Near telomere-to-telomere genome assemblies of two Chlorella species unveil the composition and evolution of centromeres in green algae.

BACKGROUND: Centromeres play a crucial and conserved role in cell division, although their composition and evolutionary history in green algae, the evolutionary ancestors of land plants, remains largely unknown. RESULTS: We constructed near telomere-to-telomere (T2T) assemblies for two Trebouxiophyceae species, Chlorella sorokiniana NS4-2 and Chlorella pyrenoidosa DBH, with chromosome numbers of 12 and 13, and genome sizes of 58.11 Mb and 53.41 Mb, respectively. We identified and validated their centromere sequences using CENH3 ChIP-seq and found that, similar to humans and higher plants, the centromeric CENH3 signals of green algae display a pattern of hypomethylation. Interestingly, the centromeres of both species largely comprised transposable elements, although they differed significantly in their composition. Species within the Chlorella genus display a more diverse centromere composition, with major constituents including members of the LTR/Copia, LINE/L1, and LINE/RTEX families. This is in contrast to green algae including Chlamydomonas reinhardtii, Coccomyxa subellipsoidea, and Chromochloris zofingiensis, in which centromere composition instead has a pronounced single-element composition. Moreover, we observed significant differences in the composition and structure of centromeres among chromosomes with strong collinearity within the Chlorella genus, suggesting that centromeric sequence evolves more rapidly than sequence in non-centromeric regions. CONCLUSIONS: This study not only provides high-quality genome data for comparative genomics of green algae but gives insight into the composition and evolutionary history of centromeres in early plants, laying an important foundation for further research on their evolution.

Diabetes-induced muscle wasting: molecular mechanisms and promising therapeutic targets.

Diabetes has become a serious public health crisis, presenting significant challenges to individuals worldwide. As the largest organ in the human body, skeletal muscle is a significant target of this chronic disease, yet muscle wasting as a complication of diabetes is still not fully understood and effective treatment methods have yet to be developed. Here, we discuss the targets involved in inducing muscle wasting under diabetic conditions, both validated targets and emerging targets. Diabetes-induced skeletal muscle wasting is known to involve changes in various signaling molecules and pathways, such as protein degradation pathways, protein synthesis pathways, mitochondrial function, and oxidative stress inflammation. Recent studies have shown that some of these present potential as promising therapeutic targets, including the neuregulin 1/epidermal growth factor receptor family, advanced glycation end-products, irisin, ferroptosis, growth differentiation factor 15 and more. This study's investigation and discussion of such pathways and their potential applications provides a theoretical basis for the development of clinical treatments for diabetes-induced muscle wasting and a foundation for continued focus on this disease.

Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients.

BACKGROUND: Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals. METHOD: We constructed two high-quality pulse wave datasets comprising a non-LVE group and an LVE group based on the 264 HF patients. Fourier series calculations were employed to determine if significant frequency differences existed between the two datasets, thereby ensuring their validity. Then, the ML-based identification was undertaken by means of classification and regression models: a weighted random forest model was employed for binary classification of the datasets, and a densely connected convolutional network was utilized to directly estimate the left ventricular diastolic diameter index (LVDdI) through regression. Finally, the accuracy of the two models was validated by comparing their results with clinical measurements, using accuracy and the area under the receiver operating characteristic curve (AUC-ROC) to assess their capability for identifying LVE patients. RESULTS: The classification model exhibited superior performance with an accuracy of 0.91 and an AUC-ROC of 0.93. The regression model achieved an accuracy of 0.88 and an AUC-ROC of 0.89, indicating that both models can quickly and accurately identify LVE in HF patients. CONCLUSION: The proposed ML methods are verified to achieve effective classification and regression with good performance for identifying LVE in HF patients based on pulse wave signals. This study thus demonstrates the feasibility and potential of the ML-based strategy for clinical practice while offering an effective and robust tool for diagnosing and intervening ventricular remodeling.

Pulse wave-based evaluation of the blood-supply capability of patients with heart failure via machine learning.

Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart's blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO2). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samples t-test (p > 0.05), the Bland-Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO2, LAD, and LVDd performance can be achieved with the maximum error < 15%. While our findings thus demonstrate the potential of pulse wave-based, non-invasive evaluation of the blood-supply capability of patients with HF, they also set the stage for further refinements in health monitoring and deterioration prevention applications.

Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis.

OBJECTIVE: This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing both the intratumoral and peritumoral-to predict the status of axillary lymph nodes in patients with early-stage breast cancer. METHODS: The study employed retrospective methods, collecting clinical information, ultrasound data, and postoperative pathological results from 321 breast cancer patients (including 224 in the training group and 97 in the validation group). Through correlation analysis, univariate analysis, and Lasso regression analysis, independent risk factors related to axillary lymph node metastasis in breast cancer were identified from conventional ultrasound and immunohistochemical indicators, and a clinical feature model was constructed. Additionally, features were extracted from ultrasound images of the intratumoral and its 1-5 mm peritumoral to establish a radiomics feature formula. Furthermore, by combining clinical features and ultrasound radiomics features, six machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, Extreme Gradient Boosting, Random Forest, and K-Nearest Neighbors) were compared for diagnostic efficacy, and constructing a joint prediction model based on the optimal ML algorithm. The use of Shapley Additive Explanations (SHAP) enhanced the visualization and interpretability of the model during the diagnostic process. RESULTS: Among the 321 breast cancer patients, 121 had axillary lymph node metastasis, and 200 did not. The clinical feature model had an AUC of 0.779 and 0.777 in the training and validation groups, respectively. Radiomics model analysis showed that the model including the Intratumor +3 mm peritumor area had the best diagnostic performance, with AUCs of 0.847 and 0.844 in the training and validation groups, respectively. The joint prediction model based on the XGBoost algorithm reached AUCs of 0.917 and 0.905 in the training and validation groups, respectively. SHAP analysis indicated that the Rad Score had the highest weight in the prediction model, playing a significant role in predicting axillary lymph node metastasis in breast cancer. CONCLUSION: The predictive model, which integrates clinical features and radiomic characteristics using the XGBoost algorithm, demonstrates significant diagnostic value for axillary lymph node metastasis in breast cancer. This model can provide significant references for preoperative surgical strategy selection and prognosis evaluation for breast cancer patients, helping to reduce postoperative complications and improve long-term survival rates. Additionally, the utilization of SHAP enhancing the global and local interpretability of the model.

Study on diagnosing thyroid nodules of ACR TI-RADS 4-5 with multimodal ultrasound radiomics technology.

BACKGROUND: Explore the feasibility of using the multimodal ultrasound (US) radiomics technology to diagnose American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) 4-5 thyroid nodules. METHOD: This study prospectively collected the clinical characteristics, conventional, and US elastography images of 100 patients diagnosed with ACR TI-RADS 4-5 nodules from May 2022 to 2023. Independent risk factors for malignant thyroid nodules were extracted and screened using methods such as the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model, and a multimodal US radiomics combined diagnostic model was established. Using a multifactorial LR analysis and a Rad-score rating, the predictive performance was validated and evaluated, and the final threshold range was determined to assess the clinical net benefit of the model. RESULTS: In the training set, the US radiomics combined predictive model area under curve (AUC = 0.928) had higher diagnostic performance compared with clinical characteristics (AUC = 0.779), conventional US (AUC = 0.794), and US elastography model (AUC = 0.852). In the validation set, the multimodal US radiomics combined diagnostic model (AUC = 0.829) also had higher diagnostic performance compared with clinical characteristics (AUC = 0.799), conventional US (AUC = 0.802), and US elastography model (AUC = 0.718). CONCLUSION: Multi-modal US radiomics technology can effectively diagnose thyroid nodules of ACR TI-RADS 4-5, and the combination of radiomics signature and conventional US features can further improve the diagnostic performance.

Extensive Carbon Contribution of Inundated Terrestrial Plants to Zooplankton Biomass in a Eutrophic Lake.

Organic carbon derived from terrestrial plants contributes to aquatic consumers, e.g., zooplankton in lakes. The degree of the contribution depends on the availability of terrestrial organic carbon in lake organic pool and the transfer efficiency of the carbon. Terrestrial organic carbon is poor-quality food for zooplankton with a mismatch of nutrition content and was incorporated to zooplankton with much lower efficiency than phytoplankton. Contributions of terrestrial carbon to zooplankton generally decrease with an increase in phytoplankton production, indicating a preferential incorporation of phytoplankton in previous investigations. However, in eutrophic lakes, the dominating cyanobacteria were of poor quality and incorporated to consumers inefficiently too. In that case, zooplankton in eutrophic wetlands, where cyanobacteria dominate the phytoplankton production and massive terrestrial plants are inundated, may not preferentially incorporate poor food-quality phytoplankton resource to their biomass. Therefore, we hypothesize that carbon contributions of terrestrial vegetation to zooplankton and to lake particulate organic pool should be similar in such aquatic ecosystems. We tested this hypothesis by sampling zooplankton and carbon sources in Ming Lake (Jinan University Campus, southern China) which was overgrown by terrestrial plants after drying and re-flooded. After 60 days of observations at weekly (or biweekly) intervals, applying stable carbon (13C), nitrogen (15 N), and hydrogen (2H) isotopic analysis and a stable isotope mixing model, we estimated the occurrence of extensive carbon contribution (≥ 50%) of flooded terrestrial plants to cladocerans and copepods. Contribution of inundated terrestrial plants to cladocerans was similar to that to lake particulate organic pool. Thus, our study quantified the role of terrestrial carbon in eutrophic wetlands, enhancing our understanding of cross-ecosystem interactions in food webs with an emphasis on the resource quality.

miR-141-3p Regulates the Proliferation and Apoptosis of Endometrial-Myometrial Interface Smooth Muscle Cells in Adenomyosis Via JAK2/STAT3 Pathway.

Adenomyosis (ADS) is a common benign gynecological disease. Abnormal proliferation at the endometrial-myometrial interface (EMI) plays a crucial role in the occurrence and progression of ADS. miR-141-3p is associated with cell proliferation and apoptosis. However, the specific mechanism of miR-141-3p in the etiology of ADS is still unknown. In this study, we explored the effects of miR-141-3p on the proliferation and apoptosis of ADS EMI smooth muscle cells (SMCs). We collected EMI tissues for the primary culture of SMCs from 25 patients diagnosed with ADS and 20 without ADS. Real-time quantitative polymerase chain reaction and western blot were used to measure the mRNA and protein expression levels of miR-141-3p, JAK2, STAT3, phospho-JAK2, and phospho-STAT3 in ADS EMI SMCs. The cell counting kit 8 assay and flow cytometry analysis were used to evaluate the proliferation and apoptosis of EMI SMCs. The miR-141-3p mimic/inhibitor was used to increase or decrease the expression level of miR-141-3p. We added WP1066 to block the phosphorylation of JAK2/STAT3 pathway components. The miR-141-3p levels were decreased, while JAK2 and STAT3 levels were increased in ADS EMI SMCs. miR-141-3p overexpression significantly inhibited the proliferation and enhanced the apoptosis of EMI SMCs, whereas a decrease in miR-141-3p expression level was connected to the opposite results. Meanwhile, inactivated JAK2/STAT3 pathway decreased proliferation and enhanced apoptosis of EMI SMCs after WP1066 treatment. Furthermore, rescue experiments confirmed that the JAK2/STAT3 pathway was the downstream pathway of miR-141-3p and reduced the effect of miR-141-3p on the proliferation and apoptosis of EMI SMCs. These results demonstrate that miR-141-3p regulates the proliferation and apoptosis of ADS EMI SMCs by modulating the JAK2/STAT3 pathway.

A theory of emergence: Knowledge, rewiring and innovation.

Why do social interactions linked to sharing knowledge drive the emergence of a regional technology economy? We proffer a positive theory and explanation-sketch identifying mechanisms and initial conditions in an explanation of emergence of a knowledge economy. We trace the emergence of a knowledge economy, from a small group of founding members to a regional technology economy. With the rapid influx of new people, knowledge spillover motivates technologists and entrepreneurs to reach out beyond existing contacts to explore the expanding knowledge economy and interact with new acquaintances in the search for novelty. In the course of network rewiring in knowledge clusters, individuals share knowledge and cooperate in innovation, and move to more central positions when they interact. Mirroring the trends of increased knowledge exploration and innovative activity at the individual level, new startup firms founded during this time period come to span a greater number of industry groups. Endogenous dynamics of overlapping knowledge networks lie behind the rapid morphogenesis of new regional technology economies in New York City and Los Angeles.

RNA-seq reveals co-dysregulated circular RNAs in the adenomyosis eutopic endometrium and endometrial-myometrial interface.

BACKGROUND: Uterine adenomyosis is associated with chronic pelvic pain, abnormal uterine bleeding, and infertility. The pathogenesis of adenomyosis is still unclear. Circular RNAs (circRNAs) have been implicated in several benign diseases and malignant tumors. We aimed to explore the co-dysregulated circular RNA profile in the eutopic endometrium and endometrial-myometrial interface (EMI) of adenomyosis. METHODS: Total RNA was extracted from the eutopic endometrium and EMI of 5 patients with adenomyosis and 3 patients without adenomyosis. Next-generation sequencing was performed to identify the circRNA expression profile of the two tissue types. Bioinformatics analysis was performed to predict circRNA-binding miRNAs and miRNA-binding mRNAs and construct ceRNA networks, and functional enrichment analysis was performed to predict the biological functions of circRNAs. RESULTS: Among the adenomyosis patients, 760 circRNAs were significantly upregulated and 119 circRNAs were significantly downregulated in the EMI of adenomyosis, while 47 circRNAs were significantly upregulated and 17 circRNAs were significantly downregulated in the eutopic endometrium of adenomyosis. We identified hsa_circ_0002144 and hsa_circ_0005806 as co-upregulated and hsa_circ_0079536 and hsa_circ_0024766 as co-downregulated in the eutopic endometrium and EMI. Bioinformatics analysis was performed to construct a ceRNA network of codifferentially expressed circRNAs. The MAPK signaling pathway is the most important signaling pathway involved in the function of the ceRNA network. CONCLUSIONS: Co-dysregulated circRNAs were present in the eutopic endometrium and EMI of adenomyosis. MiRNA binding sites were observed for all of these circRNAs and found to regulate gene expression. Co-dysregulated circRNAs may induce the eutopic endometrial invagination process through the MAPK signaling pathway and promote the progression of adenomyosis.

Potential shift from a carbon sink to a source in Amazonian peatlands under a changing climate.

Amazonian peatlands store a large amount of soil organic carbon (SOC), and its fate under a future changing climate is unknown. Here, we use a process-based peatland biogeochemistry model to quantify the carbon accumulation for peatland and nonpeatland ecosystems in the Pastaza-Marañon foreland basin (PMFB) in the Peruvian Amazon from 12,000 y before present to AD 2100. Model simulations indicate that warming accelerates peat SOC loss, while increasing precipitation accelerates peat SOC accumulation at millennial time scales. The uncertain parameters and spatial variation of climate are significant sources of uncertainty to modeled peat carbon accumulation. Under warmer and presumably wetter conditions over the 21st century, SOC accumulation rate in the PMFB slows down to 7.9 (4.3-12.2) g⋅C⋅m-2⋅y-1 from the current rate of 16.1 (9.1-23.7) g⋅C⋅m-2⋅y-1, and the region may turn into a carbon source to the atmosphere at -53.3 (-66.8 to -41.2) g⋅C⋅m-2⋅y-1 (negative indicates source), depending on the level of warming. Peatland ecosystems show a higher vulnerability than nonpeatland ecosystems, as indicated by the ratio of their soil carbon density changes (ranging from 3.9 to 5.8). This is primarily due to larger peatlands carbon stocks and more dramatic responses of their aerobic and anaerobic decompositions in comparison with nonpeatland ecosystems under future climate conditions. Peatland and nonpeatland soils in the PMFB may lose up to 0.4 (0.32-0.52) Pg⋅C by AD 2100 with the largest loss from palm swamp. The carbon-dense Amazonian peatland may switch from a current carbon sink into a source in the 21st century.

Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic.

Accurate oil market forecasting plays an important role in the theory and application of oil supply chain management for profit maximization and risk minimization. However, the coronavirus disease 2019 (COVID-19) has compelled governments worldwide to impose restrictions, consequently forcing the closure of most social and economic activities. The latter leads to the volatility of the oil markets and poses a huge challenge to oil market forecasting. Fortunately, the social media information can finely reflect oil market factors and exogenous factors, such as conflicts and political instability. Accordingly, this study collected vast online oil news and used convolutional neural network to extract relevant information automatically. Oil markets are divided into four categories: oil price, oil production, oil consumption, and oil inventory. A total of 16,794; 9,139; 8,314; and 8,548 news headlines were collected in four respective cases. Experimental results indicate that social media information contributes to the forecasting of oil price, oil production and oil consumption. The mean absolute percentage errors are respectively 0.0717, 0.0144 and 0.0168 for the oil price, production, and consumption prediction during the COVID-19 pandemic. Marketers must consider the impact of social media information on the oil or similar markets, especially during the COVID-19 outbreak.

20 kHz dual-plane stereo-PIV measurements on a swirling flame using a two-legged burst-mode laser.

Dual-plane stereoscopic particle image velocimetry (PIV) is capable of quantifying the flow field in terms of three-component (3C) flow vectors and 3C vorticity vectors simultaneously. Here, we present a test rig to carry out the 20 kHz dual-plane stereo PIV measurements on a premixed swirling flame by using a two-legged burst-mode laser. Other than the traditional methods employing the laser polarization direction and the two-color separation methods, two same-color laser sheets with a 100 ns delay were adopted to separate the imaging processes for the two pairs of cameras using the image straddling method. Each laser sheet with the same wavelength of 532 nm has a pulse cyclic frequency of 20 kHz within each burst generated by the high-repetition-rate burst-mode laser. 3C velocity vectors of a swirling flame were obtained based on the sequential particle images for each laser sheet. In spite of non-perfect simultaneous flow measurements on the two spatially separated laser sheets, the velocity error caused by the 100 ns delay on top of a 50 µs duration, which was used for the velocity vector calculation, is negligible. This short-delay separation method significantly simplifies the experimental setup for dual-plane stereo PIV measurements, especially for low-speed flows.

High-repetition-rate burst-mode-laser diagnostics of an unconfined lean premixed swirling flame under external acoustic excitation.

Lean premixed swirling flames are important in practical combustors, but a commonly encountered problem of practical swirl combustors is thermo-acoustic instability, which may cause internal structure damage to combustors. In this research, a high-repetition-rate burst-mode laser is used for simultaneous particle image velocimetry and planar laser-induced fluorescence measurement in an unconfined acoustically excited swirl burner. The time-resolved flow field and transient flame response to the acoustic perturbation are visualized at 20 kHz, offering insight into the heat release rate oscillation. The premixed mixture flow rate and acoustic modulation are varied to study the effects of Reynolds number, Strouhal number, and acoustic modulation amplitude on the swirling flame. The results suggest that the Strouhal number has a notable effect on the periodic movements of the inner recirculation zone and swirling flame configuration.

Temporally resolved two dimensional temperature field of acoustically excited swirling flames measured by mid-infrared direct absorption spectroscopy.

The detailed understandings of temperature profiles and flow-flame interaction in unsteady premixed swirling flames are crucial for the development of low emission turbine engines. Here, a phase-locked tomographic reconstruction technique measuring the large absorption cross section of CO2 at its mid-infrared fundamental band around 4.2 µm is used to acquire the flame temperature and in situ CO2 volume fraction distribution in a turbulent premixed swirling flame under different levels of external acoustic forcing amplitude. The temporally resolved temperature field variation reveals large temperature fluctuation in unsteady premixed swirling flames produced near the nozzle exit due to vortex-driven mixing of surrounding cold gas. The temperature fluctuation quickly dissipates when moving downstream of the flame with the flow velocity of the burnt gas. The accurate high temporal resolution thermodynamic measurements of the phase-locked tomographic thermometry technique reported in this work can be generally applied to periodic reacting flows.

Ascertain the Safe Range From Foramen Rotundum to the Siphon of Internal Carotid Artery for Operation Through Pterygopalatine Fossa With Thin-Section Computed Tomographic Image.

OBJECTIVE: This study aimed to ascertain the safe range from foramen rotundum to the siphon of internal carotid artery using three-dimensional reconstruction technique. METHODS: We scanned skulls of 121 volunteers to get the final results with thin-section computed tomographic image. RESULTS: The distance of AC was measured as 13.22 (3.79) mm (range, 8.33-105.67 mm; 95% CI [confidence interval], 8.55-21.39 mm). The angle to the sagittal plane was measured as 33.54 (9.23) mm (range, 5.38-66.58; 95% CI, 30.88-34.20). The angle to the coronal plane was measured as 53.17 (10.48) mm (range, 5.60-75.02; 95% CI, 51.29-55.06). The angle to the horizontal plane was measured as 9.43 (12.91) mm (range, -28.44 to 82.22; 95% CI, 7.11-11.76). CONCLUSIONS: These above-mentioned results can help locate these structures to help in minimizing surgical trauma to the nerves and blood vessels of the operation through pterygopalatine fossa under nasoendoscope.

Precise location of sellar floor of patients with different saddle forms for pituitary surgery through unilateral endonasal transsphenidal approach.

Our study aims to illustrate the precise location of sellar floor of patients with different saddle forms for pituitary surgery through unilateral endonasal transsphenoidal approach, by measuring 2 angles and 2 distances. One hundred thirty-three (74 men and 59 women) participated in this study anonymously. Results are as follows: the differences of distances 1, 2, and 3 were statistically significant in comparison of saddle forms (P < 0.05), whereas angles 1 and 2 have no significance. And similar results are also shown in the elderly group and the women. For men, only distance 2 in these parameters was statistically significant. Consequently, we should pay attention to the patients, saddle forms, ages, and sexes, especially for distances 1 and 2 and distance 3 before the surgery, which can help to locate the sellar floor precisely and determine the operable range for patients with different saddle forms.

LD-CSNet: A latent diffusion-based architecture for perceptual Compressed Sensing.

Compressed Sensing (CS) is a groundbreaking paradigm in image acquisition, challenging the constraints of the Nyquist-Shannon sampling theorem. This enables high-quality image reconstruction using a minimal number of measurements. Neural Networks' potent feature induction capabilities enable advanced data-driven CS methods to achieve high-fidelity image reconstruction. However, achieving satisfactory reconstruction performance, particularly in terms of perceptual quality, remains challenging at extremely low sampling rates. To tackle this challenge, we introduce a novel two-stage image CS framework based on latent diffusion, named LD-CSNet. In the first stage, we utilize an autoencoder pre-trained on a large dataset to represent natural images as low-dimensional latent vectors, establishing prior knowledge distinct from sparsity and effectively reducing the dimensionality of the solution space. In the second stage, we employ a conditional diffusion model for maximum likelihood estimates in the latent space. This is supported by a measurement embedding module designed to encode measurements, making them suitable for a denoising network. This guides the generation process in reconstructing low-dimensional latent vectors. Finally, the image is reconstructed using a pre-trained decoder. Experimental results across multiple public datasets demonstrate LD-CSNet's superior perceptual quality and robustness to noise. It maintains fidelity and visual quality at lower sampling rates. Research findings suggest the promising application of diffusion models in image CS. Future research can focus on developing more appropriate models for the first stage.

Identification of critical links based on the optimal reliable path in stochastic traffic networks.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.

Optimization of Porous Structures of Carbon Matrices for Loading Red Phosphorus to Achieve High-Capacity and Long-Life Anodes for All-Solid-State Lithium-Ion Batteries.

All-solid-state lithium-ion batteries (ASSLIBs) using sulfide electrolytes and high-capacity alloy-type anodes have attracted sizable interest due to their potential excellent safety and high energy density. Encapsulating insulating red phosphorus (P) inside nanopores of a carbon matrix can adequately activate its electrochemical alloying reaction with lithium. Therefore, the porosity of the carbon matrix plays a crucial role in the electrochemical performance of the resulting red P/carbon composites. Here, we use zeolite-templated carbon (ZTC) with monodisperse micropores and mesoporous carbon (CMK-3) with uniform mesopores as the model hosts of red P. Our results reveal that micropores enable more effective pore utilization for the red P loading, and the P@ZTC material can achieve a record-high content (65.0 wt %) of red P confined within pores. When used as an anode of ASSLIBs, the P@ZTC electrode delivers an ultrahigh capacity of 1823 mA h g-1 and a high initial Coulombic efficiency of 87.44%. After 400 deep discharge-charge cycles (running over 250 days) at 0.2 A g-1, the P@ZTC electrode still holds a reversible capacity of 1260 mA h g-1 (99.92% capacity retention per cycle). Moreover, a P@ZTC||LiNi0.8Co0.1Mn0.1O2 full cell can deliver a reversible areal capacity of over 3 mA h cm-2 at 0.1C after 100 cycles.