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Hydrocarbons are essential base chemicals as energy carriers and starting materials for chemical manufacture. So-called fatty acid photodecarboxylases (FAPs) represent interesting catalysts for the conversion of natural fatty acids into hydrocarbons thereby giving access to alkanes from renewable feedstock. Today, however, only few FAPs are known. In the current study we report a new FAP from the marine organism Micractinium conductrix (McFAP). In contrast to currently known FAPs McFAP exhibits high catalytic activity towards short and medium fatty acids. Recombinant expression and basic biochemical characterisation of this new member of the FAP family is reported.

We introduce the first learning-based reconstructability predictor to improve view and path planning for large-scale 3D urban scene acquisition using unmanned drones. In contrast to previous heuristic approaches, our method learns a model that explicitly predicts how well a 3D urban scene will be reconstructed from a set of viewpoints. To make such a model trainable and simultaneously applicable to drone path planning, we simulate the proxy-based 3D scene reconstruction during training to set up the prediction. Specifically, the neural network we design is trained to predict the scene reconstructability as a function of the proxy geometry, a set of viewpoints, and optionally a series of scene images acquired in flight. To reconstruct a new urban scene, we first build the 3D scene proxy, then rely on the predicted reconstruction quality and uncertainty measures by our network, based off of the proxy geometry, to guide the drone path planning. We demonstrate that our data-driven reconstructability predictions are more closely correlated to the true reconstruction quality than prior heuristic measures. Further, our learned predictor can be easily integrated into existing path planners to yield improvements. Finally, we devise a new iterative view planning framework, based on the learned reconstructability, and show superior performance of the new planner when reconstructing both synthetic and real scenes., Accepted by SIGGRAPH Asia 2022

tRNA molecules contain dense, abundant modifications that affect tRNA structure, stability, mRNA decoding and tsRNA formation. tRNA modifications and related enzymes are responsive to environmental cues and are associated with a range of physiological and pathological processes. However, there is a lack of resources that can be used to mine and analyse these dynamically changing tRNA modifications. In this study, we established tModBase (https://www.tmodbase.com/) for deciphering the landscape of tRNA modification profiles from epitranscriptome data. We analysed 103 datasets generated with second- and third-generation sequencing technologies and illustrated the misincorporation and termination signals of tRNA modification sites in ten species. We thus systematically demonstrate the modification profiles across different tissues/cell lines and summarize the characteristics of tRNA-associated human diseases. By integrating transcriptome data from 32 cancers, we developed novel tools for analysing the relationships between tRNA modifications and RNA modification enzymes, the expression of 1442 tRNA-derived small RNAs (tsRNAs), and 654 DNA variations. Our database will provide new insights into the features of tRNA modifications and the biological pathways in which they participate.

Objective: The focus of this study was the six-transmembrane epithelial antigen of the prostate 4 (STEAP4) gene, on the basis of the cloned goat STEAP4 gene sequence. Its molecular and expression characteristics were analyzed, and its influence on the differentiation of goat subcutaneous adipocytes was explored through overexpression. Method: Reverse-transcription PCR (RT-PCR) was used to clone the goat STEAP4 sequence, and online tools were used to analyze the molecular characteristic. Real-time quantitative PCR (qPCR) was used to detect the expression level of STEAP4 in goat tissues and subcutaneous adipocyte differentiation. Liposome transfection, BODIPY, Oil Red O staining, and qPCR were used to explore the effect of overexpression of STEAP4 on adipocyte differentiation. Results: The cloned goat STEAP4 gene sequence was 1388 bp, and the complete coding sequence (CDS) region was 1197 bp, which encoded a total of 398 amino acids. Compared with the predicted sequence (XM_005679300.3), there were three base mutations in the CDS region of goat STEAP4, A188G, T281C, and A507G. Among them, A507G changed the amino acid at position 170 from Ile to Val. Analysis of the physical and chemical properties of the protein showed that STEAP4 was a stable hydrophilic basic protein. STEAP4 gene expression level was highest in goat liver tissue (P), followed by lung and back subcutaneous adipose tissue. STEAP4 showed different expression levels in goat subcutaneous adipocytes at different times during the induction of differentiation. The expression in the late stage of differentiation was higher than that before differentiation and lowest at 12 h (P). Overexpression of STEAP4 promoted the accumulation of intracellular lipid droplets; C/EBPβ (CCAAT enhancer binding protein) was extremely significantly up-regulated (P), and aP2 (fatty acid binding protein) was significantly up-regulated (P). Conclusion: Overexpression of STEAP4 could promote the differentiation of goat subcutaneous preadipocytes. This study lays the foundation for an in-depth study of the role of STEAP4 in goat lipid deposition.

This paper proposes a novel color medical image encryption method based on mean shift algorithm and fractional-order hyperchaotic system. Firstly, color medical images were divided into lesion area, detection area, and edge area by using mean shift clustering. Low-value pixels and zero-value pixels in the edge region are abandoned to improve the encryption efficiency. Secondly, the doctor-patient information is embedded into the pixel matrix of the lesion region by using discrete wavelet transform and singular value decomposition. Thirdly, the pixels of the lesion region and the detection region are processed by using fractional-order hyperchaotic system. Finally, the color medical image embedded with doctor-patient information can be quickly encrypted. Security analysis shows that the encryption method has not only strong robustness and security, but also good performance against various attacks. SPNA and CA results: (a) s1-DE-SPNA, (b) s2-DE-SPNA, (c) s3-DE-SPNA, (d) s4-DE-SPNA; (e) s1-DE-CA, (f) s2-DE-CA, (g) s3-DE-CA, (h) s4-DE-CA.

We introduce neural dual contouring (NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC). Like traditional DC, it produces exactly one vertex per grid cell and one quad for each grid edge intersection, a natural and efficient structure for reproducing sharp features. However, rather than computing vertex locations and edge crossings with hand-crafted functions that depend directly on difficult-to-obtain surface gradients, NDC uses a neural network to predict them. As a result, NDC can be trained to produce meshes from signed or unsigned distance fields, binary voxel grids, or point clouds (with or without normals); and it can produce open surfaces in cases where the input represents a sheet or partial surface. During experiments with five prominent datasets, we find that NDC, when trained on one of the datasets, generalizes well to the others. Furthermore, NDC provides better surface reconstruction accuracy, feature preservation, output complexity, triangle quality, and inference time in comparison to previous learned (e.g., neural marching cubes, convolutional occupancy networks) and traditional (e.g., Poisson) methods. Code and data are available at https://github.com/czq142857/NDC., Comment: Accepted to SIGGRAPH (journal) 2022. Code: https://github.com/czq142857/NDC

In order to investigate the influence of the back-propagated pressure waves originated from a pulse detonation combustor on the operating characteristics of the compressor in the pulse detonation turbine engine, a model consist of a centrifugal compressor and an isolation section which was usually utilized to decrease the peak value of the back-propagated pressure waves was set up. Numerical simulations were carried out to study the interactions between the centrifugal compressor and the back-propagated pressure waves under different operating conditions. The matching characteristics of the compressor under the influence of the back-propagated pressure waves were analyzed in a detonation cycle. The results showed that the back-propagated pressure waves would push the shock wave in the isolator towards the throat position, but the shock wave did not pass through the throat for the compressor corrected speed of n = 100% and n = 105%. For these two cases, the back-propagated pressure waves were completely isolated and the operating characteristics of the compressor were the same as those under the steady outlet conditions. For the corrected speed of n = 50% and n = 90%, the shock wave in the isolator section was pushed out of the throat and the supersonic flow in the isolator section was destroyed under the influence of the back-propagated pressure waves. The back-propagated pressure waves would propagate upstream into the compressor. Consequently, the instantaneous mass flow rate and the torque acting on the centrifugal impeller was decreased. The work required by the compressor was also reduced. The equivalent isentropic efficiency of the compressor was decreased. The separation or backflow in the compressor caused by the back-propagated pressure waves would results in an equivalent isentropic efficiency loss of 3.2% points for the case of n = 90% and 15.6% points for the case of n = 50%.

A twin rotors concentric face-gear power-split transmission system (TRCFGPSTS) applied to the core transmission mechanism of advanced aviation twin rotors helicopter was studied. The effects of torsional stiffness and support stiffness on the load-sharing characteristics of system were mainly studied. By comprehensively considering the factors such as manufacturing error and installation error, meshing stiffness, torsional stiffness, support stiffness and elastic deformation, and according to the power flow double closed-loop mechanical characteristics of this system, the static double closed-loop torsional angle elastic deformation coordination conditions are derived. The static load-sharing model was established by combining the moment balance equation of system. The load-sharing coefficient (LSC) and sensitivity parameters characterizing the load distribution of system were obtained. The effects of the torsional stiffness of the split-torsion shaft and the double-torsion shaft and the support stiffness of each gear on the load-sharing characteristics of the system were analyzed. The results show that the torsional stiffness of the split-torsion shaft and the double-torsion shaft has a great influence on LSC and sensitivity of the system, when its value is in the range of (1–1.6) × 105 N·m /rad and (2.4–4.3) ×105 N·m /rad, the system can meet the requirements of ± 5% of load-sharing characteristics under service conditions. Reducing the torsional stiffness of the split-torsion shaft can improve the load-sharing characteristics of the system, and the torsional stiffness of the right-branching double-shaft has little effect on the load-sharing characteristics of the left-branching of the system. The support stiffness of I-stage small cone angle face gear and Ⅲ-stage face gear is the most sensitive to LSC. With the increase of the support stiffness, the LSC first decreases and then gradually flattens, and the support stiffness of Ⅲ-stage face-gear has little effect on the I-stage LSC. The research results can provide a theoretical basis for the optimal design of load-sharing structure.

Geomorphometry research largely relies on 3-dimentional land surface models, especially for natural landforms with steep slopes and large complex artificial structures. The traditional Digital Elevation Model (DEM) encounters difficulties in representing detailed features in such scenes. To address this issue, this paper introduces a concept of a “true 3D surface” and proposes a multiple Unmanned Aerial Vehicle (UAV) collaborative method for high-quality reconstruction of a true 3D surface. We established an evaluation model of scene reconstruction based on the Multi-View Stereo (MVS) modeling rules, and utilized the Fibonacci approach to select the optimal viewpoints. We also adopted the method of finding the next-best-view for each UAV in turn to extend the multi-UAV trajectory. This method involved trade-offs between energy consumption and scene reconstruction to enhance the contribution of viewpoints for improved reconstruction quality of the true 3D surface. Real flight experiments were conducted over the Wuhan University Teaching Building and the Fangshiwan Bridge in Chibi, which revealed a significant improvement in the completeness and precision of the true 3D surface constructed by our proposed method when compared to vertical photography techniques.

Stroke is an acute cerebrovascular disease that is now the most important cause of death due to brain problems in our country. CircRNAs are RNA circles that have been extensively involved in the disease. We aimed to investigate the mechanism of circ_0129657 in the pathogenesis of stroke. In this study, quantitative real-time polymerase chain reaction (RT-qPCR) and western blot assays were used to assess the expression of circ_0129657, miR-194-5p, and glia maturation factor beta (GMFB). Cell viability was measured by Cell Counting Kit-8 (CCK-8) assay. 5-Ethynyl-2′-Deoxyuridine (EdU) assay was used to detect cell proliferation. Flow cytometry was used to detect cell apoptosis. Dual-luciferase reporter, RNA pull-down, and RNA immunoprecipitation (RIP) assays were used to assess the relationship between miR-194-5p and circ_0129657 or GMFB. Mouse middle cerebral artery occlusion (MCAO) model was applied to mimic the cerebral ischemia/reperfusion injury. Our data showed that the levels of circ_0129657 and GMFB were significantly increased and the expression of miR-194-5p was significantly decreased in oxygen-glucose deprivation (OGD)-induced human brain microvascular endothelial cells (HBMECs). Silencing circ_0129657 expression in OGD-induced HBMECs could promote cell viability and cell proliferation. Moreover, circ_0129657 depletion also could inhibit apoptosis and inflammatory factor secretion. Circ_0129657 functioned as a sponge for miR-194-5p and could regulate GMFB expression via miR-194-5p competition. Furthermore, miR-194-5p downregulation or GMFB restoration could partially reverse the effects of circ_0129657 silencing on cell biological properties in OGD-induced HBMECs. Meanwhile, circ_0129657 knockdown decreased cerebral infarction volume and neurological impairment in MCAO mouse models. In conclusion, our findings suggest that circ_0129657 can inhibit cell proliferation and promote apoptosis and inflammatory factor secretion in HBMECs after oxygen-glucose deprivation via miR-194-5p/GMFB axis, providing evidence that circ_0129657 has the potential as a useful biological molecular marker in the diagnosis of stroke.

IntroductionGlioblastoma is a malignant brain tumor with poor prognosis. Lactate is the main product of tumor cells, and its secretion may relate to immunocytes’ activation. However, its role in glioblastoma is poorly understood. MethodsThis work performed bulk RNA-seq analysis and single cell RNA-seq analysis to explore the role of lactate in glioblastoma progression. Over 1400 glioblastoma samples were grouped into different clusters according to their expression and the results were validated with our own data, the xiangya cohort. Immunocytes infiltration analysis, immunogram and the map of immune checkpoint genes’ expression were applied to analyze the potential connection between the lactate level with tumor immune microenvironment. Furthermore, machine learning algorithms and cell-cell interaction algorithm were introduced to reveal the connection of tumor cells with immunocytes. By co-culturing CD8 T cells with tumor cells, and performing immunohistochemistry on Xiangya cohort samples further validated results from previous analysis.DiscussionIn this work, lactate is proved that contributes to glioblastoma immune suppressive microenvironment. High level of lactate in tumor microenvironment can affect CD8 T cells’ migration and infiltration ratio in glioblastoma. To step further, potential compounds that targets to samples from different groups were also predicted for future exploration.

IntroductionSoil carbon and nutrient contents and their stoichiometric characteristics play a vital role in indicating plant growth and element balance, which can be used to indicate nutrient limitation. However, it has been less studied about their driving factors within forest soils at the regional scale in southern China.MethodsIn this study, soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) were analyzed in the topsoil (0–10 cm) at 345 sampling plots representing different forest types in Guangxi Province.ResultsThe results showed that the mean contents of C, N, and P were 29.80, 2.46, and 0.51 g/kg, respectively, and soil C:N, C:P, and N:P were 13.95, 69.60, and 5.53 respectively. The ratios also showed remarkable correlations with each other. C, N, and P contents and their ratios presented significant differences among different soil and vegetation types. C, N, and P concentrations increased with the increase of elevation and latitude, and decrease with the increase of average annual temperature (MAT). Conversely, C:N showed an opposite trend. C, N, and N:P were also increased with increasing average annual precipitation (MAP). Collectively, soil type, vegetation type, geographical, and climatic factors explained 43.46, 64.02, 68.61, 32.93, 39.64, and 37.87% of the variance in C, N, P, C:N, C:P, and N:P, respectively. For Soil C, both latitude and MAP had strong influences. Soil type was the largest explanation for soil N and P contents. Latitude and longitude were the key factors determining the soil stoichiometric ratios.DiscussionOverall, soil type, geographical and climatic factors were the most vital explanation variables for soil nutrients and their stoichiometric ratios. These results could help improve our understanding of soil stoichiometry within forest ecosystems in southern China.

BackgroundHepatocellular carcinoma (HCC) is one of the most prevalent and deadly malignant tumors with serious clinical and socioeconomic consequences. Although gene therapy holds great promise in the treatment of hepatoma, its clinical applications are hindered by uncontrolled gene transmission and transcription.MethodsThe pY-ads-8-5HRE-cfosp-IFNG plasmid was constructed and identified by double enzyme digestion and gene sequencing. The expression of pYr-ads-8-5HRE-cfosp-IFNG in HepG2 cells was detected by quantitative PCR. PEI-Fe3O4/pYr-ads-8-5HRE-cfosp-IFNG albumin nanospheres were prepared and characterized. In vitro heating test of magnetic albumin nanospheres in an alternating magnetic field (AMF) was carried out. The therapeutic effect of PEI-Fe3O4/pYr-ads-8-5HRE-cfosp-IFNG albumin nanospheres on hepatocellular carcinoma was investigated by cell and animal experiments. After treatment, mice blood was collected for clinical biochemical analysis and histopathological evaluation of major organs was performed to assess potential adverse effects of treatment.ResultsDouble enzyme digestion and gene sequencing showed that the pY-ads-8-5HRE-cfosp-IFNG plasmid was constructed successfully. QPCR results showed that the IFNγ transcript level in the PEI-Fe3O4/pYr-ads-8-5HRE-cfosp-IFNG group was higher than that in the PEI-Fe3O4/pYr-ads-8-cfosp-IFNG group after being treated with hypoxia (PConclusionsThe results indicated the successful construction of the radiation-induced plasmid and demonstrated that the hypoxia enhancer could augment the expression of INFγ in a hypoxia environment. Gene therapy combined with magnetic fluid hyperthermia (MFH) has exhibited excellent outcomes in both cell and animal studies. Our experiments demonstrated that the PEI-Fe3O4/pYr-ads-8-5HRE-cfosp-IFNG albumin nanospheres system is a comprehensive treatment method for hepatoma, which can effectively combine immune genre therapy with hyperthermia.

Background: Elevated expression of Copine-1 (CPNE1) has been proved in various cancers; however, the underlying mechanisms by which it affects clear cell renal cell carcinoma (ccRCC) are unclear.Methods: In this study, we applied multiple bioinformatic databases to analyze the expression and clinical significance of CPNE1 in ccRCC. Co-expression analysis and functional enrichment analysis were investigated by LinkedOmics, cBioPortal and Metascape. The relationships between CPNE1 and tumor immunology were explored using ESTIMATE and CIBERSORT method. In vitro experiments, CCK-8, wound healing, transwell assays and western blotting were conducted to investigate the effects of gain- or loss-of-function of CPNE1 in ccRCC cells.Results: The expression of CPNE1 was notably elevated in ccRCC tissues and cells, and significantly correlated with grade, invasion range, stage and distant metastasis. Kaplan–Meier and Cox regression analysis displayed that CPNE1 expression was an independent prognostic factor for ccRCC patients. Functional enrichment analysis revealed that CPNE1 and its co-expressed genes mainly regulated cancer-related and immune-related pathways. Immune correlation analysis showed that CPNE1 expression was significantly related to immune and estimate scores. CPNE1 expression was positively related to higher infiltrations of immune cells, such as CD8+ T cells, plasma cells and regulatory T cells, exhibited lower infiltrations of neutrophils. Meanwhile, elevated expression of CPNE1 was characterized by high immune infiltration levels, increased expression levels of CD8+ T cell exhaustion markers (CTLA4, PDCD1 and LAG3) and worse response to immunotherapy. In vitro functional studies demonstrated that CPNE1 promoted proliferation, migration and invasion of ccRCC cells through EGFR/STAT3 pathway.Conclusion: CPNE1 is a reliable clinical predictor for the prognosis of ccRCC and promotes proliferation and migration by activating EGFR/STAT3 signaling. Moreover, CPNE1 significantly correlates with immune infiltration in ccRCC.

ObjectivesTo evaluate the efficacy of the Cox-Maze IV procedure (CMP-IV) in combination with valve surgery in patients with both atrial fibrillation (AF) and valvular disease and use machine learning algorithms to identify potential risk factors of AF recurrence.MethodsA total of 1,026 patients with AF and valvular disease from two hospitals were included in the study. 555 patients received the CMP-IV procedure in addition to valve surgery and left atrial appendage ligation (CMP-IV group), while 471 patients only received valve surgery and left atrial appendage ligation (Non-CMP-IV group). Kaplan–Meier analysis was used to calculate the sinus rhythm maintenance rate. 58 variables were selected as variables for each group and 10 machine learning models were developed respectively. The performance of the models was evaluated using five-fold cross-validation and metrics including F1 score, accuracy, precision, and recall. The four best-performing models for each group were selected for further analysis, including feature importance evaluation and SHAP analysis.ResultsThe 5-year sinus rhythm maintenance rate in the CMP-IV group was 82.13% (95% CI: 78.51%, 85.93%), while in the Non-CMP-IV group, it was 13.40% (95% CI: 10.44%, 17.20%). The eXtreme Gradient Boosting (XGBoost), LightGBM, Category Boosting (CatBoost) and Random Fores (RF) models performed the best in the CMP-IV group, with area under the curve (AUC) values of 0.768 (95% CI: 0.742, 0.786), 0.766 (95% CI: 0.744, 0.792), 0.762 (95% CI: 0.723, 0.801), and 0.732 (95% CI: 0.701, 0.763), respectively. In the Non-CMP-IV group, the LightGBM, XGBoost, CatBoost and RF models performed the best, with AUC values of 0.738 (95% CI: 0.699, 0.777), 0.732 (95% CI: 0.694, 0.770), 0.724 (95% CI: 0.668, 0.789), and 0.716 (95% CI: 0.656, 0.774), respectively. Analysis of feature importance and SHAP revealed that duration of AF, preoperative left ventricular ejection fraction, postoperative heart rhythm, preoperative neutrophil-lymphocyte ratio, preoperative left atrial diameter and heart rate were significant factors in AF recurrence.ConclusionCMP-IV is effective in treating AF and multiple machine learning models were successfully developed, and several risk factors were identified for AF recurrence, which may aid clinical decision-making and optimize the individual surgical management of AF.

The COVID-19 pandemic brought considerable change to the practice of radiotherapy. In the meantime, patients are increasingly turning to online resources for health information, with YouTube being one of the biggest platforms. However, little is known about what information is being disseminated to cancer patients about radiotherapy in the context of COVID-19. Therefore, this study aims to characterize and assess YouTube videos on radiotherapy during COVID-19. A YouTube search using the terms “Radiation therapy COVID-19”, “Radiation therapy coronavirus”, “Radiotherapy COVID-19”, and “Radiotherapy coronavirus” was completed using a clear-cache web browser. The top 50 videos were collected from each search. After applying pre-determined exclusion criteria, each video was assessed for general parameters, source, and content. Two raters were used to ensure interrater reliability. One hundred five unique videos resulted from the four searches. Ninety-eight per cent were published in the last year. The median video length was 6 min and 54 s, and the median number of views was 570. Most videos were from the USA (58%). The majority of videos were published by a commercial channel (31%), non-profit organization (28%), or healthcare facility (26%). Forty-two per cent of the videos covered a topic related to radiotherapy during the pandemic. Bias was identified in 6% of videos. YouTube information on radiotherapy during COVID-19 is non-specific and can be misleading. The results of this study highlight the need for healthcare providers to proactively address patient information needs and guide them to appropriate sources of information.

Objective： Bystander first aid can improve the survival rate after out-of-hospital cardiac arrest or trauma, College students are potential bystanders of first aid in society. In the research, we aim to determine the universality of first aid training and the mastery of first aid skills among college students in order to implement first aid. Methods：From January to October 2021, Carry out questionnaire surveys by WeChat push, A random cross-sectional study was conducted on students from five universities in Wenzhou. In order to understand the knowledge and skills of first aid, Respondents accepted the first aid scenario that answered two hypotheses. Results：Among the 1,171 college students interviewed. Among them, 37.23% are males and 62.77% are females; 78.31% have first aid training experience, and 21.69% have no first aid training experience; 71% of college students acquire theoretical knowledge and practical skills through first aid training conducted by the Red Cross, medical emergency centers or hospitals, etc. 22% of college students study through the information network; All college students interviewed expressed their willingness to participate in first aid training. But 21.69% of college students have not received first aid training, The reason for the inability to participate in the training is that they do not understand the ways to participate in first aid training, accounting for 37.01%; Lack of confidence accounted for 19.69%, and lack of time accounted for 35.04%; In the hypothetical emergency scene, In the male group, 48.96% were willing to treat the injured, and 36.19% in the female group. There is a statistical difference between the two (P＜0.01); The junior college student group and the undergraduate student group are 28.09% and 44.09% separately. The medical professional group and the students with the first aid training group are 60.4% and 43.51% separately, The willingness to implement first aid has a clear advantage over the non-medical and non-trained two groups. The comparison between the two groups was statistically significant (P

The high-temperature heat pump steam system (HTHPS) is an effective way to replace the calcining boiler's steam. However, the large temperature span of the system leads to colossal exergy loss of refrigerant in expansion valves and other components. Therefore, it is hoped to adopt the dual-flash vaporization process to improve the system's steam production performance. In this paper, three thermodynamic models and exergy models of HTHPS with a double flash evaporation process are established. Furthermore, this paper uses the multivariate simulated annealing algorithm to calculate the optimal COP of the system. Then, by comparing the single-stage compression (SC) system and the quasi-two-stage compression (QTC) system, the influence of the dual-flash vaporization process on HTHPS is analyzed. The results show that under the same environment, the COP of the dual-flash vaporization process compression system is 23.8% 44.54% higher than that of the SC system. With the increase of the system temperature span, the flash steam supplemental process brings the system to improved thermodynamic performance. Moreover, the exergy model of the dual-flash system is closest to the reversed Carnot cycle, when the evaporation temperature is 50 °C and the condensing temperature is 110 °C.

The key phytohormone auxin is involved in practically every aspect of plant growth and development. Auxin regulates these processes by controlling gene expression through functionally distinct AUXIN RESPONSE FACTORs (ARFs). As a noncanonical ARF, ARF3/ETTIN (ETT) mediates auxin responses to orchestrate multiple developmental processes during the reproductive phase. The arf3 mutation has pleiotropic effects on reproductive development, causing abnormalities in meristem homeostasis, floral determinacy, phyllotaxy, floral organ patterning, gynoecium morphogenesis, ovule development, and self-incompatibility. The importance of ARF3 is also reflected in its precise regulation at the transcriptional, posttranscriptional, translational, and epigenetic levels. Recent studies have shown that ARF3 controls dynamic shoot apical meristem (SAM) maintenance in a non-cell autonomous manner. Here, we summarize the hierarchical regulatory mechanisms by which ARF3 is regulated and the diverse roles of ARF3 regulating developmental processes during the reproductive phase.

PurposeTo establish novel prediction models for predicting acute kidney injury (AKI) after cardiac surgery based on early postoperative biomarkers.Patients and methodsThis study enrolled patients who underwent cardiac surgery in a Chinese tertiary cardiac center and consisted of a discovery cohort (n = 452, from November 2018 to June 2019) and a validation cohort (n = 326, from December 2019 to May 2020). 43 biomarkers were screened using the least absolute shrinkage and selection operator and logistic regression to construct a nomogram model. Three tree-based machine learning models were also established: eXtreme Gradient Boosting (XGBoost), random forest (RF) and deep forest (DF). Model performance was accessed using area under the receiver operating characteristic curve (AUC). AKI was defined according to the Kidney Disease Improving Global Outcomes criteria.ResultsFive biomarkers were identified as independent predictors of AKI and were included in the nomogram: soluble ST2 (sST2), N terminal pro-brain natriuretic peptide (NT-proBNP), heart-type fatty acid binding protein (H-FABP), lactic dehydrogenase (LDH), and uric acid (UA). In the validation cohort, the nomogram achieved good discrimination, with AUC of 0.834. The machine learning models also exhibited adequate discrimination, with AUC of 0.856, 0.850, and 0.836 for DF, RF, and XGBoost, respectively. Both nomogram and machine learning models had well calibrated. The AUC of sST2, NT-proBNP, H-FABP, LDH, and UA to discriminate AKI were 0.670, 0.713, 0.725, 0.704, and 0.749, respectively. In addition, all of these biomarkers were significantly correlated with AKI after adjusting clinical confounders (odds ratio and 95% confidence interval of the third vs. the first tertile: sST2, 3.55 [2.34–5.49], NT-proBNP, 5.50 [3.54–8.71], H-FABP, 6.64 [4.11–11.06], LDH, 7.47 [4.54–12.64], and UA, 8.93 [5.46–15.06]).ConclusionOur study provides a series of novel predictive models and five biomarkers for enhancing the risk stratification of AKI after cardiac surgery.

Background The procession of preadipocytes differentiation into mature adipocytes involves multiple cellular and signal transduction pathways. Recently. a seirces of noncoding RNAs (ncRNAs), including circular RNAs (circRNAs) were proved to play important roles in regulating differentiation of adipocytes. Result In this study, we aimed to identificate the potential circRNAs in the early and late stages of goat intramuscular adipocytes differentiation. Using bioinformatics methods to predict their biological functions and map the circRNA-miRNA interaction network. Over 104 million clean reads in goat intramuscular preadipocytes and adipocytes were mapped, of which16 circRNAs were differentially expressed (DE-circRNAs). Furthermore, we used real-time fluorescent quantitative PCR (qRT-PCR) technology to randomly detect the expression levels of 8 circRNAs among the DE-circRNAs, and our result verifies the accuracy of the RNA-seq data. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the DE-circRNAs, two circRNAs, circ_0005870 and circ_0000946, were found in Focal adhesion and PI3K-Akt signaling pathway. Then we draw the circRNA-miRNA interaction network and obtained the miRNAs that possibly interact with circ_0005870 and circ_0000946. Using TargetScan, miRTarBase and miR-TCDS online databases, we further obtained the mRNAs that may interact with the miRNAs, and generated the final circRNA-miRNA-mRNA interaction network. Combined with the following GO (Gene Ontology) and KEGG enrichment analysis, we obtained 5 key mRNAs related to adipocyte differentiation in our interaction network, which are FOXO3(forkhead box O3), PPP2CA (protein phosphatase 2 catalytic subunit alpha), EEIF4E (eukaryotic translation initiation factor 4), CDK6 (cyclin dependent kinase 6) and ACVR1 (activin A receptor type 1). Conclusions By using Illumina HiSeq and online databases, we generated the final circRNA-miRNA-mRNA interaction network that have valuable functions in adipocyte differentiation. Our work serves as a valuable genomic resource for in-depth exploration of the molecular mechanism of ncRNAs interaction network regulating adipocyte differentiation.

ObjectivesThis retrospective analysis aimed to evaluate the early and midterm outcomes of thoracic endovascular aortic repair (TEVAR) with fenestrated surgeon-modified stent-graft (f-SMSG) for type B aortic dissections (TBAD) involving the aortic arch.MethodsFrom March 2016 to April 2021, 47 consecutive patients were treated using TEVAR with f-SMSG. All patients were diagnosed with TBAD involving the aortic arch.ResultsIn total, 47 patients with TBAD involving the aortic arch were treated with f-SMSGs. There were 21 zone 1 and 26 zone 2 TEVAR, and 65 arteries were revascularized successfully with fenestrations. Technical success was achieved in 46 patients (97.88%). The 30-day estimated survival (± SE) and reintervention was 93.6 ± 1.0% (95% Confidence Interval [CI], 92.6–94.6%) and 91.5 ± 1.2% (95% CI, 90.3–92.7%), respectively. During a median follow-up of 51 months (range, 16–71 months), 1 patient died of rupture of aortic dissection (AD) and 3 patients died of non-aortic-related reasons. Reintervention was performed for four patients, including two patients of type IA entry flow and two patients of type IB entry flow. No occlusion of the supra-aortic trunk was observed. The estimated survival and reintervention (± SE) at 4 years was 88.7 ± 1.4% (95% CI, 87.3–90.1%) and 84.8 ± 1.5% (95% CI, 83.3–86.3%), respectively.ConclusionThoracic endovascular aortic repair with f-SMSG is an alternative treatment option for TBAD involving the aortic arch in high-volume centers.

The flow in the turbine endwall region consists of the complicated secondary flow structures driven by the lateral pressure gradient, which heavily affects the performance of film cooling. In this work, the film-cooling hole design optimization is performed considering the existence of the lateral pressure gradient in the real flow environment. Results have shown that the optimal film-cooling hole design is heavily influenced by the lateral pressure gradient in the endwall region, especially the compound angle design is clearly different from the flat plate flow environment. The optimization results are further validated with experiments using the pressure-sensitive paint (PSP) technique, and the film cooling performance is shown to be improved by 42.9%. This work demonstrates the importance of considering the real flow environment in the film-cooling hole design and also can provide guidance to the film-cooling hole design in the endwall region.

BackgroundUtilizing dual-energy x-ray absorptiometry (DXA) to assess bone mineral density (BMD) was not routine in many clinical scenarios, leading to missed diagnoses of osteoporosis. The objective of this study is to obtain effective parameters from hip computer tomography (CT) to screen patients with osteoporosis and predict their clinical outcomes.MethodsA total of 375 patients with hip CT scans for intertrochanteric fracture were included. Among them, 56 patients possessed the data of both hip CT scans and DXA and were settled as a training group. The cortical bone thickness (CTh) and Hounsfield unit (HU) values were abstracted from 31 regions of interest (ROIs) of the proximal femur. In the training group, the correlations between these parameters and BMD were investigated, and their diagnostic efficiency of osteoporosis was assessed. Finally, 375 patients were divided into osteoporotic and nonosteoporotic groups based on the optimal cut-off values, and the clinical difference between subgroups was evaluated.ResultsThe CTh value of ROI 21 and the HU value of ROI 14 were moderately correlated with the hip BMD [r = 0.475 and 0.445 (p ConclusionThe CTh and HU values of specific cortex sites in the proximal femur were positively correlated with BMD of DXA at the hip. Thresholds for osteoporosis based on CTh and HU values could be utilized to screen osteoporosis and predict clinical outcomes.

BackgroundContinuity is crucial to the health care of the internal migrant population and urgently needs improvements in China. Chinese government is committed to promoting healthcare continuity by improving the people-centered integrated care (PCIC) model in medical alliances. However, little is known about the driving mechanisms for continuity.MethodsWe created the questionnaire for this study by processes of a literature research, telephone interviews, two rounds of Delphi consultation. Based on the combination of quota sampling and judgment sampling, we collected 765 valid questionnaires from developed region and developing region in Zhejiang Province. Structural equation models were used to examined whether the attributes of PCIC (namely coordination, comprehensiveness, and accessibility of health care) associated with continuity, and explored the moderated mediating role of respect.ResultsThe result of SEM indicated that coordination had direct effect on continuity, and also had mediating effect on continuity via comprehensiveness and accessibility. The hierarchical linear regression analysis showed that the interactive items of coordination and respect had a positive effect on the comprehensiveness (β = 0.132), indicating that respect has positive moderating effect on the relationship between coordination and comprehensiveness. The simple slope test indicated that in the developed region, coordination had a significant effect on comprehensiveness for both high respect group(β = 0.678) and low respect group (β = 0.508). The moderated mediation index was statistically significant in developed areas(β = 0.091), indicating that respect had moderated mediating effect on the relationship between coordination and continuity via comprehensiveness of healthcare in the developed region; however, the moderated mediation effect was not significant in the developing region.ConclusionSuch regional differences of the continuity promoting mechanism deserve the attention of policy-makers. Governments and health authorities should encourage continuity of healthcare for migrants through improving the elements of PCIC—coordination, comprehensiveness and accessibility of healthcare, shaping medical professionalism of indiscriminate respect, and empowering migrants to have more autonomy over selection of services and decisions about their health.

The continuous development of Question Answering (QA) datasets has drawn the research community's attention toward multi-domain models. A popular approach is to use multi-dataset models, which are models trained on multiple datasets to learn their regularities and prevent overfitting to a single dataset. However, with the proliferation of QA models in online repositories such as GitHub or Hugging Face, an alternative is becoming viable. Recent works have demonstrated that combining expert agents can yield large performance gains over multi-dataset models. To ease research in multi-agent models, we extend UKP-SQuARE, an online platform for QA research, to support three families of multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii) late-fusion of agents. We conduct experiments to evaluate their inference speed and discuss the performance vs. speed trade-off compared to multi-dataset models. UKP-SQuARE is open-source and publicly available at http://square.ukp-lab.de., Comment: ACL 2023 Demo Paper

Enhancing the expressive capacity of deep learning-based time series models with self-supervised pre-training has become ever-increasingly prevalent in time series classification. Even though numerous efforts have been devoted to developing self-supervised models for time series data, we argue that the current methods are not sufficient to learn optimal time series representations due to solely unidirectional encoding over sparse point-wise input units. In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks. The distinct characteristics of the TimeMAE lie in processing each time series into a sequence of non-overlapping sub-series via window-slicing partitioning, followed by random masking strategies over the semantic units of localized sub-series. Such a simple yet effective setting can help us achieve the goal of killing three birds with one stone, i.e., (1) learning enriched contextual representations of time series with a bidirectional encoding scheme; (2) increasing the information density of basic semantic units; (3) efficiently encoding representations of time series using transformer networks. Nevertheless, it is a non-trivial to perform reconstructing task over such a novel formulated modeling paradigm. To solve the discrepancy issue incurred by newly injected masked embeddings, we design a decoupled autoencoder architecture, which learns the representations of visible (unmasked) positions and masked ones with two different encoder modules, respectively. Furthermore, we construct two types of informative targets to accomplish the corresponding pretext tasks. One is to create a tokenizer module that assigns a codeword to each masked region, allowing the masked codeword classification (MCC) task to be completed effectively..., Comment: Submitted to IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING(TKDE), under review

We present ShaDDR, an example-based deep generative neural network which produces a high-resolution textured 3D shape through geometry detailization and conditional texture generation applied to an input coarse voxel shape. Trained on a small set of detailed and textured exemplar shapes, our method learns to detailize the geometry via multi-resolution voxel upsampling and generate textures on voxel surfaces via differentiable rendering against exemplar texture images from a few views. The generation is real-time, taking less than 1 second to produce a 3D model with voxel resolutions up to 512^3. The generated shape preserves the overall structure of the input coarse voxel model, while the style of the generated geometric details and textures can be manipulated through learned latent codes. In the experiments, we show that our method can generate higher-resolution shapes with plausible and improved geometric details and clean textures compared to prior works. Furthermore, we showcase the ability of our method to learn geometric details and textures from shapes reconstructed from real-world photos. In addition, we have developed an interactive modeling application to demonstrate the generalizability of our method to various user inputs and the controllability it offers, allowing users to interactively sculpt a coarse voxel shape to define the overall structure of the detailized 3D shape.

We present a mask-piloted Transformer which improves masked-attention in Mask2Former for image segmentation. The improvement is based on our observation that Mask2Former suffers from inconsistent mask predictions between consecutive decoder layers, which leads to inconsistent optimization goals and low utilization of decoder queries. To address this problem, we propose a mask-piloted training approach, which additionally feeds noised ground-truth masks in masked-attention and trains the model to reconstruct the original ones. Compared with the predicted masks used in mask-attention, the ground-truth masks serve as a pilot and effectively alleviate the negative impact of inaccurate mask predictions in Mask2Former. Based on this technique, our \M achieves a remarkable performance improvement on all three image segmentation tasks (instance, panoptic, and semantic), yielding $+2.3$AP and $+1.6$mIoU on the Cityscapes instance and semantic segmentation tasks with a ResNet-50 backbone. Our method also significantly speeds up the training, outperforming Mask2Former with half of the number of training epochs on ADE20K with both a ResNet-50 and a Swin-L backbones. Moreover, our method only introduces little computation during training and no extra computation during inference. Our code will be released at \url{https://github.com/IDEA-Research/MP-Former}., Comment: CVPR 2023

Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), digital twin, Metaverse and other related digital technologies have attracted much attention in recent years. These new emerging technologies are changing the world significantly. This research introduces a fusion model, i.e. Fusion Universe (FU), where the virtual, physical, and cognitive worlds are merged together. Therefore, it is crucial to establish a set of principles for the fusion model that is compatible with our physical universe laws and principles. This paper investigates several aspects that could affect immersive and interactive experience; and proposes the fundamental principles for Fusion Universe that can integrate physical and virtual world seamlessly., Comment: 5 pages, 8 figures

As a consequence of Wan's theorem about Dwork's conjecture, the unit root $L$-functions of the $n$-dimensional Kloosterman family are $p$-adic meromorphic. By studying the symmetric power $L$-functions associated to the Kloosterman family, we prove that for each $0\le j\le n$, the unit root $L$-function coming from slope $j$ has a unique unit root which equals 1., Comment: 14 pages

We present a neural network approach to transfer the motion from a single image of an articulated object to a rest-state (i.e., unarticulated) 3D model. Our network learns to predict the object's pose, part segmentation, and corresponding motion parameters to reproduce the articulation shown in the input image. The network is composed of three distinct branches that take a shared joint image-shape embedding and is trained end-to-end. Unlike previous methods, our approach is independent of the topology of the object and can work with objects from arbitrary categories. Our method, trained with only synthetic data, can be used to automatically animate a mesh, infer motion from real images, and transfer articulation to functionally similar but geometrically distinct 3D models at test time., Comment: 8 pages

Mobile Edge Computing (MEC) holds excellent potential in Congestion Management (CM) of 6G vehicular networks. A reasonable schedule of MEC ensures a more reliable and efficient CM system. Unfortunately, existing parallel and sequential models cannot cope with scarce computing resources and constrained channels, especially during traffic rush hour. In this paper, we propose a channel-constrained multi-core sequential model (CCMSM) for task offloading and resource allocation. The CCMSM incorporates a utility index that couples system energy consumption and delay, applying Genetic Algorithm combining Sparrow Search Algorithm (GA-SSA) in the branching optimization. Furthermore, we prove that the system delay is the shortest with the FCFS computing strategy in the MEC server. Simulation demonstrates that the proposed CCMSM achieves a higher optimization level and exhibits better robustness and resilient scalability for traffic spikes.

The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and other perception tasks. However, the current field lacks a unified and comprehensive benchmark specifically tailored for DETR-based models. To address this issue, we develop a unified, highly modular, and lightweight codebase called detrex, which supports a majority of the mainstream DETR-based instance recognition algorithms, covering various fundamental tasks, including object detection, segmentation, and pose estimation. We conduct extensive experiments under detrex and perform a comprehensive benchmark for DETR-based models. Moreover, we enhance the performance of detection transformers through the refinement of training hyper-parameters, providing strong baselines for supported algorithms.We hope that detrex could offer research communities a standardized and unified platform to evaluate and compare different DETR-based models while fostering a deeper understanding and driving advancements in DETR-based instance recognition. Our code is available at https://github.com/IDEA-Research/detrex. The project is currently being actively developed. We encourage the community to use detrex codebase for further development and contributions., Comment: project link: https://github.com/IDEA-Research/detrex

In this paper, we present an open-set object detector, called Grounding DINO, by marrying Transformer-based detector DINO with grounded pre-training, which can detect arbitrary objects with human inputs such as category names or referring expressions. The key solution of open-set object detection is introducing language to a closed-set detector for open-set concept generalization. To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion. While previous works mainly evaluate open-set object detection on novel categories, we propose to also perform evaluations on referring expression comprehension for objects specified with attributes. Grounding DINO performs remarkably well on all three settings, including benchmarks on COCO, LVIS, ODinW, and RefCOCO/+/g. Grounding DINO achieves a $52.5$ AP on the COCO detection zero-shot transfer benchmark, i.e., without any training data from COCO. It sets a new record on the ODinW zero-shot benchmark with a mean $26.1$ AP. Code will be available at \url{https://github.com/IDEA-Research/GroundingDINO}., Comment: Code will be available at https://github.com/IDEA-Research/GroundingDINO

The receptor-interacting serine/threonine protein kinase 1 (RIPK1) is tightly related to Digestive System Neoplasms genesis. Therefore, inhibitors that target RIPK1 have gained popularity in today's anti-cancer therapy. Molecular docking, molecular dynamics simulations, and molecular mechanics/Poisson-Boltzmann surface area calculations were used to investigate the binding mode and inhibition mechanism of five 4,5-dihydropyrazole derivatives with RIPK1 in the present work. The results showed that the five inhibitors mainly interacted with RIPK1 through van der Waals interaction and were stably present in the hydrophobic pocket next to the ATP-binding pocket. During the simulation, the inhibitor 2R with opposite chirality displayed a significant flip-flop in the binding pose, causing the key residue Asp156 to not easily maintain the inactive conformation, affecting its inhibitory ability. Inhibitors with identical chirality but different 1-substituents could exert non-competitive inhibition via three pathways. First of all, the inhibitor interacted directly with ATP, influencing its γ-phosphate position. Second, it altered the conformation of Lys45, which was crucial for both ATP binding and γ-phosphate transfer. And then it affected the conformation of the P-loop and β1 sheet, resulting in differences in substrate peptide recognition. The results can provide a theoretical basis for the design of such inhibitors in the future.

Astrophysical jets are collimated streams of magnetized plasma launched from compact objects, such as neutron stars or black holes. These jets, powered by the accretion of surrounding gas onto the compact object, can accelerate particles to extreme energies and produce powerful radiation. In this report, I investigate energy dissipation and particle acceleration in two key regions in jets: (i) external shocks which form where jets interact with ambient gas and (ii) internally in the jet where particles are likely to be energized through the process of magnetic reconnection. First, I explore inverse Compton scatterings of electrons accelerated at the external shock as a candidate for the high energy emissions from gamma-ray burst afterglows. I consider two sources of seed photons for scattering: synchrotron photons from the blast wave (synchrotron self-Compton) and photon fields external to the shock (external Compton) from the star-forming region in the host galaxy. I develop an analytical model to predict the high-energy spectra from these blasts and reproduce the observed spectra and lightcurves of GRB~190114C. The model implies that inverse Compton can dominate the sub-TeV/TeV emission in this event. Second, I study the particle acceleration mechanism of magnetic reconnection internally in astrophysical jets. I employ particle-in-cell (PIC) simulations of 3D relativistic magnetic reconnection. My analysis reveals a novel acceleration mechanism that only operates in 3D that results in faster particle acceleration. Unlike in 2D simulations where particles are trapped in the reconnected plasma and stop being accelerated, a fraction of particles in 3D can escape from this region (along the third direction) and be further accelerated. The escaped particles are characterized by a harder energy spectrum with a higher cutoff compared to those found in previous studies. Based on the PIC simulation findings, I build an analytical model for the particle kinetics, which divides particles into two groups --- one undergoing fast energization in the reconnection upstream region and the other residing in the reconnected plasma without energy change. The model predicts a power-law spectra for both groups of particles. PIC simulations reveal a universal magnetization-independent spectra with $dN/d\gamma\propto \gamma^{-2}$ for the overall particle population. The results demonstrate that relativistic reconnection in jets may be a promising mechanism for generating Ultra-High-energy Cosmic Rays.

The unsteady hydrodynamic drag exerted on an oscillating sphere near a planar wall is addressed experimentally, theoretically, and numerically. The experiments are performed by using colloidal-probe Atomic Force Microscopy (AFM) in thermal noise mode. The natural resonance frequencies and quality factors are extracted from the measurement of the power spectrum density of the probe oscillation for a broad range of gap distances and Womersley numbers. The shift in the natural resonance frequency of the colloidal probe as the probe goes close to a solid wall infers the wall-induced variations of the effective mass of the probe. Interestingly, a crossover from a positive to a negative shift is observed as the Womersley number increases. In order to rationalize the results, the confined unsteady Stokes equation is solved numerically using a finite-element method, as well as asymptotic calculations.The in-phase and out-of-phase terms of the hydrodynamic drag acting on the sphere are obtained and agree well to the experimental results. All together, the experimental, theoretical, and numerical results show that the hydrodynamic force felt by an immersed sphere oscillating near a wall is highly dependent on the Womersley number.

Deep learning has been recently introduced for efficient acoustic howling suppression (AHS). However, the recurrent nature of howling creates a mismatch between offline training and streaming inference, limiting the quality of enhanced speech. To address this limitation, we propose a hybrid method that combines a Kalman filter with a self-attentive recurrent neural network (SARNN) to leverage their respective advantages for robust AHS. During offline training, a pre-processed signal obtained from the Kalman filter and an ideal microphone signal generated via teacher-forced training strategy are used to train the deep neural network (DNN). During streaming inference, the DNN's parameters are fixed while its output serves as a reference signal for updating the Kalman filter. Evaluation in both offline and streaming inference scenarios using simulated and real-recorded data shows that the proposed method efficiently suppresses howling and consistently outperforms baselines., Comment: submitted to INTERSPEECH 2023

In this article, we study the distribution of large quadratic character sums. Based on the recent work of Lamzouri~\cite{La2022}, we obtain the structure results of quadratic characters with large character sums., Comment: 15 pages

Within the framework of relative and absolute quantum field theories (QFTs), we present a general formalism for understanding polarizations of the intermediate defect group and constructing non-invertible duality defects in theories in $2k$ spacetime dimensions with self-dual gauge fields. We introduce the polarization pair, which fully specifies absolute QFTs as far as their $(k-1)$-form defect groups are concerned, including their $(k-1)$-form symmetries, global structures (including discrete $\theta$-angle), and local counterterms. Using the associated symmetry TFT, we show that the polarization pair is capable of succinctly describing topological manipulations, e.g., gauging $(k-1)$-form global symmetries and stacking counterterms, of absolute QFTs. Furthermore, automorphisms of the $(k-1)$-form charge lattice naturally act on polarization pairs via their action on the defect group; they can be viewed as dualities between absolute QFTs descending from the same relative QFT. Using this formalism, we present a prescription for building non-invertible symmetries of absolute QFTs. A large class of known examples, e.g., non-invertible defects in 4D $\mathcal{N}=4$ super-Yang--Mills, can be reformulated via this prescription. As another class of examples, we identify and investigate in detail a family of non-invertible duality defects in 6D superconformal field theories (SCFTs), including from the perspective of the symmetry TFT derived from Type IIB string theory., Comment: 48 pages + appendices, 7 figures, 2 tables