUND: Infants born via cesarean section (CS) are at an increased risk of immune-related diseases later in life, potentially due to altered gut microbiota. Recent research has focused on the administration of probiotics in the prevention of gut microbiota dysbiosis in neonates delivered by CS. This study was performed to investigate the effects of probiotic supplementation on the gut microbiota of CS-delivered infants. METHODS: Thirty full-term neonates delivered by CS were randomized into the intervention (supplemented orally with a probiotic containing Bifidobacterium longum, Lactobacillus acidophilus, and Enterococcus faecalis for 2 weeks) and control groups. Stool samples were collected at birth and 2 weeks and 42 days after birth. The composition of the gut microbiota was analyzed using 16S rRNA sequencing technology. RESULTS: The applied bacterial strains were abundant in the CS-delivered infants supplemented with probiotics. Probiotics increased the abundance of some beneficial bacteria, such as Bacteroides, Acinetobacter, Veillonella, and Faecalibacterium. Low colonization of Klebsiella, a potentially pathogenic bacterium, was observed in the intervention group. CONCLUSIONS: Our results showed that probiotics supplemented immediately after CS enriched the gut microbiota composition and altered the pattern of early gut colonization. TRIAL REGISTRATION: registration number NCT05086458.

Migration risk of fall armyworm (Spodoptera frugiperda) from North Africa to Southern Europe.

With the development of globalization and agriculture trade, as well as its own strong migratory capacity, fall armyworm (FAW) (Spodoptera frugiperda) (J.E. Smith) has invaded more than 70 countries, posing a serious threat to the production of major crops in these areas. FAW has now also been detected in Egypt in North Africa, putting Europe, which is separated from it only by the Mediterranean Sea, at high risk of invasion. Therefore, this study integrated multiple factors of insect source, host plant, and environment to provide a risk analysis of the potential trajectories and time periods of migration of FAW into Europe in 2016~2022. First, the CLIMEX model was used to predict the annual and seasonal suitable distribution of FAW. The HYSPLIT numerical trajectory model was then used to simulate the possibility of the FAW invasion of Europe through wind-driven dispersal. The results showed that the risk of FAW invasion between years was highly consistent (P<0.001). Coastal areas were most suitable for the expansion of the FAW, and Spain and Italy had the highest risk of invasion, with 39.08% and 32.20% of effective landing points respectively. Dynamic migration prediction based on spatio-temporal data can enable early warning of FAW, which is important for joint multinational pest management and crop protection.

Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion.

Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson's correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses.

Detection of amylase activity and moisture content in rice by reflectance spectroscopy combined with spectral data transformation.

In this study, reflectance spectroscopy was used to achieve rapid and non-destructive detection of amylase activity and moisture content in rice. Since rice husk can interfere with spectral measurements, spectral data transformation was used to remove the husk interference. Reflectance spectra of rice were transformed by direct standardization, convolutional autoencoder network, and kernel regression (KR). Then, random frog and elliptical envelope were adopted to select effective wavelengths, and partial least squares regression (PLSR) and support vector regression were used to establish analysis models. The optimal transformation was from KR, and PLSR and effective wavelengths of the transformed spectra obtained excellent performance with coefficient of determination of test of 0.6987 and 0.8317 and root-mean-square error of test of 0.3359 and 2.2239, respectively. The result was better than that of the rice spectra and was close to that of the husked rice spectra. When the moisture content was integrated into the regression model of amylase activity, a better result was obtained. Thus, the proposed method can detect amylase activity and moisture content in rice accurately.

Fecal microbiota transplantation improves VPA-induced ASD mice by modulating the serotonergic and glutamatergic synapse signaling pathways.

Autism spectrum disorder (ASD) is a complex behavioral disorder diagnosed by social interaction difficulties, restricted verbal communication, and repetitive behaviors. Fecal microbiota transplantation (FMT) is a safe and efficient strategy to adjust gut microbiota dysbiosis and improve ASD-related behavioral symptoms, but its regulatory mechanism is unknown. The impact of the microbiota and its functions on ASD development is urgently being investigated to develop new therapeutic strategies for ASD. We reconstituted the gut microbiota of a valproic acid (VPA)-induced autism mouse model through FMT and found that ASD is in part driven by specific gut dysbiosis and metabolite changes that are involved in the signaling of serotonergic synapse and glutamatergic synapse pathways, which might be associated with behavioral changes. Further analysis of the microbiota showed a profound decrease in the genera Bacteroides and Odoribacter, both of which likely contributed to the regulation of serotonergic and glutamatergic synapse metabolism in mice. The engraftment of Turicibacter and Alistipes was also positively correlated with the improvement in behavior after FMT. Our results suggested that successful transfer of the gut microbiota from healthy donors to ASD mice was sufficient to improve ASD-related behaviors. Modulation of gut dysbiosis by FMT could be an effective approach to improve ASD-related behaviors in patients.

Efficacy of quadratus lumborum block on postoperative pain and side effects in patients who underwent urological surgery: A meta-analysis.

BACKGROUND: Ultrasound-guided quadratus lumborum block (QLB) is considered a novel nerve block for postoperative pain control. However, its efficacy after urological surgery remains unclear. OBJECTIVES: The purpose of the current meta-analysis was to evaluate the effects of the QLB block versus control (placebo or no injection) on postoperative pain and other adverse outcomes after urological surgery, providing extensive evidence of whether quadratus lumborum block is suitable for pain management after urological surgery. STUDY DESIGN: Systematic review with meta-analysis of randomized clinical trials. METHODS: We searched PubMed, Cochrane Library, Embase, Web of Science, and ClinicalTrials.gov to collect studies investigating the effects of QLB on analgesia after urological surgery. The primary outcomes included visual analog scale (VAS) at rest and during movement, 24-h postoperative morphine consumption, and the incidence of postoperative nausea and vomiting (PONV). RESULTS: Overall, 13 randomized controlled trials (RCTs) were reviewed, including 751 patients who underwent urological surgery. The QLB group exhibited a lower VAS score postoperatively at rest or on movement at 0, 6, 12, and 24 h, with less 24-h postoperative morphine consumption and lower incidence of PONV. LIMITATIONS: Although the result is stable, heterogeneity exists in the current research. CONCLUSIONS: QLB exhibited a favorable effect of postoperative analgesia with reduced postoperative complications at rest or during movement after urological surgery. However, it is still a novel technology at a primary stage, which needs further research to develop.

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion.

Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.

The Role of Dexmedetomidine in Tumor-Progressive Factors in the Perioperative Period and Cancer Recurrence: A Narrative Review.

Dexmedetomidine, a specific α2 adrenergic receptor agonist, is highly frequently used in the perioperatively for its favorable pharmacology, such as mitigating postoperative cognitive dysfunction. Increasing attention has been recently focused on the effect of whether dexmedetomidine influences cancer recurrence, which urges the discussion of the role of dexmedetomidine in tumor-progressive factors. The pharmacologic characteristics of dexmedetomidine, the tumor-progressive factors in the perioperative period, and the relationships between dexmedetomidine and tumor-progressive factors were described in this review. Available evidence suggests that dexmedetomidine could reduce the degree of immune function suppression, such as keeping the number of CD3+ cells, NK cells, CD4+/CD8+ ratio, and Th1/Th2 ratio stable and decreasing the level of proinflammatory cytokine (interleukin 6 and tumor necrosis factor-alpha) during cancer operations. However, dexmedetomidine exhibits different roles in cell biological behavior depending on cancer cell types. The conclusions on whether dexmedetomidine would influence cancer recurrence could not be currently drawn for the lack of strong clinical evidence. Therefore, this is still a new area that needs further exploration.

Multi-scale convolution neural network with residual modules for determination of drugs in human hair using surface-enhanced Raman spectroscopy with a gold nanorod film self-assembled by inverted evaporation.

Detection of illegal drug users is crucial in controlling drug-related crimes, reducing drug prevalence, and protecting human lives to ensure social stability. In this study, surface-enhanced Raman spectroscopy (SERS) and deep learning networks were employed to determine methamphetamine, ketamine, and morphine in human hair. Drugs were obtained from hair through alkaline hydrolysis and liquid-liquid extraction, and gold nanorods were employed to prepare the self-assembled film as the SERS substrate by inverted evaporation. The film showed good uniformity and excellent sensitivity, with a relative standard deviation of 15.6% and a detection limit of at least 10-10 M in the SERS detection of crystal violet. The spectra of methamphetamine, ketamine, and morphine at 0.05-1.0, 0.1-2.0, and 0.1-2.0 ng/mg were obtained, and the three drugs could be detected. Inception, a multi-scale feature extraction network, was combined with residual modules (Inception-ResNet) to develop the identification models of drugs, and the effect of spectral input form as a vector or matrix was explored. Inception-ResNet with input form of matrix outweighed other methods with 100.00%, 100.00%, and 99.23% accuracies in the training, validation, and prediction sets, respectively. In brief, SERS and Inception-ResNet with the spectra in matrix form provide an efficient and accurate determination of drugs in human hair, enabling the retrospective evaluation of drug use, and the method will be anticipated to detect excitant, poison, and toxic chemicals in human hair.

Highly Transparent and Flexible Zn-Ti3C2Tx MXene Hybrid Capacitors.

With the development of transparent and wearable electronic devices, energy supply units with high transmittance and flexibility, long cycle life, and high power and energy density are urgently needed. Zinc ion hybrid capacitors (ZIHCs) combined with the advantages of both supercapacitors and zinc ion batteries are promising energy supply components in the abovementioned devices. In addition, the preparation of multifunctional devices has become a trend for the need of space- and resource-saving. Therefore, obtaining ZIHCs with high transmittance and exploring their potential applications are meaningful challenges. Herein, a transparent and flexible ZIHC composed of a patterned zinc foil anode, transparent MXene cathode, and ZnSO4-polyacrylamide (PAM) hydrogel electrolyte is designed and realized. The ZIHC exhibits a superior capacitance of 318 µF cm-2 (5 mV s-1) with 94% transmittance and retains 76% of the initial capacitance after 10,000 charge-discharge cycles. It also shows excellent flexibility, i.e., its capacitance has no obvious attenuation under different bending states. Interestingly, the leakage current of the ZIHC is highly sensitive to electric fields, which shows potential application in electric field detection. This work presents a method to realize the multifunctional ZIHC with electric field sensing function for transparent and flexible wearable devices in the future.

Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network.

Electronic nose (E-nose) and hyperspectral image (HSI) were combined to evaluate mutton total volatile basic nitrogen (TVB-N), which is a comprehensive index of freshness. The response values of 10 E-nose sensors were collected, and seven responsive sensors were screened via histogram statistics. Reflectance spectra and image features were extracted from HSI images, and the effective variables were selected through random frog and Pearson correlation analyses. With multi-source features, an input-modified convolution neural network (IMCNN) was constructed to predict TVB-N. The seven E-nose sensors, spectra of effective wavelengths (EWs), and five important image features were combined with IMCNN to achieve the best result, with the root mean square error, correlation coefficient, and ratio of performance deviation of the prediction set of 3.039 mg/100 g, 0.920, and 3.59, respectively. Hence, the proposed method furnishes an approach to accurately analyze mutton freshness and provide a technical basis for investigation of other meat qualities.

Alterations of Gut Mycobiota Profiles in Adenoma and Colorectal Cancer.

Accumulating evidence indicates that gut microbiota dysbiosis contributes to colorectal cancer and adenoma. However, a few studies revealed the altered gut mycobiota architecture in colorectal cancer. The present study characterized the gut mycobiota profiles in adenoma and colorectal cancer patients by metagenomic sequencing. Malassezia restricta increased, while Leucoagaricus_sp_SymCcos and fungal_sp_ARF18 significantly decreased in adenoma. Phanerochaete_chrysosporium, Lachancea_waltii, and Aspergillus_rambellii were the top 3 fungi that were significantly enriched in colorectal cancer, while Candida_versatilis, Pseudocercospora_pini_densiflorae, and Candida_sp_JCM_15000 were dominant in the healthy controls. Thirteen fungi, ranked as critical biomarkers in diagnosing colorectal cancer, showed positive associations among all samples. Lachancea_waltii and Phanerochaete_chrysosporium showed the most significant association within CRC. The values of area under the receiver-operating characteristics curve (AUROC) of selected 13 mycobiota were 0.926 in the training model and 0.757 in the 10-fold validation model. Our study provided a reliable investigation of the alterations of gut mycobiota in the development of colorectal cancer and established a convincing diagnostic model for colorectal cancer, which might improve the treatment strategy for colorectal cancer in the future.

Determination of adulteration in wheat flour using multi-grained cascade forest-related models coupled with the fusion information of hyperspectral imaging.

Wheat flour (WF) is a common ingredient in staple foods. However, the presence of intentional or unintentional adulterants makes it difficult to guarantee WF quality. Multi-grained cascade forest (gcForest) model, a non-neural network deep learning structure, fused with image-spectra features from hyperspectral imaging (HSI) was employed for detecting adulterant type (peanut, walnut, or benzoyl peroxide) and the corresponding concentration (0.03%, 0.05%, 0.1%, 0.5%, 1%, and 2%). Based on the spectra of full wavelength and effective wavelength (EW) from hyperspectral images of WF samples, the gcForest-related models exhibited high performance (lowest ACCP = 92.45%) and stability (lowest area under the curve = 0.9986). Furthermore, the fusion of the EW and the image features extracted by the symmetric all convolutional neural network (SACNN) was used to establish the gcForest-related models. The maximum accuracy improvement of the fusion feature model relative to the single spectral model and the image model was 2.45% and 44.37%, respectively. The results indicate that the gcForest-related model, combined with the image-spectra fusion feature of HSI, provides an effective tool for detection in food and agriculture.

Reflectance spectroscopy with operator difference for determination of behenic acid in edible vegetable oils by using convolutional neural network and polynomial correction.

A novel polynomial correction method, order-adaptive polynomial correction (OAPC), was proposed to correct reflectance spectra with operator differences, and convolutional neural network (CNN) was used to develop analysis model to predict behenic acid in edible oils. With application of OAPC, CNN performed well with coefficient of determination of correction (R2cor) of 0.8843 and root mean square error of correction (RMSEcor) of 0.1182, outperforming partial least squares regression, support vector regression and random forest with OAPC, as well as the cases without OAPC. Based on 16 effective wavelengths selected by combination of bootstrapping soft shrinkage, random frog and Pearson's correlation, CNN and OAPC exhibited excellent performance with R2cor of 0.9560 and RMSEcor of 0.0730. Meanwhile, only 5% correction samples were selected by Kennard-Stone for OAPC. Overall, the proposed method could alleviate the impact of operator differences on spectral analysis, thereby providing potential to correct differences from measurement instruments or environments.

Metagenomic and metabolomic remodeling in nonagenarians and centenarians and its association with genetic and socioeconomic factors.

A better understanding of the biological and environmental variables that contribute to exceptional longevity has the potential to inform the treatment of geriatric diseases and help achieve healthy aging. Here, we compared the gut microbiome and blood metabolome of extremely long-lived individuals (94-105 years old) to that of their children (50-79 years old) in 116 Han Chinese families. We found extensive metagenomic and metabolomic remodeling in advanced age and observed a generational divergence in the correlations with socioeconomic factors. An analysis of quantitative trait loci revealed that genetic associations with metagenomic and metabolomic features were largely generation-specific, but we also found 131 plasma metabolic quantitative trait loci associations that were cross-generational with the genetic variants concentrated in six loci. These included associations between FADS1/2 and arachidonate, PTPA and succinylcarnitine and FLVCR1 and choline. Our characterization of the extensive metagenomic and metabolomic remodeling that occurs in people reaching extreme ages may offer new targets for aging-related interventions.