Photoredox-catalyzed alkylarylation of activated alkenes via a ring-opening/Truce-Smiles rearrangement cascade.

A photoredox-catalyzed alkylarylation of activated alkenes via a radical C-C bond cleavage/Truce-Smiles rearrangement cascade is developed. The protocol features mild and redox-neutral conditions, broad substrate scope and excellent functional group compatibility, providing a facile and efficient approach to the long-chain distal keto-amides with all-carbon quaternary centers at the alpha position.

Multi-Omics Study on Molecular Mechanisms of Single-Atom Fe-Doped Two-Dimensional Conjugated Phthalocyanine Framework for Photocatalytic Antibacterial Performance.

Currently, photocatalysis of the two-dimensional (2D) conjugated phthalocyanine framework with a single Fe atom (CPF-Fe) has shown efficient photocatalytic activities for the removal of harmful effluents and antibacterial activity. Their photocatalytic mechanisms are dependent on the redox reaction-which is led by the active species generated from the photocatalytic process. Nevertheless, the molecular mechanism of CPF-Fe antimicrobial activity has not been sufficiently explored. In this study, we successfully synthesized CPF-Fe with great broad-spectrum antibacterial properties under visible light and used it as an antibacterial agent. The molecular mechanism of CPF-Fe against Escherichia coli and Salmonella enteritidis was explored through multi-omics analyses (transcriptomics and metabolomics correlation analyses). The results showed that CPF-Fe not only led to the oxidative stress of bacteria by generating large amounts of h+ and ROS but also caused failure in the synthesis of bacterial cell wall components as well as an osmotic pressure imbalance by disrupting glycolysis, oxidative phosphorylation, and TCA cycle pathways. More surprisingly, CPF-Fe could disrupt the metabolism of amino acids and nucleic acids, as well as inhibit their energy metabolism, resulting in the death of bacterial cells. The research further revealed the antibacterial mechanism of CPF-Fe from a molecular perspective, providing a theoretical basis for the application of CPF-Fe photocatalytic antibacterial nanomaterials.

Accurate and fast localization of EBSD pattern centers for screen moving technology.

The authors of this study develop an accurate and fast method for the localization of the pattern centers (PCs) in the electron backscatter diffraction (EBSD) technique by using the model of deformation of screen moving technology. The proposed algorithm is divided into two steps: (a) Approximation: We use collinear feature points to obtain the initial value of the coordinates of the PC and the zoom factor. (b) Subdivision: We then construct a deformation function containing the three parameters to be solved, select a large region for global registration, use the inverse compositional Gauss-Newton (ICGN) to optimize the objective function, and obtain the results of iteration of the PC and the zoom factor. The proposed algorithm was applied to simulated patterns, and yielded an accuracy of measurement of the PCs that was better than 4.6×10-6 of their resolution while taking only 0.2 s for computations. Moreover, the proposed algorithm has a large radius of convergence that makes it robust to the initial estimate. We also discuss the influence of factors of mechanical instability on its results of calibration during the insertion of the detector, and show that errors in measurements caused by the tilt motion of the camera are related only to the tilt angle of its motion and the detector distance, and are unrelated to the distance moved by it.

Exercise-induced Musclin determines the fate of fibro-adipogenic progenitors to control muscle homeostasis.

The effects of exercise on fibro-adipogenic progenitors (FAPs) are unclear, and the direct molecular link is still unknown. In this study, we reveal that exercise reduces the frequency of FAPs and attenuates collagen deposition and adipose formation in injured or disused muscles through Musclin. Mechanistically, Musclin inhibits FAP proliferation and promotes apoptosis in FAPs by upregulating FILIP1L. Chromatin immunoprecipitation (ChIP)-qPCR confirms that FoxO3a is the transcription factor of FILIP1L. In addition, the Musclin/FILIP1L pathway facilitates the phagocytosis of apoptotic FAPs by macrophages through downregulating the expression of CD47. Genetic ablation of FILIP1L in FAPs abolishes the effects of exercise or Musclin on FAPs and the benefits on the reduction of fibrosis and fatty infiltration. Overall, exercise forms a microenvironment of myokines in muscle and prevents the abnormal accumulation of FAPs in a Musclin/FILIP1L-dependent manner. The administration of exogenous Musclin exerts a therapeutic effect, demonstrating a potential therapeutic approach for muscle atrophy or acute muscle injury.

Aphid Recognition and Counting Based on an Improved YOLOv5 Algorithm in a Climate Chamber Environment.

Due to changes in light intensity, varying degrees of aphid aggregation, and small scales in the climate chamber environment, accurately identifying and counting aphids remains a challenge. In this paper, an improved YOLOv5 aphid detection model based on CNN is proposed to address aphid recognition and counting. First, to reduce the overfitting problem of insufficient data, the proposed YOLOv5 model uses an image enhancement method combining Mosaic and GridMask to expand the aphid dataset. Second, a convolutional block attention mechanism (CBAM) is proposed in the backbone layer to improve the recognition accuracy of aphid small targets. Subsequently, the feature fusion method of bi-directional feature pyramid network (BiFPN) is employed to enhance the YOLOv5 neck, further improving the recognition accuracy and speed of aphids; in addition, a Transformer structure is introduced in front of the detection head to investigate the impact of aphid aggregation and light intensity on recognition accuracy. Experiments have shown that, through the fusion of the proposed methods, the model recognition accuracy and recall rate can reach 99.1%, the value mAP@0.5 can reach 99.3%, and the inference time can reach 9.4 ms, which is significantly better than other YOLO series networks. Moreover, it has strong robustness in actual recognition tasks and can provide a reference for pest prevention and control in climate chambers.

Astragaloside IV promotes angiogenesis by targeting SIRT7/VEGFA signaling pathway to improve brain injury after cerebral infarction in rats.

Cerebral infarction (CI) has become one of the leading causes of death and acquired disability worldwide. Astragaloside IV (AST IV), one of the basic components of Astragalus membranaceus, has a protective effect on CI. However, the underlying mechanism has not been conclusively elucidated. Therefore, this study aims to explore the underlying mechanism of AST IV improving brain injury after CI. Middle cerebral artery occlusion (MCAO) and oxygen-glucose deprivation/reoxygenation (OGD/R) were used to simulate cerebral infarction injury in SD rats and HUVECs cells. Neurologic score, Evans blue, TTC and HE staining were used to observe brain injury in rats. Cell viability and migration were measured in vitro. Angiogenesis was detected by immunofluorescence and tube formation assay, and cell cycle was detected by flow cytometry. Western blot was used to find the expression of related proteins. Molecular docking, virtual mutation, site-directed mutagenesis, MST, and lentivirus silencing were used for target validation. The results showed that AST IV alleviated neurological impairment and promoted angiogenesis after CI. Moreover, AST IV greatly increased the transcription levels of SIRT6 and SIRT7, but had no effect on SIRT1-SIRT5, and promoted cell viability, migration, angiogenesis and S phase ratio in OGD/R-induced HUVECs. Furthermore, AST IV up-regulated the protein expressions of CDK4, cyclin D1, VEGFA and VEGF2R. Interestingly, AST IV not only bound to SIRT7, but also increased the expression of SIRT7. Silencing SIRT7 by lentivirus neutralizes the positive effects of AST IV. Taken together, the present study revealed that AST IV may improve brain tissue damage after CI by targeting SIRT7/VEGFA signaling pathway to promote angiogenesis.

A fast Fourier convolutional deep neural network for accurate and explainable discrimination of wheat yellow rust and nitrogen deficiency from Sentinel-2 time series data.

Introduction: Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction. Methods: In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series. Results and discussion: The proposed model has been evaluated with ground truth data under both controlled and natural conditions. The results demonstrate that the high-level vector features interpret the influence of the host-stress interaction/response and the proposed model achieves competitive advantages in the detection and discrimination of yellow rust and nitrogen deficiency on Sentinel-2 time series in terms of classification accuracy, robustness, and generalization.

Easy to build, modular and large scale pipe conveying fluid experimental setup.

The pipe conveying fluid is a classic fluid structure interaction experiment. First studied for industrial applications such as liners and pipelines, it became a "paradigm" of non-linear dynamics in the same way as the vertical rotating shaft. Hundreds of papers studying different pipe instabilities and different phenomena with various numerical and analytical methods have been published in the last decades. However, many studies lack the comparison with experimental data to validate the analytical models and numerical simulations. Indeed, designing and building a pipe conveying fluid experimental setup can prove to be a long and a burdensome process. This paper presents an easy to build pipe conveying fluid experimental setup built in the LM2 laboratory at Polytechnique Montréal. Fig. 1 presents the global architecture of this experimental rig. This large scale setup uses relatively high speed cameras to track the pipe in three dimensions. It does not require heavy construction or major plumbing and electrical work. Moreover, it is removable and can be modified easily to observe different phenomena with various large scale pipes or boundary conditions. Lastly, it is relatively inexpensive as it costs less than 20 000 US dollars including all the sensors and acquisition systems.

Pepper leaf disease recognition based on enhanced lightweight convolutional neural networks.

Pepper leaf disease identification based on convolutional neural networks (CNNs) is one of the interesting research areas. However, most existing CNN-based pepper leaf disease detection models are suboptimal in terms of accuracy and computing performance. In particular, it is challenging to apply CNNs on embedded portable devices due to a large amount of computation and memory consumption for leaf disease recognition in large fields. Therefore, this paper introduces an enhanced lightweight model based on GoogLeNet architecture. The initial step involves compressing the Inception structure to reduce model parameters, leading to a remarkable enhancement in recognition speed. Furthermore, the network incorporates the spatial pyramid pooling structure to seamlessly integrate local and global features. Subsequently, the proposed improved model has been trained on the real dataset of 9183 images, containing 6 types of pepper diseases. The cross-validation results show that the model accuracy is 97.87%, which is 6% higher than that of GoogLeNet based on Inception-V1 and Inception-V3. The memory requirement of the model is only 10.3 MB, which is reduced by 52.31%-86.69%, comparing to GoogLeNet. We have also compared the model with the existing CNN-based models including AlexNet, ResNet-50 and MobileNet-V2. The result shows that the average inference time of the proposed model decreases by 61.49%, 41.78% and 23.81%, respectively. The results show that the proposed enhanced model can significantly improve performance in terms of accuracy and computing efficiency, which has potential to improve productivity in the pepper farming industry.

Alkoxyl Radical-Mediated Ring Expansion/1,4-Difunctionalization of 1,3-Enynes upon Copper Catalysis.

A redox-neutral copper-catalyzed cascade reaction involving alkoxyl radical-mediated ring expansion/1,4-difunctionalization of 1,3-enynes was developed, offering a straightforward approach to the tetra-substituted allenes with macrolactone, CN, and CF3 functional groups. Remarkably, incorporation of the NH2 group onto the 1,3-enyne moiety enabled further cyclization to give a unique scaffold containing a lactone and an indole moiety.

Weight progression and adherence to weight gain target in women with vs. without gestational diabetes: a retrospective cohort study.

BACKGROUND: Weight management has been an important component of the service in obstetric care offered to pregnant women. Current gestational weight gain recommendations were primarily for the general obstetric population, raising concern about the applicability to women with gestational diabetes mellitus (GDM). We aimed to assess the difference in weight progression and adherence to the recommended gestational weight gain targets between women with gestational diabetes mellitus (GDM) and women with normal glucose tolerance (NGT). METHODS: This was a hospital-based retrospective study of 56,616 pregnant women (9,430 GDM women and 47,186 NGT women) from Guangzhou between 2017 and 2021. The average change in weight progression was estimated based on serial weight measurements throughout pregnancy, using a mixed effects model with a random intercept to account for repeated measures of the same individual. RESULTS: Women with GDM gained less weight (12.07 [SD 5.20] kg) than women with NGT (14.04 [SD 5.04] kg) throughout pregnancy. Before OGTT, a small difference was observed in the average change in weight progression between the two groups (GDM, 0.44 kg/week vs. NGT, 0.45 kg/week, p < 0.001), however, this gap widened significantly after the test (0.34 vs. 0.50 kg/week, p < 0.001). GDM individuals were identified with an approximately 4-fold increased proportion of insufficient weight gain (41.1% vs. 10.4%) and a 2-fold decreased proportion of excessive weight gain (22.6% vs. 54.2%) compared to NGT individuals. These results were consistently observed across different BMI categories, including underweight (insufficient: 52.7% vs. 19.9%; excessive: 15.6% vs. 35.3%), normal weight (insufficient 38.2% vs. 7.4%; excessive: 22.2% vs. 57.3%), and overweight/obese (insufficient: 43.1% vs. 9.8%; excessive: 30.1% vs. 68.8%). CONCLUSION: Weight progression varied significantly between GDM and NGT individuals, resulting in a substantial difference in identifying insufficient and excessive weight gain between the two groups under current gestational weight gain guidelines.

GP73 enhances the ox-LDL-induced inflammatory response in THP-1 derived macrophages via affecting NLRP3 inflammasome signaling.

BACKGROUND: Atherosclerosis is a chronic inflammatory disease with its molecular basis incompletely understood. Here, we determined whether the Golgi phosphoprotein 73 (GP73), a novel protein highly related to inflammation and disrupted lipid metabolism, was involved in the development of atherosclerosis. METHODS: Public microarray databases of human vascular samples were analyzed for expression patterns. Apolipoprotein-E-gene-deficient (ApoE-/-) mice (8-week-old) were randomly assigned to either a chow diet group or a high-fat diet group. The levels of serum GP73, lipid profiles and key inflammatory cytokines were determined by ELISA. The aortic root plaque was isolated and used for by Oil Red O staining. PMA-differentiated THP-1 macrophages were transfected with GP73 small interfering RNA (siRNA) or infected with adenovirus expressing GP73, and then stimulated with oxidized low density lipoprotein (ox-LDL). The expressions of pro-inflammatory cytokines and signal pathway key targets were determined by ELISA kit and Western blot respectively. In addition, ichloro-dihydro-fluorescein diacetate (DCFH-DA) was used to measure the intracellular ROS levels. RESULTS: The expressions of GP73 and NLRP3 were substantially upregulated in human atherosclerotic lesions. There were significant linear correlations between GP73 and inflammatory cytokines expressions. High-fat diet-induced atherosclerosis and increased levels of plasma inflammatory mediators (IL-1ß, IL-18, and TNF-α) were observed in ApoE-/- mice. Besides, the expressions of GP73 in the aorta and serum were significantly upregulated and positively correlated with the NLRP3 expression. In the THP-1 derived macrophages, ox-LDL treatment upregulated the expressions of GP73 and NLRP3 proteins and activated the inflammatory responses in a concentration-dependent and time-dependent manner. Silencing of GP73 attenuated the inflammatory response and rescued the decreased migration induced by ox-LDL, inhibiting the NLRP3 inflammasome signaling and the ROS and p-NF-κB activation. CONCLUSIONS: We demonstrated that GP73 promoted the ox-LDL-induced inflammation in macrophages by affecting the NF-κB/NLRP3 inflammasome signaling, and may play a role in atherosclerosis.

A cheap metal catalyzed ring expansion/cross-coupling cascade: a new route to functionalized medium-sized and macrolactones.

An efficient alkoxyl radical-triggered ring expansion/cross-coupling cascade was developed under cheap metal catalysis. Through the metal-catalyzed radical relay strategy, a wide range of medium-sized lactones (9-11 membered) and macrolactones (12, 13, 15, 18, and 19-membered) were constructed in moderate to good yields, along with diverse functional groups including CN, N3, SCN, and X groups installed concurrently. Density functional theory (DFT) calculations revealed that reductive elimination of the cycloalkyl-Cu(iii) species is a more favorable reaction pathway for the cross-coupling step. Based on the results of experiments and DFT, a Cu(i)/Cu(ii)/Cu(iii) catalytic cycle is proposed for this tandem reaction.

Measurement of the pattern shifts for HR-EBSD with larger lattice rotations.

High-resolution electron backscattering diffraction (HR-EBSD) was used to measure rotations and elastic strains by matching diffraction patterns based on cross-correlation. However, the subset-based phase correlation algorithm was unable to determine pattern shifts accurately when large rotations occurred. In this paper, a new matching algorithm was proposed to measure pattern shifts and recover the elastic strain and lattice rotation with finite deformation theory. The algorithm was implemented in two steps: (a) Integral pixel matching: The pixel-related information of the Kikuchi patterns was mapped to the original three-dimensional sphere to obtain the image projected in parallel by using the feature points as the pattern center through the transformation of its spatial coordinates. The correlation between the images projected in parallel before and after deformation was then obtained. The locations of the integral pixels were determined by the peaks of the surface of correlation obtained by traversing all pixels in the search area. (b) subpixel refinement: the locations of subpixels were obtained by FAGN with an appropriate shape function involving rotation and translation. The algorithm was applied to dynamic simulated test sets, and its results were compared with those of the first-pass cross-correlation and the second-pass cross-correlation method with remapping. The proposed method was more robust in the case of rotation and solved the problem that displacement vectors could not be accurately measured when a larger lattice rotation occurred. The mean errors of the measured displacement, rotation, and strain components were 0.02 pixel, 0.5×10-4rad, and 1×10-4, respectively. Compared with the second-pass cross-correlation method, the angle of rotation was more precisely extracted.

A New Pest Detection Method Based on Improved YOLOv5m.

Pest detection in plants is essential for ensuring high productivity. Convolutional neural networks (CNN)-based deep learning advancements recently have made it possible for researchers to increase object detection accuracy. In this study, pest detection in plants with higher accuracy is proposed by an improved YOLOv5m-based method. First, the SWin Transformer (SWinTR) and Transformer (C3TR) mechanisms are introduced into the YOLOv5m network so that they can capture more global features and can increase the receptive field. Then, in the backbone, ResSPP is considered to make the network extract more features. Furthermore, the global features of the feature map are extracted in the feature fusion phase and forwarded to the detection phase via a modification of the three output necks C3 into SWinTR. Finally, WConcat is added to the fusion feature, which increases the feature fusion capability of the network. Experimental results demonstrate that the improved YOLOv5m achieved 95.7% precision rate, 93.1% recall rate, 94.38% F1 score, and 96.4% Mean Average Precision (mAP). Meanwhile, the proposed model is significantly better than the original YOLOv3, YOLOv4, and YOLOv5m models. The improved YOLOv5m model shows greater robustness and effectiveness in detecting pests, and it could more precisely detect different pests from the dataset.

A Systematic Review of Linezolid Pharmacokinetics/Pharmacodynamics in Patients Undergoing Continuous Renal Replacement Therapy: Does One Size Fit All?

BACKGROUND: Selection of the optimal antimicrobial posology in critically ill patients remains a challenge, especially in patients with sepsis who undergo continuous renal replacement therapy (CRRT). This systematic review aimed to analyze factors that influence the extracorporeal removal of linezolid. METHODS: A comprehensive search was performed to identify studies published up to March 2022 in PubMed, MEDLINE and EMBASE databases. Studies involving adults receiving CRRT and treatment with linezolid were considered eligible if the CRRT setting and linezolid's pharmacokinetic parameters were clearly mentioned. RESULTS: Six out of 110 potentially relevant studies were included. A total of 101 treatments were identified among 97 enrolled patients. Our analysis showed that continuous veno-venous hemodiafiltration (CVVHDF) was the most frequential used modality (52 cases). Despite distribution volume, the clearance (CL) of linezolid in these studies had large variability. Extracorporeal linezolid removal may be markedly impacted by CRRT dose. There is significant between-subject variability in the probability of pharmacokinetics-pharmacodynamics (PK-PD) target attainment of patients treated with CRRT. CONCLUSION: Dose adjustment, shortening the dosing interval, and continuous infusion were proposed as regimen optimization. Therapeutic drug monitoring is recommended due to the high variability of linezolid exposure among patients with CRRT, specifically for those whose bodyweight is high, renal function is preserved, and the MIC of infection bacteria is above 2 µg/mL.

An Approach for Autonomous Feeding Robot Path Planning in Poultry Smart Farm.

In order to solve the problems of poor feeding environment, untimely feeding and high labor demand in poultry smart farms, the development of feeding robots is imminent, while the research on path planning algorithms is an important part of developing feeding robots. The energy consumption of the feeding robot is one of the important elements of concern in the process of path planning. In this study, the shortest path does not mean that the feeding robot consumes the least energy, because the total mass of the feeding robot keeps changing during the feeding process. It is necessary to find the most suitable path so that the feeding robot consumes the lowest amount of energy during the feeding process. A branch and bound algorithm to calculate the minimum energy consumption travel path for small-scale buckets lacking feed is proposed. The lower bound of the branch and bound on the energy consumption is obtained by the approach of preferred selection of the set of shortest edges combined with the sequence inequality, and the upper bound could be obtained based on Christofides's Heuristic algorithm. A double-crossover operator genetic algorithm based on an upper bound on energy consumption for large-scale buckets lacking feed is proposed, and different crossover operations are performed according to the relationship between the fitness value and the upper bound of energy consumption in order to find a better path. The experiment results show that the approach proposed in this study is efficient; for small-scale buckets lacking feed, a branch and bound algorithm could calculate the minimum energy consumption path of 17 points in 300 s, and for large-scale buckets lacking feed, a double-crossover operator genetic algorithm based on an upper bound on energy consumption could calculate the minimum energy consumption travel path within 30 points in 60 s. The result is more accurate compared to the genetic algorithm with a single crossover operator.

Gut microbiota profiling revealed the regulating effects of salidroside on iron metabolism in diabetic mice.

Background: Diabetes is a common metabolic disease that is associated with gut microbiota dysbiosis and iron metabolism. Salidroside (SAL) is the main ingredient of the traditional Chinese herb Rhodiola, previous studies have shown that SAL could reshape the gut microbiota and limit iron accumulation. Therefore, it is possible that SAL can act as an alternative therapy for diabetes, and its underlying mechanism is worth exploring. Methods: SAL was used to treat diabetic db/db mice. Serum glucose and iron levels and the histopathology of myocardial fibres were evaluated. The gut microbiota composition was determined by 16S rRNA Illumina sequencing technology. Results: Treatment with SAL significantly reduced blood glucose and ameliorated diabetic cardiomyopathy in diabetic db/db mice, which was accompanied by inhibited ferroptosis and iron accumulation. Furthermore, the 16S rRNA sequencing results showed that SAL induced a change in the gut microbiota composition. Overall, SAL could increase the proportion of probiotic bacteria and decrease Lactobacillus to improve gut microbiota. Specifically, SAL increased the ratio of Bacteroidetes to Firmicutes in diabetic mice. The most significant biomarker was the genus Lactobacillus between the MD group and the SAL group. In addition, COG and KEGG analyses suggested that SAL mainly participated in nutrient metabolism, among them iron metabolism was associated with the abundance of Lactobacillus. Conclusions: SAL could reduce the glucose level and protect against diabetic cardiomyopathy in diabetic mice, which might be mediated by the change in the gut microbiota and the regulation of iron metabolism. The findings suggested that SAL was a promising complementary option for diabetes therapy.