Lightweight monocular depth estimation using a fusion-improved transformer.

The existing deep estimation networks often overlook the issue of computational efficiency while pursuing high accuracy. This paper proposes a lightweight self-supervised network that combines convolutional neural networks (CNN) and Transformers as the feature extraction and encoding layers for images, enabling the network to capture both local geometric and global semantic features for depth estimation. First, depth-separable convolution is used to construct a dilated convolution residual module based on a shallow network to improve the shallow CNN feature extraction receptive field. In the transformer, a multidepth separable convolution head transposed attention module is proposed to reduce the computational burden of spatial self-attention. In the feedforward network, a two-step gating mechanism is proposed to improve the nonlinear representation ability of the feedforward network. Finally, the CNN and transformer are integrated to implement a depth estimation network with a local-global context interaction function. Compared with other lightweight models, this model has fewer model parameters and higher estimation accuracy. It also has better generalizability for different outdoor datasets. Additionally, the inference speed can reach 87 FPS, achieving better real-time performance and accounting for both inference speed and estimation accuracy.

Bacterial Diversity in Sediments from Lianhuan Lake, Northeast China.

Lake microbiota play a crucial role in geochemical cycles, influencing both energy flow and material production. However, the distribution patterns of bacterial communities in lake sediments remain largely unclear. In this study, we used 16S rRNA high-throughput sequencing technology to investigate the bacterial structure and diversity in sediments across different locations (six independent lakes) within Lianhuan Lake and analyzed their relationship with environmental factors. Our findings revealed that both the alpha and beta diversity of sediment bacterial communities varied significantly among the six independent lakes. Furthermore, changes between lakes had a significant impact on the relative abundance of bacterial phyla, such as Pseudomonadota and Chloroflexota. The relative abundance of Pseudomonadota was highest in Habuta Lake and lowest in Xihulu Lake, while Chloroflexota abundance was lowest in Habuta Lake and highest in Tiehala Lake. At the genus level, the relative abundance of Luteitalea was highest in Xihulu Lake compared to the other five lakes, whereas the relative abundances of Clostridium, Thiobacillus, and Ilumatobacter were highest in Habuta Lake. Mantel tests and heatmaps revealed that the relative abundance of Pseudomonadota was significantly negatively correlated with pH, while the abundance of Chloroflexota was significantly positively correlated with total phosphorus and total nitrogen in water, and negatively correlated with electrical conductivity. In conclusion, this study significantly enhances our understanding of bacterial communities in the different lakes within the Lianhuan Lake watershed.

Search and match across spatial omics samples at single-cell resolution.

Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match and visualize both similarities and differences of molecular features in space across multiple samples is lacking. To address this, we introduce CAST (cross-sample alignment of spatial omics), a deep graph neural network-based method enabling spatial-to-spatial searching and matching at the single-cell level. CAST aligns tissues based on intrinsic similarities of spatial molecular features and reconstructs spatially resolved single-cell multi-omic profiles. CAST further allows spatially resolved differential analysis (∆Analysis) to pinpoint and visualize disease-associated molecular pathways and cell-cell interactions and single-cell relative translational efficiency profiling to reveal variations in translational control across cell types and regions. CAST serves as an integrative framework for seamless single-cell spatial data searching and matching across technologies, modalities and sample conditions.

Aspirin in the Form of Microneedle Repairs DNA and Reduces Inflammation in Persistent Skin Damage.

Skin damage caused by chemical corrosion is currently one of the common skin diseases and poisoning symptoms, with nitrogen mustard compounds causing the most persistent and severe damage. These chemicals penetrate the top layer of the skin, enter the dermis, and cause DNA damage, oxidative stress, and inflammation. However, to date, no effective drug treatment has been found. Even the potential antidotes could not effectively penetrate the top layer of the skin to exert their effects due to the skin barrier. To address this problem, an innovative transdermal drug delivery strategy based on aspirin microneedles was proposed. The classic medicine aspirin was first discovered not only to reduce inflammation and oxidative stress but also to promote DNA repair and reduce DNA damage. The aspirin microneedles directly delivered the drug to the damaged area, released aspirin through the skin barrier, and exhibited good biocompatibility. These findings indicate that aspirin microneedles have great potential for promoting wound healing and broad application prospects.

Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks.

Characterizing the transcriptional and translational gene expression patterns at the single-cell level within their three-dimensional (3D) tissue context is essential for revealing how genes shape tissue structure and function in health and disease. However, most existing spatial profiling techniques are limited to 5-20 µm thin tissue sections. Here, we developed Deep-STARmap and Deep-RIBOmap, which enable 3D in situ quantification of thousands of gene transcripts and their corresponding translation activities, respectively, within 200-µm thick tissue blocks. This is achieved through scalable probe synthesis, hydrogel embedding with efficient probe anchoring, and robust cDNA crosslinking. We first utilized Deep-STARmap in combination with multicolor fluorescent protein imaging for simultaneous molecular cell typing and 3D neuron morphology tracing in the mouse brain. We also demonstrate that 3D spatial profiling facilitates comprehensive and quantitative analysis of tumor-immune interactions in human skin cancer.

Differentiating lung neuroendocrine neoplasms from tumor-like infection using CT in patients with ectopic ACTH syndrome.

OBJECTIVES: Pulmonary neuroendocrine neoplasms (NENs) are the most frequent cause of ectopic adrenocorticotropic hormone syndrome (EAS); lung infection is common in EAS. An imaging finding of infection in EAS patients can mimic NENs. This retrospective study investigated EAS-associated pulmonary imaging indicators. METHODS: Forty-five pulmonary NENs and 27 tumor-like infections from 59 EAS patients (45 NEN and 14 infection patients) were included. Clinical manifestations, CT features, 18F-FDG, or 68Ga-DOTATATE-PET/CT images and pathological results were collected. RESULTS: High-sensitivity C-reactive protein (p < 0.001) and expectoration occurrence (p = 0.04) were higher, and finger oxygen saturation (p = 0.01) was lower in the infection group than the NENs group. Higher-grade NENs were underrepresented in our cohort. Pulmonary NENs were solitary primary tumors, 80% of which were peripheral tumors. Overlying vessel sign and airway involvement were more frequent in the NENs group (p < 0.001). Multifocal (p = 0.001) and peripheral (p = 0.02) lesions, cavity (p < 0.001), spiculation (p = 0.01), pleural retraction (p < 0.001), connection to pulmonary veins (p = 0.02), and distal atelectasis or inflammatory exudation (p = 0.001) were more frequent in the infection group. The median CT value increment between the non-contrast and arterial phases was significantly higher in NENs lesions (p < 0.001). Receiver operating characteristic curve analysis indicated a moderate predictive ability at 48.3 HU of delta CT value (sensitivity, 95.0%; specificity, 54.1%). CONCLUSION: Chest CT scans are valuable for localizing and characterizing pulmonary lesions in rare EAS, thereby enabling prompt differential diagnosis and treatment. CRITICAL RELEVANCE STATEMENT: Thin-slice CT images are valuable for the localization and identification of pulmonary ectopic adrenocorticotropic hormone syndrome lesions, leading to prompt differential diagnosis and effective treatment. KEY POINTS: Lung tumor-like infections can mimic neuroendocrine neoplasms (NENs) in ectopic adrenocorticotropic hormone syndrome (EAS) patients. NENs are solitary lesions, whereas infections are multiple peripheral pseudotumors each with identifying imaging findings. Typical CT signs aid in localization and creating an appropriate differential diagnosis.

Meldonium, as a potential neuroprotective agent, promotes neuronal survival by protecting mitochondria in cerebral ischemia-reperfusion injury.

BACKGROUND: Stroke is a globally dangerous disease capable of causing irreversible neuronal damage with limited therapeutic options. Meldonium, an inhibitor of carnitine-dependent metabolism, is considered an anti-ischemic drug. However, the mechanisms through which meldonium improves ischemic injury and its potential to protect neurons remain largely unknown. METHODS: A rat model with middle cerebral artery occlusion (MCAO) was used to investigate meldonium's neuroprotective efficacy in vivo. Infarct volume, neurological deficit score, histopathology, neuronal apoptosis, motor function, morphological alteration and antioxidant capacity were explored via 2,3,5-Triphenyltetrazolium chloride staining, Longa scoring method, hematoxylin and eosin staining, terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay, rotarod test, transmission electron microscopy and Oxidative stress index related kit. A primary rat hippocampal neuron model subjected to oxygen-glucose deprivation reperfusion was used to study meldonium's protective ability in vitro. Neuronal viability, mitochondrial membrane potential, mitochondrial morphology, respiratory function, ATP production, and its potential mechanism were assayed by MTT cell proliferation and cytotoxicity assay kit, cell-permeant MitoTracker® probes, mitochondrial stress, real-time ATP rate and western blotting. RESULTS: Meldonium markedly reduced the infarct size, improved neurological function and motor ability, and inhibited neuronal apoptosis in vivo. Meldonium enhanced the morphology, antioxidant capacity, and ATP production of mitochondria and inhibited the opening of the mitochondrial permeability transition pore in the cerebral cortex and hippocampus during cerebral ischemia-reperfusion injury (CIRI) in rats. Additionally, meldonium improved the damaged fusion process and respiratory function of neuronal mitochondria in vitro. Further investigation revealed that meldonium activated the Akt/GSK-3ß signaling pathway to inhibit mitochondria-dependent neuronal apoptosis. CONCLUSION: Our study demonstrated that meldonium shows a neuroprotective function during CIRI by preserving the mitochondrial function, thus prevented neurons from apoptosis.

Serum uric acid to high-density lipoprotein cholesterol ratio is a predictor for all-cause and cardiovascular disease mortality in patients with diabetes: Evidence from NHANES 2005-2018.

BACKGROUND AND AIMS: The relationship between uric acid to high-density lipoprotein cholesterol ratio (UHR) and mortality in individuals with diabetes remains uncertain. This study aimed to explore the relationship between serum UHR and all-cause and cardiovascular disease (CVD) mortality in adults with diabetes. METHODS AND RESULTS: A total of 5,665 patients with diabetes were enrolled from the 2005-2018 National Health and Nutrition Examination Survey (NHANES). Mortality data were determined through the National Death Index (NDI) until December 31, 2019. The multivariate hazard ratio (HR) and 95% confidence interval (CI) were examined by Cox proportional risk modeling and threshold effects analysis. Stratified analyses were conducted to identify the populations with high-risk mortality. Among the participants with diabetes, 1,088 all-cause mortality, containing 310 CVD mortality occurred. Following adjustment for multivariate, higher UHR was significantly and nonlinearly associated with increased all-cause mortality (HR 1.02, 95% CI 1.02-1.02) and CVD mortality (HR 1.03, 95% CI 1.03-1.03). Furthermore, a U-shaped relationship between UHR and all-cause and CVD mortality, with a plateau at 12.57% for all-cause mortality and 9.86% for CVD mortality. Below the inflection points, a higher UHR was associated with a 4% reduced risk for all-cause mortality. Conversely, exceeding the inflection points, a 4% higher risk for all-cause and a 3% higher risk for CVD mortality associated with elevated UHR. CONCLUSIONS: Nonlinearity of UHR with all-cause and CVD mortality was observed in adults with diabetes in the United States, with thresholds identified at 12.57% for all-cause and 9.86% for CVD mortality respectively.

[Analysis of Antibiotic Resistance of Bioaerosols from Wastewater Treatment Process].

To explore the prevalence and source of antibiotic resistant genes （ARGs） and pathogenic antibiotic resistant bacteria （PARB） associated with bioaerosols in wastewater treatment plants （WWTPs）, metagenomic sequencing and assembly were applied to elucidate the antibiotic resistome of bioaerosols and wastewater in WWTPs. The results showed that more subtypes of ARGs and a higher abundance of PARB were found in bioaerosols from WWTPs and downwind than those from upwind. Multidrug and macB were respectively the most dominant type and subtype of ARGs in bioaerosols from WWTPs. In total, 37 types of PARB carried at least two or more ARG types and were characterized by multiple drug resistance. At the fine grid, aerated tank, and sludge dewatering room, wastewater was the main source of bioaerosol ARGs and PARB. A total of 32 PARB were easily aerosolized in at least one wastewater treatment unit, such as Pseudomonas aeruginosa and Escherichia coli. This study will provide theoretical support for the risk assessment and health protection of antibiotic resistant pollution associated with bioaerosols from WWTPs.

UWB/MEMS IMU integrated positioning method based on NLOS angle discrimination and MAP constraints.

A dynamic nonline-of-sight (NLOS) angle discrimination method is proposed to address the insufficiency of current research on the NLOS error characteristics of ultrawideband (UWB) signals in dynamic environments as well as the problem that UWB signals frequently suffer from occlusion, leading to poor or impossible localization. The experimental results indicate that the degree of UWB signal occlusion increases as the horizontal angle decreases, and when the horizontal angle is less than 167°, the UWB ranging error is so large that no ranging value is available. On this basis, a tightly integrated UWB/MEMS IMU positioning algorithm incorporating NLOS angle discrimination and map constraints is proposed; it employs horizontal angles to discriminate UWB ranging values in NLOS environments in accordance with the dynamic NLOS characteristics of UWB signals to assign better weights to UWB observations. Through comparative analysis of the results from both groups of experiments, the algorithm achieved northward, eastward, and planar positioning errors of 0.189 m and 0.126 m, 0.119 m and 0.134 m, 0.243 m and 0.211 m, respectively. Compared to the Robust Adaptive Kalman Filtering algorithm, the positional accuracy in the plane improved by 22.9% and 28.5%, respectively.

Object Detection and Information Perception by Fusing YOLO-SCG and Point Cloud Clustering.

Robots need to sense information about the external environment before moving, which helps them to recognize and understand their surroundings so that they can plan safe and effective paths and avoid obstacles. Conventional algorithms using a single sensor cannot obtain enough information and lack real-time capabilities. To solve these problems, we propose an information perception algorithm with vision as the core and the fusion of LiDAR. Regarding vision, we propose the YOLO-SCG model, which is able to detect objects faster and more accurately. When processing point clouds, we integrate the detection results of vision for local clustering, improving both the processing speed of the point cloud and the detection effectiveness. Experiments verify that our proposed YOLO-SCG algorithm improves accuracy by 4.06% and detection speed by 7.81% compared to YOLOv9, and our algorithm excels in distinguishing different objects in the clustering of point clouds.

Diversity of ribosomes at the level of rRNA variation associated with human health and disease.

With hundreds of copies of rDNA, it is unknown whether they possess sequence variations that form different types of ribosomes. Here, we developed an algorithm for long-read variant calling, termed RGA, which revealed that variations in human rDNA loci are predominantly insertion-deletion (indel) variants. We developed full-length rRNA sequencing (RIBO-RT) and in situ sequencing (SWITCH-seq), which showed that translating ribosomes possess variation in rRNA. Over 1,000 variants are lowly expressed. However, tens of variants are abundant and form distinct rRNA subtypes with different structures near indels as revealed by long-read rRNA structure probing coupled to dimethyl sulfate sequencing. rRNA subtypes show differential expression in endoderm/ectoderm-derived tissues, and in cancer, low-abundance rRNA variants can become highly expressed. Together, this study identifies the diversity of ribosomes at the level of rRNA variants, their chromosomal location, and unique structure as well as the association of ribosome variation with tissue-specific biology and cancer.

The Role of the Medial Prefrontal Cortex in Spatial Margin of Safety Calculations.

Naturalistic observations show that animals pre-empt danger by moving to locations that increase their success in avoiding future threats. To test this in humans, we created a spatial margin of safety (MOS) decision task that quantifies pre-emptive avoidance by measuring the distance subjects place themselves to safety when facing different threats whose attack locations vary in predictability. Behavioral results show that human participants place themselves closer to safe locations when facing threats that attack in spatial locations with more outliers. Using both univariate and multivariate pattern analysis (MVPA) on fMRI data collected during a 2 h session on participants of both sexes, we demonstrate a dissociable role for the vmPFC in MOS-related decision-making. MVPA results revealed that the posterior vmPFC encoded for more unpredictable threats with univariate analyses showing a functional coupling with the amygdala and hippocampus. Conversely, the anterior vmPFC was more active for the more predictable attacks and showed coupling with the striatum. Our findings converge in showing that during pre-emptive danger, the anterior vmPFC may provide a safety signal, possibly via foreseeable outcomes, while the posterior vmPFC drives unpredictable danger signals.

Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy.

BACKGROUND AND PURPOSE: Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning, addressing challenges in practicality and generalizability in real-world clinical settings. MATERIALS AND METHODS: A total of 120 Stage II/III mid-low rectal cancer patients were retrospectively enrolled and divided into three datasets: training (n = 60), external validation (ExVal, n = 30), and generalizability evaluation (GenEva, n = 30) datasets respectively. The patients in the training and ExVal dataset were acquired on the same CT simulator, while those in GenEva were on a different CT simulator. The commercial DLAS software was first localized fine-tuned (LFT) for clinical target volume (CTV) and organs-at-risk (OAR) using the training data, and then validated on ExVal and GenEva respectively. Performance evaluation involved comparing the LFT model with the vendor-provided pretrained model (VPM) against ground truth contours, using metrics like Dice similarity coefficient (DSC), 95th Hausdorff distance (95HD), sensitivity and specificity. RESULTS: LFT significantly improved CTV delineation accuracy (p < 0.05) with LFT outperforming VPM in target volume, DSC, 95HD and specificity. Both models exhibited adequate accuracy for bladder and femoral heads, and LFT demonstrated significant enhancement in segmenting the more complex small intestine. We did not identify performance degradation when LFT and VPM models were applied in the GenEva dataset. CONCLUSIONS: The necessity and potential benefits of LFT DLAS towards institution-specific model adaption is underscored. The commercial DLAS software exhibits superior accuracy once localized fine-tuned, and is highly robust to imaging equipment changes.

Insights into the efficient degradation mechanism of extracellular proteases mediated by Purpureocillium lilacinum.

Protease secretion is crucial for degrading nematode cuticles using nematophagous fungus Purpureocillium lilacinum, but the secretion pattern of protease remains poorly understood. This study aimed to explore the degradation mechanism of proteases by investigating the characteristics of protease secretion under various carbon and nitrogen sources, and different carbon to nitrogen (C:N) ratios in P. lilacinum. The results showed that corn flour as a carbon source and yeast extract as a nitrogen source specifically induced protease secretion in P. lilacinum. P. lilacinum produced significant amounts of gelatinase and casein enzyme at C:N ratios of 10:1, 20:1, and 40:1, indicating that higher C:N ratios were more beneficial for secreting extracellular proteases. Proteomic analysis revealed 14 proteases, including 4 S8 serine endopeptidases and one M28 aminopeptidase. Among four S8 serine peptidases, Alp1 exhibited a high secretion level at C:N ratio less than 5:1, whereas PR1C, PR1D, and P32 displayed higher secretion levels at higher C:N ratios. In addition, the transcription levels of GATA transcription factors were investigated, revealing that Asd-4, A0A179G170, and A0A179HGL4 were more prevalent at a C:N ratio of 40:1. In contrast, the transcription levels of SREP, AreA, and NsdD were higher at lower C:N ratios. The putative regulatory profile of extracellular protease production in P. lilacinum, induced by different C:N ratios, was analyzed. The findings offered insights into the complexity of protease production and aided in the hydrolytic degradation of nematode cuticles.

Reinforcement learning for individualized lung cancer screening schedules: A nested case-control study.

BACKGROUND: The current guidelines for managing screen-detected pulmonary nodules offer rule-based recommendations for immediate diagnostic work-up or follow-up at intervals of 3, 6, or 12 months. Customized visit plans are lacking. PURPOSE: To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL-based policy models. METHODS: Using a nested case-control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer-free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL-based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack-years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL-based policy models with rule-based follow-up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer). RESULTS: We identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack-years, beyond those considered in guideline protocols) and the selection of follow-up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL-based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule-based protocols, the three best-performing RL-based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes. CONCLUSIONS: This study highlights the potential of using an RL-based approach that is both clinically interpretable and performance-robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system.

Soil Fungal Community Differences in Manual Plantation Larch Forest and Natural Larch Forest in Northeast China.

Soil fungal communities are pivotal components in ecosystems and play an essential role in global biogeochemical cycles. In this study, we determined the fungal communities of a natural larch forest and a manual plantation larch forest in Heilongjiang Zhongyangzhan Black-billed Capercaillie Nature Reserve and Gala Mountain Forest using high-throughput sequencing. The interactions between soil fungal communities were analysed utilising a co-occurrence network. The relationship between soil nutrients and soil fungal communities was determined with the help of Mantel analysis and a correlation heatmap. The Kruskal-Wallis test indicated that different genera of fungi differed in the two forest types. The results show that there was a significant change in the alpha diversity of soil fungal communities in both forests. In contrast, nonmetric multidimensional scaling (NMDS) analysis showed significant differences in the soil fungal community structures between the manual plantation larch forest and the natural larch forest. The soil fungal co-occurrence network showed that the complexity of the soil fungal communities in the manual plantation larch forest decreased significantly compared to those in the natural larch forest. A Mantel analysis revealed a correlation between the soil fungal co-occurrence network, the composition of soil fungi, and soil nutrients. The RDA analysis also showed that AN, TK, and pH mainly influenced the soil fungal community. The null model test results showed the importance of stochastic processes in soil fungal community assembly in manual plantation larch forests. Overall, this study enhances our understanding of the differences in soil fungal communities in manual plantation larch forests and natural larch forests, providing insights into their sustainable management. It also serves as a reminder that the ecological balance of natural ecosystems is difficult to restore through human intervention, so we need to protect natural ecosystems.

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities.

PURPOSE: To examine whether there is a significant difference in image quality between the deep learning reconstruction (DLR [AiCE, Advanced Intelligent Clear-IQ Engine]) and hybrid iterative reconstruction (HIR [AIDR 3D, adaptive iterative dose reduction three dimensional]) algorithms on the conventional enhanced and CE-boost (contrast-enhancement-boost) images of indirect computed tomography venography (CTV) of lower extremities. MATERIALS AND METHODS: In this retrospective study, seventy patients who underwent CTV from June 2021 to October 2022 to assess deep vein thrombosis and varicose veins were included. Unenhanced and enhanced images were reconstructed for AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images were obtained using subtraction software. Objective and subjective image qualities were assessed, and radiation doses were recorded. RESULTS: The CT values of the inferior vena cava (IVC), femoral vein ( FV), and popliteal vein (PV) in the CE-boost images were approximately 1.3 (1.31-1.36) times higher than in those of the enhanced images. There were no significant differences in mean CT values of IVC, FV, and PV between AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images. Noise in AiCE, AiCE-boost images was significantly lower than in AIDR 3D and AIDR 3D-boost images ( P < 0.05). The SNR (signal-to-noise ratio), CNR (contrast-to-noise ratio), and subjective scores of AiCE-boost images were the highest among 4 groups, surpassing AiCE, AIDR 3D, and AIDR 3D-boost images (all P < 0.05). CONCLUSION: In indirect CTV of the lower extremities images, DLR with the CE-boost technique could decrease the image noise and improve the CT values, SNR, CNR, and subjective image scores. AiCE-boost images received the highest subjective image quality score and were more readily accepted by radiologists.

Synthesis of Z-gem-Cl,CF3-Substituted Alkenes by Stereoselective Cross-Metathesis and the Role of Disubstituted Mo Alkylidenes.

Stereochemically defined organofluorine compounds are central to drug discovery and development. Here, we present a catalytic cross-metathesis method for the synthesis of Z-trisubstituted olefins that contain a Cl- and a CF3-bound carbon terminus. Notably, the process is stereoselective, which is in contrast to the existing stereoretentive strategies that also involve a trisubstituted olefin as starting material. Reactions are catalyzed by a Mo monoaryloxide pyrrolide alkylidene, involve a trisubstituted alkene and gem-Cl,CF3-substituted alkene, and are fully Z-selective. Catalytic cross-coupling can be used to convert the C-Cl bond of the trisubstituted olefin to C-B, C-D, and different C-C bonds. We elucidate the role of Cl,CF3-disubstituted Mo alkylidenes. Experimental and computational (DFT) data show that in some instances a disubstituted alkylidene is formed and then transformed to a more active complex. In other cases, the Cl,CF3-disubstituted alkylidene is a direct participant in a catalytic cycle. The studies described shed new light on the chemistry of high oxidation-state disubstituted alkylidenes-scarcely investigated entities likely to be pivotal to approaches for stereocontrolled synthesis of tetrasubstituted alkenes through olefin metathesis.