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Abstract Three-dimensional (3D) printed surgical models provide an excellent surgical training option to closely mimic real operations to teach medical students who currently rely largely on visual learning aided with simple suturing pads. There is an unmet need to create simple to complex surgical training programs suitable for medical students. A prospective cohort study was conducted on a group of 16 6th year students. They were randomly divided into two groups for suture training on a basic training pad or on unique 3D-printed intestinal anastomosis models. After 4 weeks of open and laparoscopic surgical training, exams were performed on the standardized 3D-printed model at the end of each stage to assess surgical performance including surgical time and scores. Both groups had similar skills before the start of each stage. In stage 1, both groups showed comparable learning performance, but the 3D model group performed better in Exam 1. In stage 2, the 3D model group took more time but showed significant improvements and outperformed the pad group in Exam 2 in both performance scores and time. Post-training questionnaires indicated increased interest in surgery and technical training among students using 3D models. Realistic 3D-printed models benefit surgical training, expected to become integral in teaching operative skills and techniques to medical students.

This paper explored the novel approach of targeting the cyclic guanosine monophosphate (GMP)-adenosine monophosphate (AMP) synthase-stimulator of interferon genes (cGAS-STING) pathway for the treatment of osteosarcoma (OS). Osteosarcoma is a common malignancy in adolescents. Most patients die from lung metastasis. It reviewed the epidemiology and pathological characteristics of OS, highlighting its highly malignant nature and tendency for pulmonary metastasis, underscoring the importance of identifying new therapeutic targets. The cGAS-STING pathway was closely associated with the malignant biological behaviors of OS cells, suggesting that targeting this pathway could be a promising therapeutic strategy. Currently, research on the role of the cGAS-STING pathway in OS treatment has been limited, and the underlying mechanisms remain unclear. Therefore, further investigation into the mechanisms of the cGAS-STING pathway in OS and the exploration of therapeutic strategies based on this pathway are of great significance for developing more effective treatments for OS. This paper offered a fresh perspective on the treatment of OS, providing hope for new therapeutic options for OS patients by targeting the cGAS-STING pathway.

Abstract Chemotherapy‐based combination regimens are recommended as first‐line treatment for colorectal cancer. However, multidrug resistance (MDR) and limited drug infiltration in tumor microenvironment remain critical challenges. Herein, a pH/redox dual activated supramolecular DAS@CD‐OxPt (IV) nanoparticles (NPs) via host‐guest molecular recognition to achieve relay drugs delivery of active oxaliplatin (OxPt (IV)) and Src inhibitor dasatinib (DAS) between tumor cells is developed. DAS@CD‐OxPt (IV) NPs exhibit prolonged circulation in the blood and intra‐tumoral retention. Triggered by the endo/lysosome (pH 5.0), flexible DAS@CD‐OxPt (IV) NPs exhibited proton‐driven in situ assembly to form nanofiber in tumor cells. Dual chemotherapeutic agents released from DAS@CD‐OxPt (IV) NPs synergistically cause irreversible DNA damage by blocking p53‐mediated DNA repair. Supramolecular nanofibers can further serve as the “ammunition depot” to continuously release drugs from dying cells and transport them into neighboring tumor cells, leading to domino‐like cell death and enhanced immunogenicity. Furthermore, DAS@CD‐OxPt (IV) NPs combined with immune checkpoint blockade (ICB) therapy strikingly suppress CT26 tumor growth and pulmonary metastasis.

This paper reports on the successful synthesis of a novel alumina powder with a unique rose-like structure composed of alumina platelets, using the molten salt method. The synthesized powder was then utilized to prepare alumina ceramic with impressive mechanical properties. The influences of the synthesizing temperature of alumina powder on the microstructure and mechanical properties of alumina ceramic are investigated in details. The results indicate that the alumina ceramic prepared using the rose-like powder synthesized at 800 °C exhibits a high relative density of 99.5 %, flexural strength of 408.2 ± 18.7 MPa, and fracture toughness of 6.14 ± 0.63 MPa m1/2. The work presented in this paper will provide a useful guideline for the preparation of high-performance alumina ceramic materials in the future.

Structural health monitoring (SHM) of composite materials is of great significance in various practical applications. However, it is a challenge to accurately monitor the damage of composites without affecting their mechanical properties. In this paper, an embedded sensing layer based on carbon nanotube-coated glass fiber is designed, combined with electrical resistance tomography (ERT) for in situ damage monitoring. Multi-wall carbon nanotube-coated glass fiber (MWCNT-GF) is prepared and embedded into laminates as an in situ sensing layer. Low-velocity impact experiments demonstrate that the embedded sensing layer has high compatibility with the composite laminates and has no adverse effect on its impact response; although, the energy absorption behavior of glass fiber-reinforced polymer (GFRP) laminates containing MWCNT-GF occurs about 10% earlier than that of GFRP laminates overall. ERT technology is used to analyze the laminates after a low-velocity impact test. The results show that the in situ monitoring method with the embedded MWCNT-GF sensing layer can achieve high precision in imaging localization of impact damage, and the error of the detected damage area is only 4.5%.

The current work aimed to evaluate the effect of ensiling density on the fermentation quality and aerobic stability of Pennisetum giganteum silages. The silage was ensiled in laboratory silos (1 L), and three treatments were designed according to different ensiling densities: (1) low density (LD, 750 kg/m3); (2) medium density (MD, 900 kg/m3); and (3) high density (HD, 1050 kg/m3). During ensiling, the silage was sampled for a fermentation quality analysis. All silages were well preserved, as indicated by the high lactic acid (LA) content and low pH (3-N) content (68.2 g/kg total nitrogen, TN) among all silages after 45 days of ensiling. The aerobic stability of HD, MD, and LD persisted for 51 h, 54 h, and 48 h, respectively. The NH3-N contents of HD and MD were below 80 g/kg TN during aerobic exposure. These results show that the increase in ensiling density improved the fermentation quality and aerobic stability of Pennisetum giganteum silages.

The origami mechanisms possess numerous unique advantages, including folding, reconfigurability, and multi-stability. The presence of multi-stability introduces a novel concept for the design of constant force mechanisms (CFM). In this study, we present a CFM with multi-segment constant-force regions by leveraging the multi-stable characteristics of the origami mechanism. The design principle behind this CFM involves combining an accordion origami structure with positive stiffness and a Kresling origami structure with multi-segment negative stiffness regions. To achieve zero stiffness, the Kresling origami structure is aligned parallel to the accordion origami. To effectively utilize both the Kresling and accordion origamis, we have established a mechanical model that describes their respective stiffness characteristics to establish design rules for the constant-force mechanism. By carefully designing the parameters of these two origami structures, we evaluate how variations in the structural parameters of Kresling influence the constant force properties of our proposed multi-segment CFM. To illustrate its inherent property of providing constant force across multiple segments, we employ finite element analysis and experiments to obtain force-displacement curves for our mechanism. The results demonstrate the feasibility of our presented design method which paves the way for constructing a simple CFM.

This work aimed to assess microbial inoculants (Lactiplantibacillus plantarum and Lentilactobacillus buchneri), chemical additives (natamycin and hexanoic acid), and their combination on fermentation characteristics and aerobic stability in total mixed ration (TMR) silage. The TMR consisted of 30% water bamboo shell (WBS), 10% alfalfa, 20% rice straw, and 40% concentrate. There were six treatments as follows: (1) deionized water (control, CON). (2) lactic acid bacteria (Lactiplantibacillus plantarum + Lentilactobacillus buchneri; LPB, 1 × 106 cfu/g FW). (3) natamycin (NT, 0.02 g/kg FW). (4) hexanoic acid (HA, 0.02 g/kg FW). (5) lactic acid bacteria + natamycin (SLNT, 0.02 g/kg FW). (6) lactic acid bacteria + hexanoic acid (SLHA, 0.02 g/kg FW). After fermentation, laboratory silos (10 L) were opened to assess fermentation quality, followed by a 6-day aerobic stability test. The results showed that all silages were well fermented with high lactic acid (LA) content, low ammonia nitrogen (NH3-N), and negligible butyric acid (BA) levels. Among all silages, SLNT silage exhibited the greatest LA, acetic acid (AA) levels, LAB counts, and the lowest pH and NH3-N. For aerobic stability, all additives significantly (p < 0.05) enhanced aerobic stability, delayed (p < 0.05) the decrease in LA and water-soluble carbohydrates (WSC) and the increase in pH, and significantly (p < 0.05) minimized yeast proliferation. The SLNT silage showed the best aerobic stability, with SLHA, NT, HA, and LPB following. In conclusion, SLNT is recommended as the optimal additive in improving the fermentation quality and aerobic stability of TMR silage, with SLHA, NT, HA, and LPB following.

Abstract Light exposure can profoundly affect neurological functions and behaviors. Here, we show that short-term exposure to moderate (400 lux) white light during Y-maze test promoted spatial memory retrieval and induced only mild anxiety in mice. This beneficial effect involves the activation of a circuit including neurons in the central amygdala (CeA), locus coeruleus (LC), and dentate gyrus (DG). Specifically, moderate light activated corticotropin-releasing hormone (CRH) positive (+) CeA neurons and induced the release of corticotropin-releasing factor (CRF) from their axon terminals ending in the LC. CRF then activated tyrosine hydroxylase-expressing LC neurons, which send projections to DG and release norepinephrine (NE). NE activated β-adrenergic receptors on CaMKIIα-expressing DG neurons, ultimately promoting spatial memory retrieval. Our study thus demonstrated a specific light scheme that can promote spatial memory without excessive stress, and unraveled the underlying CeA-LC-DG circuit and associated neurochemical mechanisms.

Silage quality and aerobic stability are the key factors affecting the utilization efficiency of silage feed, and ensiling density stands as the fundamental principle of silage making. The experiment presented here evaluates the effects of ensiling density on the silage quality and aerobic stability of alfalfa silage. In this experiment, alfalfa was harvested, wilted, chopped, and subsequently packed into 10 L laboratory silos. The ensiling densities were set to 800 g/L, 700 g/L, and 600 g/L, respectively, with three replicates in each group. Sampling and analysis were carried out at 45 days of silage and 8 days of aerobic exposure. The results showed that ensiling density significantly (p < 0.05) affected the content of ammonia nitrogen, Flieg score, the counts of yeast, and mold. After 45 days of ensiling, 800 g/L silage had the highest contents of dry matter, water-soluble carbohydrates, crude protein, lactic acid, and total organic acids, and the lowest pH and ammonia nitrogen compared to the 700 g/L and 600 g/L silage (p < 0.05). The Flieg score of 800 g/L silage was higher than those of the 700 g/L and 600 g/L silage. The counts of yeast and mold of 800 g/L silage was significantly lower than those of the 700 g/L and 600 g/L silage. During aerobic exposure, a consistent decrease in lactic acid and an increase in pH were observed among all silages. The aerobic stability of 800 g/L silage (156 h) was significantly higher than that in the 700 g/L (136 h) and 600 g/L silage (111 h). It was suggested that the increasing ensiling density above 800 g/L was an effective method to improve both the silage quality and the aerobic stability of alfalfa silage.

Abstract To design a flexure hinge with high precision and high natural frequency, the sinusoidal flexure hinge is proposed in this article. First, the formulae for the compliance and precision factors of the hinge were derived based on the Euler–Bernoulli beam theory and the Gauss–Legendre quadrature formula. The natural frequency was also investigated based on the transfer matrix method. Compared with the simulation results of ANSYS Workbench, the results show that the modeling error is less than 6.7%. Second, the influence of structural parameters on compliance, precision factor, compliance precision ratio, and natural frequency was analyzed. The results show that compliance and precision are often contradictory, and the minimum thickness significantly influences the hinge's performance. Compared with conic flexure hinges in terms of compliance, precision, compliance precision ratios, and natural frequency, the sinusoidal flexure hinges have a better comprehensive performance. Finally, a flexure hinge was manufactured, and compliance was measured. The experimental results show that the error between the experimental value and the modeling value is 7.8%. Both simulation and experimental results verify the effectiveness of the sinusoidal flexure hinge model.

Ensiling density has significant importance for the quality and preservation of silage. Appropriate ensiling density could improve the nutritional value, extend the storage time of silage and reduce the risk of mold and spoilage. This work aimed to evaluate the effect of ensiling densities on the fermentation quality and aerobic stability of rice straw. The rice straw was obtained after threshing, then chopped and ensiled into a 10 L laboratory silo with three ensiling densities (high density at 700 g/L, medium density at 600 g/L and low density at 500 g/L). Five silos per density were opened after 3, 5, 7, 14, 30 and 60 days of ensiling, and then, the fermentation quality and aerobic stability were analyzed. During ensiling, high density had the highest lactic acid content, and the lowest pH and ammonia nitrogen. There was no difference (p > 0.05) in the propionic acid, butyric acid and ethanol contents among all silage, and the contents of propionic acid and butyric acid were trace amounts. On day 60 of ensiling, the Flieg’s point of high density and medium density were higher than the low density. During aerobic exposure, the continuous lactic acid decrease and pH increase were observed in all silage. The aerobic bacteria and yeasts count in the high density and medium density were lower than that in the low density. The aerobic stability of the high density (26 h) and the medium density (24 h) were higher than that of the low density (13 h). It was suggested that if the ensiling density is higher than 600 g/L, it could effectively improve the fermentation quality and aerobic stability of rice straw.

This study investigates the opening size effect of a novel short fiber reinforced quasi-isotropic composite laminate with unidirectionally arrayed chopped strands (UACS) under tensile load by approaches of simulations and experiments. The layup of [45/0/-45/90]s was used to fabricate novel carbon fiber laminates with various central hole diameter from 4 mm to 10 mm. Low notch sensitivity and strength reduction rate were observed in UACS open-hole laminates. It was found that when the width diameter ratio (d/w) of UACS laminate with open-hole is less than 20%, 80% of the original strength can be maintained, which can greatly reduce the design safety factor. Compared with continuous fiber laminates, high increase rate of strength reduction rate was induced by bi-angled slits with the increasing of open hole diameter. The strain distribution around the hole was affected significantly by bi-angled slits, which also inhibited the initiation and propagation of delamination effectively. The strain concentration factor of UACS laminates increases with the increasing open hole diameter. The damage progression of notched and unnotched UACS laminates were analyzed by simulation method, which are consistent well with experimental results.

Taking a DCT transmission preliminary gear 3 to 2 as an example，through the analysis of rattle theory and influence factors，the precise comparison verification result between simulation and test of rattle sensitivity are obtained. The quantitative evaluation target of rattle sensitivity is formulated，and different flywheels are matched for real vehicle verification. This research has theoretical significance and practical value for guiding low-noise design and matching engine platform of transmission.

Deep learning excels in the identification of specific plant diseases. However, dealing with multi-domain datasets, which encompass a variety of categories, presents challenges due to limited data availability. (1) Background: In real-world scenarios, data distribution is uneven, the scale continues to grow, new categories emerge, and a phenomenon known as ‘catastrophic forgetting’ occurs. Models rely on a substantial amount of data for labeling and training. (2) Methods: We introduce a two-stage approach. The first stage is the scalable feature learning phase, where the previous feature representation is fixed. Through a new feature extractor, incoming and stored data are trained to expand features. In the second stage, by introducing an auxiliary loss to determine whether key parameters are retained, we reduce the instability of weight parameters. This maintains the separability of old features and encourages the model to learn new concepts, diversity, and discriminative features. (3) Results: Our findings indicate that when the data landscape shifts, recognition accuracy in multi-task continual learning, leveraging the simultaneous availability of datasets, significantly outperforms single convolutional networks and multi-task learning models. (4) Conclusions: Our method advances continual learning towards practical applications. It is particularly effective in mitigating catastrophic forgetting in multi-domain datasets and enhancing the robustness of deep-learning models.

Abstract Origami enables the folding of objects into a variety of shapes. This paper presents a swimming robot using the folding and unfolding motion of a tabular origami. A squid‐inspired jet propulsion mechanism is used to design an origami swimming robot. The swimming robot utilizes a pulsed‐jet mechanism through the folding motion of the tubular origami structure, which could deform and undulate to provide propulsion for the swimming robot. The robot is incorporated in a stable body, while both swimming forward and steering are generated by the folding motion of the origami structure and undulating fins. An analytical model to investigate the jet propulsion mechanism to capture the relationship between the motor's speed and the generated thrust is developed. The prototype of the swimming robot is fabricated by 3D printing, and experiments verify the robot's components and assembly system. The results show that the swimming robot could achieve swimming forward by folding origami, which could provide unique advantages over other existing underwater propulsion technologies, including scalability and water treatment capabilities. The origami structure provides a novel and simple propulsion mechanism for the swimming robot here, and the mechanism promotes the development of a new class of independent swimming robots.

Abstract Background Due to its rarity, duodenal papillary carcinoma (DPC) is seldom studied as a unique disease and no specific molecular features or treatment guidelines are provided. Methods Whole‐exome sequencing was performed to gain new insights into the DPC mutation landscape and to identify potential signalling pathways and therapeutic targets. Mechanistically, immunohistochemistry (IHC), immunofluorescence, RNA‐seq, ATAC‐seq and in vitro cell function experiments were performed to confirm the underlying mechanisms. Results We described the mutational landscape of DPC for the first time as a group of rare tumours with a high frequency of dysregulation in the chromatin remodelling pathway, particularly PBRM1‐inactivating mutations that are significantly higher than duodenal adenocarcinomas and ampullary adenocarcinoma (27% vs. 0% vs. 7%, p

With the widespread success of deep learning in biomedical image segmentation, domain shift becomes a critical and challenging problem, as the gap between two domains can severely affect model performance when deployed to unseen data with heterogeneous features. To alleviate this problem, we present a novel unsupervised domain adaptation network, for generalizing models learned from the labeled source domain to the unlabeled target domain for cross-modality biomedical image segmentation. Specifically, our approach consists of two key modules, a conditional domain discriminator (CDD) and a category-centric prototype aligner (CCPA). The CDD, extended from conditional domain adversarial networks in classifier tasks, is effective and robust in handling complex cross-modality biomedical images. The CCPA, improved from the graph-induced prototype alignment mechanism in cross-domain object detection, can exploit precise instance-level features through an elaborate prototype representation. In addition, it can address the negative effect of class imbalance via entropy-based loss. Extensive experiments on a public benchmark for the cardiac substructure segmentation task demonstrate that our method significantly improves performance on the target domain.

For trees, leaves are often used for identification, but the shape of leaves changes greatly, bark will be another identifying feature. However, it is difficult to recognize by a single organ when there are intra class differences and inter class similarities between leaves or bark. So we fuse features of leaf and bark. Firstly, we collected 17 species of leaves and bark of trees through field shooting and web crawling. Then propose a method of combining convolution neural network (CNN) with cascade fusion, additive fusion algorithm, bilinear fusion and score level fusion. Finally, the features extracted from the leaves and bark are fused in the ReLu layer and Fully connected layer. The method was compared with single organ recognition, Support Vector Machines (SVM), and existing fusion methods, results show that the two organ fusion method proposed are better than the other recognition methods, and recognition accuracy is 87.86%. For similar trees, when it is impossible to accurately determine its species by a single organ, the fusion of two organs can effectively improve this situation.

Antiferromagnetic materials are potentially useful for spintronic applications. Here, the authors report high thermoelectric power value of 390 μV/K Seebeck coefficient in IrMn-based half magnetic tunnel junctions at room temperature.

Central nervous system leukemia (CNS-L) is caused by leukemic cells infiltrating into the meninges or brain parenchyma and remains the main reason for disease relapse. Currently, it is hard to detect CNS-L accurately by clinically available imaging models due to the relatively low amount of tumor cells, confined blood supply, and the inferior glucose metabolism intensity. Recently, integrin α6-laminin interactions have been identified to mediate CNS-L, which suggests that integrin α6 may be a promising molecular imaging target for the detection of CNS-L. The acute lymphoblastic leukemia (ALL) cell line NALM6 stabled and transfected with luciferase was used to establish the CNS-L mouse model. CNS-L-bearing mice were monitored and confirmed by bioluminescence imaging. Three of our previously developed integrin α6-targeted peptide-based molecular imaging agents, Cy5-S5 for near-infrared fluorescence (NIRF), Gd-S5 for magnetic resonance (MR), and 18F-S5 for positron emission tomography (PET) imaging, were employed for the molecular imaging of these CNS-L-bearing mice. Bioluminescence imaging showed a local intensive signal in the heads among CNS-L-bearing mice; meanwhile, Cy5-S5/NIRF imaging produced intensive fluorescence intensity in the same head regions. Moreover, Gd-S5/MR imaging generated superior MR signal enhancement at the site of meninges, which were located between the skull bone and brain parenchyma. Comparatively, MR imaging with the clinically available MR enhancer Gd-DTPA did not produce the distinguishable MR signal in the same head regions. Additionally, 18F-S5/PET imaging also generated focal radio-concentration at the same head regions, which generated nearly 5-times tumor-to-background ratio compared to the clinically available PET radiotracer 18F-FDG. Finally, pathological examination identified layer-displayed leukemic cells in the superficial part of the brain parenchyma tissue, and immunohistochemical staining confirmed the overexpression of the integrin α6 within the lesion. These findings suggest the potential application of these integrin α6-targeted molecular imaging agents for the accurate detection of CNS-L.

ObjectiveTo compare the efficacy of intravenous (IV) lidocaine with standard analgesics (NSAIDS, opioids) for pain control due to any cause in the emergency department.MethodsThe electronic databases of PubMed, Embase, ScienceDirect, CENTRAL, and Google Scholar were explored from 1st January 2000 to 30th March 2021 and randomized controlled trials (RCTs) comparing IV lidocaine with a control group of standard analgesics were included.ResultsTwelve RCTs including 1,351 patients were included. The cause of pain included abdominal pain, renal or biliary colic, traumatic pain, radicular low back pain, critical limb ischemia, migraine, tension-type headache, and pain of unknown origin. On pooled analysis, we found no statistically significant difference in pain scores between IV lidocaine and control group at 15 min (MD: −0.24 95% CI: −1.08, 0.61 I2 = 81% p = 0.59), 30 min (MD: −0.24 95% CI: −1.03, 0.55 I2 = 86% p = 0.55), 45 min (MD: 0.31 95% CI: −0.66, 1.29 I2 = 66% p = 0.53), and 60 min (MD: 0.59 95% CI: −0.26, 1.44 I2 = 75% p = 0.18). There was no statistically significant difference in the need for rescue analgesics between the two groups (OR: 1.45 95% CI: 0.82, 2.56 I2 = 41% p = 0.20), but on subgroup analysis, the need for rescue analgesics was significantly higher with IV lidocaine in studies on abdominal pain but not for musculoskeletal pain. On meta-analysis, there was no statistically significant difference in the incidence of side-effects between the two study groups (OR: 1.09 95% CI: 0.59, 2.02 I2 = 48% p = 0.78).ConclusionIV lidocaine can be considered as an alternative analgesic for pain control in the ED. However, its efficacy may not be higher than standard analgesics. Further RCTs with a large sample size are needed to corroborate the current conclusions.

Summary: Reducing CO2 emissions from coal-fired electricity generation in China is critical to limit global warming. Long-term projections of China's electricity supply tend to assume that coal generation will be a mainstay of China's electricity system through 2050, due to limitations in the scalability of hydropower, nuclear, and natural gas generation and the commercial availability of carbon capture and storage. This paper examines the resource, economic, and institutional implications of reducing and replacing coal generation in China with mostly renewable energy and energy storage by 2040. We find that the scale of solar, wind, and storage resources needed to do so is on the order of 100–150 GW/year of solar and wind capacity and 15 GW/year of energy storage from 2020 to 2025, growing to 250 GW/year and 90 GW/year, respectively, from 2025 to 2040. We then also evaluate the sensitivities if coal plants are retired by 2050.

Power quality issues have become one of the most important issue for researchers to concern. In this paper, simulation and experiment of algorithm and circuit design of Unified Power Quality Conditioner (UPQC) are provided. Control algorithm and topology design of one UPQC which includes active power filter (APF) and dynamic voltage restorer (DVR) are introduced. Stability condition of the filter unit is deduced and proved by Routh stability criterion. Simulation for APF and DVR is carried out in PSCAD to show the proposed control strategy. Experiments such as current tracking, harmonic detection and compensation and voltage drop compensation are provided in details. Experimental results show that the proposed control method and the designed topology are effective and practical.

BackgroundPulmonary lymphoepithelioma-like carcinoma (LELC) is a rare subtype of lung cancer with less than 700 cases being reported in the literature, and no specialized treatment guidelines have been established. The prognostic significance of metabolic parameters on 18F-FDG PET in pulmonary LELC still remains unknown.MethodsFrom July 2011 to September 2020, 76 pulmonary LELC patients undergoing pre-treatment 18F-FDG PET imaging were enrolled, and PET parameters including maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated. In addition, whole-body tumor burdens were categorized into primary tumor lesion (PRL), thoracic lymph-node lesion (TRLN), and distant metastasis (DM) for respective metabolic parameters acquisition. ROC curves were generated to evaluate the predictive performance of the PET parameters, and correlations between tumor burdens of the different regional lesions were analyzed using linear correlation analysis. The prognostic significance for progression-free survival (PFS) and overall survival (OS) was assessed using univariate and multivariate survival analyses.ResultsTumor stage, pre-/post-treatment serum EBV-DNA copies, SUVmax (cutoff 17.5), MTV, and TLG were significantly associated with PFS and OS in univariate analysis. MTV and TLG (AUC = 0.862 and 0.857, respectively) showed significantly higher predictive value than SUVmax (AUC = 0.754) and remained independent prognostic indicators for PFS in multivariate analysis (P = 0.026 and 0.019, respectively). Besides, non-colinearity was detected between metabolic burdens of the different regional lesions. MTV-PRL, MTV-DM, TLG-PRL, and TLG-DM were identified to be independent prognostic factors for PFS and OS, whereas MTV-TRLN and TLG-TRLN were not.ConclusionThe study demonstrated that MTV and TLG had independent prognostic significance for pulmonary LELC, which supported the incorporation of 18F-FDG PET imaging into clinical treatment protocols for pulmonary LELC and implied multi-disciplinary cooperation for primary and distant metastatic lesions to further improve prognosis.

China has set a goal to achieve peak CO2 emissions before 2030 and carbon neutrality by 2060. To achieve the goals of carbon peak and carbon neutrality, China needs to address the challenge of the large and still growing CO2 emission base. This paper investigated the energy consumption and CO2 emission in Beijing from 2020-2035 based on the energy elasticity coefficient and contribution value of the sub-energy increment (CVSI) method. Beijing is one of the first cities in China to propose the "carbon peak" target as of 2020. From 2020 Beijing will strive to achieve the goal of carbon neutrality. The results show that in 2035 the CO2 emission in Beijing may drop to 50% of 2020. This decline would be affected by economic growth, energy efficiency and the proportion of renewable energy use. Beijing's energy supply mainly comes from outside the region. Therefore, for Beijing, in addition to increasing the proportion of renewable energy sources outside the region, its own energy acceptance also needs to be strengthened, including strengthening energy storage construction, actively researching and promoting carbon capture and utilization of gas-fired units, which are effective ways to achieve carbon neutrality target.

The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.

Objective: The present study aimed at investigating the clinical risk factors for COVID-19 patients developing from moderate condition to severe condition, and providing reference for early intervention and prognosis.Methods: We collected the clinical data of 24 patients with moderate-to-severe COVID-19 who were admitted to the isolation ward of the First Affiliated Hospital of Bengbu Medical College from January, 2020 to February 20, 2020, and evaluated the data of clinical characteristics, blood test results, inflammatory index, chest CT imaging characteristics, and antiviral treatment, comparing this with the clinical data of 41 patients with moderate condition in the same period. From this comparison we thus summarized the current knowledge of potential risk factors for COVID-19 patients developing from moderate to severe condition.Results: (1) Clinical characteristics: The moderate-to-severe group and the moderate group in terms of combined common underlying diseases and respiratory frequency showed significant difference statistically (t-value were 13.32, 6.17, respectively, P < 0.05), while no significant difference between the two groups in gender, age, or clinical symptoms was statistically observed(P > 0.05).(2) Analysis of blood test results: The lymphocyte count and plasma albumin of the moderate-to-severe group were significantly lower than those of the moderate group (t-values were 4.16, 4.11, respectively, P < 0.05), and the blood glucose and urea of the moderate-to-severe group were significantly higher than those of the moderate group (t-value were 3.27, 4.19, respectively, P < 0.05). However, there was no significant difference in terms of white blood cell count (WBC), platelet count (PLT), and glutamic-pyruvic transaminase (GPT) (P > 0.05).(3) Comparison of inflammatory indicators: The level of IL-6 and CRP of the moderate-to-severe group were significantly higher than those of the moderate group (t-values were 2.84, 4.88, respectively, P < 0.05).(4) Imaging comparison: As for patients with moderate COVID-19, the imaging manifestations were the concurrence of ground-glass opacity, patchy shadow, and consolidation shadow in both lungs, diffuse ground-glass opacity in both lungs accompanied by air bronchogram, and large area consolidation of both lungs with pulmonary interstitial changes. The possibility for these patients to develop into severe condition increased, and the differences were statistically significant (t = 10.92, P < 0.05).(5) Clinical antiviral treatment: There was no statistically significant difference in the combination of two or three antiviral drugs between the two groups (χ2 = 0.05, P > 0.05).Conclusion: Current evidence suggested that the combination of common underlying diseases, respiratory frequency, lymphocyte count, blood glucose, albumin, urea level, inflammatory factors (CRP, IL-6), and imaging manifestations collectively contributed to the potential risk factors for the development of COVID-19 from moderate condition to severe condition. Particular attention should be paid to early detection and intervention during clinical work, which will be of vital significance to the ascent of the recovery rate as well as the reduction of mortality.

Summary: Spin waves offer promising perspectives as information carriers for future computational architectures beyond conventional complementary metal-oxide-semiconductor (CMOS) technology, owing to their benefits for device minimizations and low-ohmic losses. Although plenty of magnonic devices have been proposed previously, scalable nanoscale networks based on spin waves are still missing. Here, we demonstrate a reprogrammable two-dimensional spin wave network by combining the chiral exchange spin waves and chiral domain walls. The spin-wave network can be extended to two dimensions and offers unprecedented control of exchange spin waves. Each cell in the network can excite, transmit, and detect spin waves independently in the chiral domain wall, and spin-wave logics are also demonstrated. Our results open up perspectives for integrating spin waves into future logic and computing circuits and networks.

Recent semantic segmentation networks mainly focus on how to fuse multi-level features from classification networks to improve segmentation accuracy. Some researches evenly emphasize the correlation of pixels in a global region, such as conditional random field (CRF). However, the strong correlation feature of pixels in a limited region is less considered in the previous researches and the remedy ability of the correlation of local pixels in semantic segmentation is severely ignored. To deal with this problem, we introduce a hierarchical adjacency dependent network (HadNet), in which an adjacency dependency module (ADM) is constructed by calculating and utilizing the impact fact of the pixel in different directions to classify the pixel. We explored the correlation of adjacent pixels and feature coverage in different feature levels to improve the segmentation accuracy. We evaluate our method on the popular Pascal VOC 2012 test set, and achieve a comparable result of mIOU accuracy of 79.8% with the state of art methods, such as DeepLabv3+ and Exfuse. Further, we discuss and analyze the data distribution of COCO dataset for deeply understanding the feature correlation and coverage in semantic segmentation.

As an important component in dialogue system, Dialogue Act Recognition (DAR) has attracted much attention of researchers. Most of the existing approaches only focus on the information of utterances, but relatively few investigate the roles of DA labels in different layers of a conversation and also some semantic inference in irregular conversations, such as questions and answers skipping back and forth, has not been probed properly so that some dialogue acts are not always recognized correctly. To overcome the aforementioned deficiencies, we propose a hierarchical project method, in which DA labels are embedded in three levels (word, utterance, and DA) to calculate the importance of each word, provide contextual and pragmatic information, and guide the model to generate the category of utterance, respectively. Both multiple attention mechanism and semantic update mechanism are applied to infer the more accurate representation of utterance for DA recognition in a general case. Experimental results show that our model can achieve comparable performance to the state-of-the-art models with less extra information.

Short-wavelength spin waves with high group velocity are one of the key ingredients for the spin-wave based memory-logics. Here the authors demonstrate the propagation of spin waves with wavelength down to 50 nm and group velocity up to 2600 m s−1 using ferromagnetic nanowires grown on a thin Y3Fe5O12 film strip structure.

High heterogeneity and nonuniformly distributed multiscale pore systems are two characteristics of the unconventional reservoirs, which lead to very complex transport mechanisms. Limited by inadequate computational capability and imaging field of view, flow simulation cannot be directly performed on complex pore structures. The traditional methods usually coarsen the grid to reduce the computational load but will lead to the missing microstructure information and inaccurate simulation results. To develop a better understanding of flow properties in unconventional reservoirs, this study proposed a new upscaling method integrated gray lattice Boltzmann method (GLBM) and pore network model (PNM), accounting for the fluid flow in heterogeneous porous media. This method can reasonably reduce the computational loads while preserving certain micropore characteristics. Verifications are conducted by comparing the simulation and experimental results on tight sandstones, and good agreements are achieved. The proposed method is proven to be capable of estimating bulk properties in highly heterogenous unconventional reservoirs. This method could contribute to the development of multiscale pore structure characterizations and enhance the understandings of fluid flow mechanisms in unconventional reservoirs.

We studied the influence of doping HfTe5 with 5% Ti on electric (resistivity and the Hall effect) and thermoelectric transport properties (the Seebeck coefficient, magneto-thermoelectric power, and Nernst effect). The properties of 5% Ti-doped HfTe5 do not change much. Nernst coefficients larger than magneto-thermoelectric power were observed in a temperature range near the compensation temperature at which the Seebeck coefficient vanishes. This indicates that a two-carrier conduction model could describe our experimental results. Owing to the high thermoelectric performance, thermopiles were made on a printed circuit board based on doped and undoped HfTe5. A large Seebeck voltage was obtained at room temperature. It became even larger in a low temperature range and presented strong magnetic field dependence.

This study aimed to investigate the therapeutic effects of aspirin (ASA) and its potential mechanisms of action in monocrotaline (MCT)-induced pulmonary arterial hypertension (PAH) in rats. PAH was induced in a rat model by a single intraperitoneal (IP) injection of MCT. Saline was injected in a control group. Two weeks following MCT injection, right ventricular systolic pressure (RVSP) and systolic blood pressure (SBP) were measured in six rats from each group to confirm establishment of a PAH model. The remaining MCT-treated rats were randomly allocated to receive IP injection of saline, ASA, or ERK1/2 inhibitor PD98059. Four weeks following treatment, RVSP was measured and all rats were sacrificed for histological study. There was no significant difference in SBP in any group two weeks following MCT administration. Nonetheless RVSP was significantly increased in the MCT group compared with the control group. At 6 weeks, ASA treatment remarkably attenuated MCT-induced increased RVSP, RV hypertrophy, and pulmonary artery remodeling compared with the MCT group. The density of pulmonary capillaries in ASA-treated rats was also dramatically increased. Treatment with ASA significantly inhibited the increased p-ERK1/2 and restored the impaired endothelial nitric oxide synthase (eNOS) in MCT-treated rats. This study demonstrated that ASA distinctively attenuates MCT-induced PAH by inhibition of the ERK1/2 signaling pathway.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

In bacteria, glycogen plays important roles in carbon and energy storage. Its structure has recently been linked with bacterial environmental durability. Among the essential genes for bacterial glycogen metabolism, the glgB-encoded branching enzyme GBE plays an essential role in forming α-1,6-glycosidic branching points, and determines the unique branching patterns in glycogen. Previously, evolutionary analysis of a small sets of GBEs based on their N-terminal domain organization revealed that two types of GBEs might exist: (1) Type 1 GBE with both N1 and N2 (also known as CBM48) domains and (2) Type 2 GBE with only the N2 domain. In this study, we initially analyzed N-terminal domains of 169 manually reviewed bacterial GBEs based on hidden Markov models. A previously unreported group of GBEs (Type 3) with around 100 amino acids ahead of the N1 domains was identified. Phylogenetic analysis found clustered patterns of GBE types in certain bacterial phyla, with the shorter, Type 2 GBEs predominantly found in Gram-positive species, while the longer Type 1 GBEs are found in Gram-negative species. Several in vitro studies have linked N1 domain with transfer of short oligosaccharide chains during glycogen formation, which could lead to small and compact glycogen structures. Compact glycogen degrades more slowly and, as a result, may serve as a durable energy reserve, contributing to the enhanced environmental persistence for bacteria. We were therefore interested in classifying GBEs based on their N-terminal domain via large-scale sequence analysis. In addition, we set to understand the evolutionary patterns of different GBEs through phylogenetic analysis at species and sequence levels. Three-dimensional modeling of GBE N-termini was also performed for structural comparisons. A further study of 9,387 GBE sequences identified 147 GBEs that might belong to a possibly novel group of Type 3 GBE, most of which fall into the phylum of Actinobacteria. We also attempted to correlate glycogen average chain length (ACL) with GBE types. However, no significant conclusions were drawn due to limited data availability. In sum, our study systematically investigated bacterial GBEs in terms of domain organizations from evolutionary point of view, which provides guidance for further experimental study of GBE N-terminal functions in glycogen structure and bacterial physiology.

As a new carbon-based nanomaterial, graphene has exhibited unique advantages in significantly improving the combination properties of traditional polymer hydrogels. The specific properties of graphene, such as high electrical conductivity, high thermal conductivity and excellent mechanical properties, have made graphene not only a gelator to self-assemble into the graphene-based hydrogels (GBH) with extraordinary electromechanical performance, but also a filler to blend with small molecules and macromolecules for the preparation of multifunctional GBH. It fully exploits the practical applications of traditional hydrogels. This review summarizes the preparation methods, properties, and the applications of GBH. Further developments and challenges of GBH are also prospected.

In order to seize market opportunities, rapid service fulfillment and high quality network service become the operators' urgently needs to solve problems. In order to achieve the customer service end-to-end rapid fulfillment, a network data configuration activation method based on SOA was proposed, and the network resource data configuration & activation system was constructed, the one-click end-to-end service fulfillment was realized. By this method, not only greatly improves the efficiency of service fulfillment, also avoids the artificial network data configuration errors in the process of service fulfillment.

This paper conducts panel analysis to evaluate the effects of a structural economic shift from the industrial to the tertiary sector, a reduction in industrial overcapacity, and improvements in energy efficiency on energy consumption using data for 30 Chinese provinces from 1995 to 2015. We find that, at the national level, the structural shift to the tertiary sector, the reduction in cement and steel production, and the increase in energy efficiency in the industrial sector all have statistically significantly negative effects. We also divide the sample into three geographic and economic regions to evaluate regional differences. We find that the gross domestic product (GDP) share of the tertiary sector shows its greatest impact on reducing energy consumption in the eastern region, a decline in heavy industry production would reduce energy demand more in the central region, and improvement in industrial electricity efficiency would also help reduce energy consumption the most in eastern China. We also forecast energy consumption in China will reach 4.8−4.9 billion tonnes of coal equivalent (tce) in 2020 and further grow to 5.0−5.4 billion tce in 2030.

In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier.

Thiourea derivatives demonstrate potent cytotoxic activity against various leukemias and many tumor cell lines. In our previous study, the combination of thiourea and phosphonate has been proven as an effective strategy for developing antitumor agents. Herein, we synthesized and evaluated a series of novel chiral dipeptide thioureas containing an α-aminophosphonate moiety as antitumor agents. Finally, we developed novel dipeptide thioureas 11d and 11f that showed comparable inhibition with that of Cisplatin against BGC-823 and A-549 cells, respectively.