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Heart failure with preserved ejection fraction (HFpEF) is commonly found in persons living with HIV (PLWH) even when antiretroviral therapy suppresses HIV viremia. However, studying this condition has been challenging because an appropriate animal model is not available. In this article, we studied calcium transient in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in culture to simulate the cardiomyocyte relaxation defect noted in PLWH and HFpEF and assess whether various drugs have an effect. We show that treatment of hiPSC-CMs with inflammatory cytokines (such as interferon-γ or TNF-α) impairs their Ca2+ uptake into sarcoplasmic reticulum and that SGLT2 inhibitors, clinically proven as effective for HFpEF, reverse this effect. Additionally, treatment with mitochondrial antioxidants (like mito-Tempo) and certain antiretrovirals resulted in the reversal of the effects of these cytokines on calcium transient. Finally, incubation of hiPSC-CMs with serum from HIV patients with and without diastolic dysfunction did not alter their Ca2+-decay time, indicating that the exposure to the serum of these patients is not sufficient to induce the decrease in Ca2+ uptake in vitro. Together, our results indicate that hiPSC-CMs can be used as a model to study molecular mechanisms of inflammation-mediated abnormal cardiomyocyte relaxation and screen for potential new interventions.

Accurate fruit detection is crucial for automated fruit picking. However, real-world scenarios, influenced by complex environmental factors such as illumination variations, occlusion, and overlap, pose significant challenges to accurate fruit detection. These challenges subsequently impact the commercialization of fruit harvesting robots. A tomato detection model named YOLO-SwinTF, based on YOLOv7, is proposed to address these challenges. Integrating Swin Transformer (ST) blocks into the backbone network enables the model to capture global information by modeling long-range visual dependencies. Trident Pyramid Networks (TPN) are introduced to overcome the limitations of PANet’s focus on communication-based processing. TPN incorporates multiple self-processing (SP) modules within existing top-down and bottom-up architectures, allowing feature maps to generate new findings for communication. In addition, Focaler-IoU is introduced to reconstruct the original intersection-over-union (IoU) loss to allow the loss function to adjust its focus based on the distribution of difficult and easy samples. The proposed model is evaluated on a tomato dataset, and the experimental results demonstrated that the proposed model’s detection recall, precision, F1 score, and AP reach 96.27%, 96.17%, 96.22%, and 98.67%, respectively. These represent improvements of 1.64%, 0.92%, 1.28%, and 0.88% compared to the original YOLOv7 model. When compared to other state-of-the-art detection methods, this approach achieves superior performance in terms of accuracy while maintaining comparable detection speed. In addition, the proposed model exhibits strong robustness under various lighting and occlusion conditions, demonstrating its significant potential in tomato detection.

The stock market is playing an increasingly important role in the global economy. Accurate stock price forecasting not only aids the government in predicting economic trends but also helps investors anticipate higher expected returns. Nevertheless, hurdles such as nonlinearity, complexity and high volatility make it a daunting task to predict stock prices. To address this issue, this study proposes a new hybrid model, termed Hierarchical Decomposition-based Forecasting Model (HDFM), to decompose and forecast stock prices in a hierarchical fashion. The model utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for the initial decomposition of stock price time series. To enhance the predictive efficiency, sub-series with similar sample entropy from the decomposition were combined using the K-means clustering method. Through a thorough analysis, it was found that the first combined sub-series contained more high-frequency signals. Therefore, the first combined sub-series is subjected to a second decomposition with variational mode decomposition (VMD). Subsequently, the gated recurrent unit (GRU) model was used to individually predict each sub-series. The final results were obtained by merging the prediction outcomes. The proposed model was evaluated on three different stock markets. The experimental results show that the proposed model outperforms other forecasting methods across all stock indices. Moreover, ablation studies demonstrated the effectiveness of each component within the proposed model.

With the efficiency of storage and retrieval speed, the hashing methods have attracted a lot of attention for cross-modal retrieval applications. In contrast to traditional cross-modal hashing by using handcrafted features, deep cross-modal hashing integrates the advantages of deep learning and hashing methods to encode raw multimodal data into compact binary codes with semantic information preserved. Generally speaking, most of the existing deep cross-modal hashing methods simply define the semantic similarity between heterogeneous modalities by counting the number of shared semantic labels (such as, two samples share at least one label, they are similar, otherwise they are dissimilar), which fails to represent the accurate multi-label semantic relations between heterogeneous data. In this paper, we propose a new Deep Self-supervised Hashing with Fine-grained Similarity Mining (DSH-FSM) framework to efficiently preserve the fine-grained multi-label semantic similarity, learning a highly separable embedding space. Specifically, by employing an asymmetric guidance strategy, a novel Semantic-Network is introduced into cross-modal hashing to learn two semantic dictionaries, including the semantic feature dictionary and the semantic code dictionary, which guides the Image-Network and the Text-Network to capture multi-label semantic relevance across different modalities. Based on the obtained semantic dictionary, an asymmetric margin-scalable loss is proposed to obtain fine-grained pair-wise similarity information, which could contribute to the production of similarity-preserving and discriminative binary codes. Besides, two feature extractors with transformer encoders are designed to achieve the Image-Network and Text-Network, which could extract the representative semantic characteristics from raw heterogeneous samples. Extensive experimental results on various benchmark datasets show that our proposed DSH-FSM framework achieves state-of-the-art cross-modal similarity search performance. Compared to the state-of-the-art methods, the results of mAP are significantly improved by 1.9%, 9.1%, and 9.8%, respectively, on the three widely used datasets.

Soil desertification is an important challenge in global soil management, and effectively and stably restoring soil function is an urgent problem. Using synthetic microbial communities (SynComs) is a burgeoning microbial strategy aimed at enhancing soil nutrients through functional synergies among diverse microorganisms; nevertheless, their effectiveness in restoring desertified soils remains unknown. In this study, we conducted a two-year field experiment using a SynCom constructed by in situ probiotic bacteria and set up control, chemical fertilizer, and combined SynCom–chemical fertilizer (combined fertilizer) treatments to investigate the linkage between microbial communities and soil multifunctionality in the soil surface layer (0–10 cm). Both the bacterial and fungal communities differed the most under the combined fertilizer treatment compared to the control. The bacterial communities differed more under treatments of the SynCom than the chemical fertilizer, while the fungal communities differed more under the chemical fertilizer treatment than the SynCom treatment. Regarding soil function, the SynCom strengthened the correlation between enzyme activities and both bacterial communities and functional properties. pH and available potassium were the main influencing factors under the chemical fertilizer and combined fertilizer treatments. The beta-diversity of the bacterial communities was significantly correlated with soil multifunctionality. Random forest analyses showed that the SynCom significantly enhanced the bacterial communities, driving soil multifunctionality, and that some potential microbial taxa drove multiple nutrient cycles simultaneously. In summary, the SynCom effectively increased the abundance of most carbon, nitrogen, and phosphorus functional genes as well as soil enzyme activities. The bacterial community composition contributed significantly to soil multifunctionality. Hence, the development of novel microbial agents holds significant potential for improving soil functionality and managing desertification.

Sirtuins (SIRT) exhibit deacetylation or ADP-ribosyltransferase activity and regulate a wide range of cellular processes in the nucleus, mitochondria, and cytoplasm. The role of the only sirtuin that resides in the cytoplasm, SIRT2, in the development of ischemic injury and cardiac hypertrophy is not known. In this paper, we show that the hearts of mice with deletion of Sirt2 (Sirt2-/-) display improved cardiac function after ischemia-reperfusion (I/R) and pressure overload (PO), suggesting that SIRT2 exerts maladaptive effects in the heart in response to stress. Similar results were obtained in mice with cardiomyocyte-specific Sirt2 deletion. Mechanistic studies suggest that SIRT2 modulates cellular levels and activity of nuclear factor (erythroid-derived 2)-like 2 (NRF2), which results in reduced expression of antioxidant proteins. Deletion of Nrf2 in the hearts of Sirt2-/- mice reversed protection after PO. Finally, treatment of mouse hearts with a specific SIRT2 inhibitor reduced cardiac size and attenuates cardiac hypertrophy in response to PO. These data indicate that SIRT2 has detrimental effects in the heart and plays a role in cardiac response to injury and the progression of cardiac hypertrophy, which makes this protein a unique member of the SIRT family. Additionally, our studies provide a novel approach for treatment of cardiac hypertrophy and injury by targeting SIRT2 pharmacologically, providing a novel avenue for the treatment of these disorders.

Background and objectivePost-operative pneumonia (POP), a common complication, may be associated with prolonged hospitalization and long-term mortality in aneurysmal subarachnoid hemorrhage (aSAH) patients. This study aimed to explore the association between pre-operative prognostic nutrition index (PNI) and POP in aSAH patients.MethodsA total of 280 aSAH patients were enrolled from Nanjing Drum Tower Hospital. PNI was calculated as follows: [10 × albumin(gr/dl)] + [0.005 × absolute pre-operative lymphocyte count (per mm3)]. We utilized multivariate analyses, restricted cubic spline, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) to elucidate the role of PNI in POP.ResultsPre-operative PNI levels in the POP group were higher, compared with the non-POP group (41.0 [39.0, 45.4] vs. 44.4 [40.5, 47.3], P = 0.001). When we included PNI as a categorical variable in the multivariate analysis, the levels of PNI were associated with POP (odds ratio, 0.433; 95% confidence interval, 0.253–0.743; P=0.002). In addition, when we included PNI as a continuous variable in the multivariate analysis, the PNI levels were also associated with POP (odds ratio, 0.942; 95% confidence interval, 0.892–0.994; P = 0.028). The level of albumin was also a predictor of the occurrence of POP, with a lower diagnostic power than PNI [AUC: 0.611 (95% confidence interval, 0.549–0.682; P = 0.001) for PNI vs. 0.584 (95% confidence interval, 0.517–0.650; P = 0.017) for albumin]. Multivariable-adjusted spline regression indicated a linear dose–response association between PNI and POP in aSAH participants (P for linearity = 0.027; P for non-linearity = 0.130). Reclassification assessed by IDI and NRI was significantly improved with the addition of PNI to the conventional model of POP in aSAH patients (NRI: 0.322 [0.089–0.555], P = 0.007; IDI: 0.016 [0.001–0.031], P = 0.040).ConclusionThe lower levels of pre-operative PNI may be associated with the higher incidence of POP in aSAH patients. Neurosurgeons are supposed to pay more attention to pre-operative nutrition status in aSAH patients.

Abstract Owing to the significant roles in balancing carrier transport, bandgap alignment, and perovskite crystallization, realizing high‐quality electron transport layers (ETLs) and their effective electronic contact with the perovskite photoactive layer is crucial for future commercialization of perovskite solar cells (PSCs). Here, a high‐quality SnO2 ETL is firstly deposited at room temperature via the low‐cost electron beam evaporation (E‐beam) technology to help achieve an impressive PCE of 18.88% for rigid PSCs and a PCE of 15.73% for flexible devices. Then we introduced a novel passivating ligand molecule, 11 Maleimidoundecanoic acids (11MA), to help induce functional defects passivation at the interface of SnO2/Perovskite to coordinate with Pb2+ in perovskite and Sn4+ in SnO2 via the functional groups (‐C = O, ‐COOH). As a result, 11MA could help achieve the best PCE of 20.94% with negligible hysteresis for rigid devices. Also, doping 11MA in the perovskite photoactive layer further enhanced the PCE to 22.08% with much improved 2000 hours ambient stabilities. Finally, we fabricated different large‐area rigid devices (10, 40, 100, and 600 mm2) toward speeding up the commercialization of PSCs by industrial E‐beam technology and rational defects passivation strategies.

The large-segment hoisting construction technology for bridges is increasingly widely used due to its flexibility and efficiency, although it also poses challenges to construction monitoring. Traditional monitoring technology is unitary with low data processing efficiency, making it difficult to meet the accuracy requirements of large-segment hoisting. The application of digital technology has brought about an opportunity for innovation in bridge construction monitoring technology. To address existing challenges and explore digital applications, this paper takes the integral hoisting construction control of the large-segment steel box girder in a large cross-sea bridge as an example, developing an alignment, stress, and temperature monitoring scheme by taking the key points of hoisting construction control into consideration. A monitoring platform was developed, and the workflow of large-segment hoisting construction monitoring is systematically summarized from the viewpoint of practical engineering, which provides a valuable reference for achieving precise and efficient construction monitoring and control in similar projects.

With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions.

Several studies have indicated that the oral and gut microbiota may exhibit differences in patients with cirrhosis. Less is known about the microbiota in the stomach, which is located between the oral cavity and the intestinal tract. In this study, the gastric mucosal microbiota of patients with liver cirrhosis and controls were analyzed with 16S ribosomal RNA (rRNA) pyrosequencing. Cirrhotic patients had significantly lower Helicobacter pylori (H. pylori) infection rates, as confirmed by both the histological method and the pyrosequencing method. In H. pylori-negative subjects, gastric bacterial communities of healthy and cirrhosis cohorts were clustered into four clusters based on bacterial compositions: Cluster_1 and Cluster_2 (mostly cirrhosis), Cluster_3 (mostly healthy), and Cluster_4 (around half of each). Compositional and functional differences were observed among these different clusters. At the genus level, Cluster_1 and Cluster_2 showed enrichment of Neisseria and Streptococcus, respectively. Functionally, Cluster_2 was characterized as depleted of genetic information processing, as well as of modules related to glycan biosynthesis and metabolism. Patients in Cluster_2 had more severe gastrointestinal symptoms and a higher rate of previous endoscopic variceal ligation (EVL) therapy than patients in other clusters. Our findings suggest that the colonization of both H. pylori and non-H. pylori is influenced in liver cirrhosis. Although the H. pylori-negative gastric mucosal microbiota showed considerable heterogeneity, associations between specific gastric microbiota and clinical characteristics could be observed. Previous EVL therapy might lead to a distinct structure of the gastric mucosal microbiota, thus aggravating the gastrointestinal symptoms in H. pylori-negative cirrhotic patients.

Pregnancy is associated with substantial physiological changes of the heart, and disruptions in these processes can lead to peripartum cardiomyopathy (PPCM). The molecular processes that cause physiological and pathological changes in the heart during pregnancy are not well characterized. Here, we show that mTORc1 was activated in pregnancy to facilitate cardiac enlargement that was reversed after delivery in mice. mTORc1 activation in pregnancy was negatively regulated by the mRNA-destabilizing protein ZFP36L2 through its degradation of Mdm2 mRNA and P53 stabilization, leading to increased SESN2 and REDD1 expression. This pathway impeded uncontrolled cardiomyocyte hypertrophy during pregnancy, and mice with cardiac-specific Zfp36l2 deletion developed rapid cardiac dysfunction after delivery, while prenatal treatment of these mice with rapamycin improved postpartum cardiac function. Collectively, these data provide what we believe to be a novel pathway for the regulation of mTORc1 through mRNA stabilization of a P53 ubiquitin ligase. This pathway was critical for normal cardiac growth during pregnancy, and its reduction led to PPCM-like adverse remodeling in mice.

Mobile colistin resistance (mcr) genes represent an emerging threat to public health. Reports on the prevalence, antimicrobial profiles, and clonality of MCR-9-producing Enterobacter cloacae complex (ECC) isolates on a national scale in China are limited. We screened 3,373 samples from humans, animals, and the environment and identified eleven MCR-9-positive ECC isolates. We further investigated their susceptibility, epidemiology, plasmid profiles, genetic features, and virulence potential. Ten strains were isolated from severe bloodstream infection cases, especially three of them were recovered from neonatal sepsis. Enterobacter hormaechei was the most predominant species among the MCR-9-producing ECC population. Moreover, the co-existence of MCR-9, CTX-M, and SHV-12 encoding genes in MCR-9-positive isolates was globally observed. Notably, mcr-9 was mainly carried by IncHI2 plasmids, and we found a novel ~187 kb IncFII plasmid harboring mcr-9, with low similarity with known plasmids. In summary, our study presented genomic insights into genetic characteristics of MCR-9-producing ECC isolates retrieved from human, animal, and environment samples with one health perspective. This study is the first to reveal NDM-1- and MCR-9-co-producing ECC from neonatal sepsis in China. Our data highlights the risk for the hidden spread of the mcr-9 colistin resistance gene.

ABSTRACT: In this study, we report the effect of the number of formulated repeat units (n), the type of diamine and the cross-linker content on pore morphology, structure and mechanical properties of vacuum-dried polyimide aerogels. Morphology observations demonstrate that an excess of cross-linker is conducive to increase the pore size, strengthen and stiffen the network to avoid the collapse and suppress the irreversible shrinkage during the vacuum drying procedure. For 2,2'-dimethylbenzidine derived aerogels, the pore diameter increases with increasing cross-linker content or decreasing n. The shrinkage is higher in aerogels derived from 4,4'-oxidianiline due to the chain rigidity and mobility difference compared to rigid diamine-based aerogels, resulting in lower porosity and higher density. Compared with those previously reported aerogels with the same backbone chemistries, vacuum dried polyimide aerogels herein show higher compressive strength and Young’s modulus resulting from higher density and coarser skeleton networks. More importantly, isothermal aging results reveal very low increasement of shrinkage and density occurring over the aging period, which even can be ignored. The facile, low-cost vacuum drying procedure, dramatically improved mechanical properties, and superior thermal resistance will broaden the practical applications of polyimide aerogels.

Abstract Background Immune inhibitory receptors play an important role in chronic infections. However, little is known about their role in hepatitis B virus (HBV) infection. Here, we analyzed the relationship between programmed death-1 (PD-1) and lymphocyte activation gene-3 (LAG-3) expression on CD4+ T cells and HBV disease progression. Results PD-1 and LAG-3 expression was significantly higher on CD4+ T cells from HBV patients than on those from the HCs. In addition, a significant positive correlation was found between the PD-1 and LAG-3 expression levels and the ALT(alanine aminotransferase) level. CD4+ T cell function was inhibited by high PD-1 and LAG-3 levels, and CD4+ T cells with high PD-1 and LAG-3 expression lost the ability to secrete IFN-γ, IL-2 and TNF-α. Furthermore, blockade of the PD-1 and LAG-3 pathways reversed the damage to CD4+ T cell proliferation and cytokine secretion. Conclusions CD4+ T cell exhaustion during chronic HBV had high PD-1 and LAG-3 expression and the absence of helper T cell cytokines, including IFN-γ, IL-2 and TNF-α. After blocking PD-L1 and LAG-3, CD4+ T cell function in chronic hepatitis B patients was partially restored.

Background: Data on inter-tumoral heterogeneity and clonal evolution of pancreatic neuroendocrine neoplasms (panNENs) with liver metastasis are limited. The aim of this study was to explore different patterns of clonal evolution of pancreatic neuroendocrine neoplasms with liver metastasis and the possible distinctive signaling pathways involved between G2 neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs).Methods: Tumor tissues of five patients (10 samples) with pancreatic neuroendocrine neoplasms with synchronous liver metastasis were analyzed using next-generation sequencing. PyClone, Gene Ontology, and Reactome pathway enrichment analysis were also applied.Results: Mutated genes varied in individuals, reflecting the inter-tumoral heterogeneity of panNENs. The distribution of subclones varied during tumor metastasis, and different clonal evolution patterns were revealed between NETs and NECs. Gene Ontology and Reactome analyses revealed that in both NETs and NECs, signaling pathways and biological processes shared similarities and differences in the primary and metastatic lesions. In addition, the signaling pathway features were different between NETs and NECs. In the primary lesions, epigenetic changes and post-transcriptional modifications participated in NETs, while FGFR signaling, EGFR signaling, and NTRK2 signaling were largely involved in NECs. Although DNA repair and TP53 regulation were both involved in the metastatic lesions, most of the signaling pathways and biological processes disrupted by the mutated genes were different.Conclusions: Our study revealed spatial inter-tumoral heterogeneity and temporal clonal evolution in PanNENs, providing potential therapeutic targets for further prospective clinical trials.

ABSTRACT The spread of colistin resistance gene mcr-1 at the animal-human interface remains largely unknown. This work aimed to investigate the molecular characteristics of two extended-spectrum-β-lactamase (ESBL)-producing Escherichia coli strains with mcr-1, i.e., strains H8 and H9, isolated from the same mink farmer. In this study, five mcr-positive E. coli strains were isolated from the mink farm. Pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST) identified two genetically unrelated MCR-1 producers (H8 and H9) from the same farmer and two clonally related MCR-1-positive isolates (M5 and M6) from two different mink samples. Additionally, a mcr-1 variant, designated mcr-1.12, was identified in isolate M4. MIC determination revealed that all of the MCR-producing strains exhibited multiresistant phenotypes but showed susceptibility to imipenem, meropenem, amikacin, and tigecycline. Replicon typing showed that mcr-1 was associated with IncHI2 plasmids in 4 cases, while the gene was located on an IncI2 plasmid in 1 case. PacBio sequencing and plasmid analysis confirmed that the mcr-1 gene was located on an ∼204-kb IncHI2 plasmid in H8 and was carried by an ∼61-kb IncI2 plasmid in H9. To our knowledge, this work represents the first report of the occurrence of MCR-producing isolates from mink. Moreover, our report also describes the coexistence of two different MCR-1 producers in the same farmer. It highlights that fur farms can be reservoirs of mcr-1 genes. The identification of mcr-carrying plasmids on a fur farm is of potential public health importance, as it suggests that mcr is widespread in the animal husbandry industry. IMPORTANCE Colistin resistance is a real threat for both human and animal health. The mobile colistin resistance gene mcr has contributed to the persistence and transmission of colistin resistance at the interfaces of animals, humans, and ecosystems. Although mcr genes have usually been recovered from food animals, patients, and healthy humans, transmission of mcr genes at the animal-human interface remains largely unknown. This was the first study to isolate and characterize MCR-producing isolates from mink, as well as to report the coexistence of two different MCR-1 producers in the same farmer. The characterization and analysis of two MCR-1-producing E. coli isolates may have important implications for comprehension of the transmission dynamics of these bacteria. We emphasize the importance of improved multisectorial surveillance of colistin-resistant E. coli in this region.

The Snf1-related kinase (SNRK) is widely expressed and yet its function is poorly understood. Here the authors show that SNRK regulates mitochondrial coupling via the Trib3-PPARα-UCP3 pathway and that cardiac overexpression of SNRK decreases metabolic substrate usage and oxygen consumption but maintains cardiac function and energy in mice.

In order to better predict the development of shrinkage deformation of steam-cured concrete mixed with slag, a deformation-temperature-humidity integrated model test, a hydration heat test, and an elastic modulus test were performed. The effects of the steam-curing process and the content of slag on shrinkage deformation, hydration degree and elastic modulus of concrete were studied. The results indicate that during the steam-curing process, the concrete has an “expansion-shrinkage” pattern. After the steam curing, the deformation of concrete is dominated by drying shrinkage. After the addition of slag, the shrinkage deformation of steam-cured concrete is increased. The autogenous shrinkage increases by 0.5–12%, and the total shrinkage increases by 1.5–8% at 60 days. At the same time, slag reduces the hydration degree of steam-cured concrete and modulus of elasticity. A prediction model for the hydration degree of steam-cured concrete is established, which can be used to calculate the degree of hydration at any curing age. Based on the capillary tension generated by the capillary pores in concrete, an integrated model of autogenous shrinkage and total shrinkage is established with the relative humidity directly related to the water loss in the concrete as the driving parameter. Whether the shrinkage deformation is caused by hydration reaction or the external environment, this model can better predict the shrinkage deformation of steam-cured concrete.

Abstract Excess cellular iron increases reactive oxygen species (ROS) production and causes cellular damage. Mitochondria are the major site of iron metabolism and ROS production; however, few studies have investigated the role of mitochondrial iron in the development of cardiac disorders, such as ischemic heart disease or cardiomyopathy (CM). We observe increased mitochondrial iron in mice after ischemia/reperfusion (I/R) and in human hearts with ischemic CM, and hypothesize that decreasing mitochondrial iron protects against I/R damage and the development of CM. Reducing mitochondrial iron genetically through cardiac‐specific overexpression of a mitochondrial iron export protein or pharmacologically using a mitochondria‐permeable iron chelator protects mice against I/R injury. Furthermore, decreasing mitochondrial iron protects the murine hearts in a model of spontaneous CM with mitochondrial iron accumulation. Reduced mitochondrial ROS that is independent of alterations in the electron transport chain's ROS producing capacity contributes to the protective effects. Overall, our findings suggest that mitochondrial iron contributes to cardiac ischemic damage, and may be a novel therapeutic target against ischemic heart disease.

Ascites bacterial burden is associated with poor clinical outcomes in patients with end-stage liver disease. However, the impact of ascitic microbial composition on clinical course was still not clear. In this study, the ascitic microbiota composition of 100 cirrhotic patients with culture-negative and non-neutrocytic ascites were researched with 16S rRNA pyrosequencing and enterotype-like cluster analysis.Results: By characterizing the ascitic microbial composition, two distinct microbial clusters were observed, Cluster 1 (86 patients) and Cluster 2 (14 patients). Cluster 1 showed lower microbial richness than Cluster 2. At the phylum level, Cluster 1 had greater abundance of Bacteroidetes and Firmicutes, but less abundance of Proteobacteria and Actinobacteria than Cluster 2. At the family level, family Bacteroidales S24-7 group, Prevotellaceae, Lachnospiraceae, Lactobacillaceae, Rikenellaceae, and Vibrionaceae were found over-represented in Cluster 1. And family Acetobacteraceae, Erysipelotrichaceae, Rickettsiaceae, and Streptococcaceae were found enriched in Cluster 2. The levels of plasma cytokine IL-17A, IL-7, and PDGF-BB were found significantly higher in Cluster 1 than in Cluster 2. There were four OTUs closely correlated with plasma cytokines, which were OTU 140 and OTU 271 (both from Bacteroidales S24-7 group), OTU 68 (Veillonellaceae), and OTU 53 (Helicobacteraceae). Patients from Cluster 1 showed significant higher short-term mortality than patients from Cluster 2.Conclusion: Our study demonstrated that the microbial composition of culture-negative and non-neutrocytic ascites in cirrhotic patients is associated with short-term clinical outcomes. The results here offer a rational for the identification of patients with high risk, and provide references for selective use of prophylactic methods.

In order to overcome the drawbacks of the ridge regression and label propagation algorithms, we propose a new semi-supervised classification method named semi-supervised ridge regression with adaptive graph-based label propagation (SSRR-AGLP). Firstly, we present a new adaptive graph-learning scheme and integrate it into the procedure of label propagation, in which the locality and sparsity of samples are considered simultaneously. Then, we introduce the ridge regression algorithm into label propagation to solve the “out of sample„ problem. As a consequence, the proposed SSSRR-AGLP integrates adaptive graph learning, label propagation and ridge regression into a unified framework. Finally, an effective iterative updating algorithm is designed for solving the algorithm, and the convergence analysis is also provided. Extensive experiments are conducted on five databases. Through comparing the results with some well-known algorithms, the effectiveness and superiority of the proposed algorithm are demonstrated.

Nature Communications 8: Article number: 14095 (2017); Published: 24 January 2017; Updated: 30 August 2017. The authors inadvertently omitted Eltyeb Abdelwahid, who contributed to the generation of animal models and their initial evaluation, from the author list. This has now been corrected in both the PDF and HTML versions of the Article.

BACKGROUND: Ras associated with diabetes (Rad) inhibits vascular lesion formation by reducing the attachment and migration of vascular smooth muscle cells (VSMCs). However, the transcriptional regulation of Rad in VSMCs is unclear. METHODOLOGY AND PRINCIPAL FINDINGS: We found that Platelet-Derived Growth Factor (PDGF)induced Rad expression in a time- and dose-dependent manner in rat aortic smooth muscle cells (RASMCs) using quantitative real-time PCR. By serial deletion analysis of the Rad promoter, we identified that two GC-rich early growth response-1 (Egr-1) binding sites are essential for PDGF-induced Rad promoter activation. Overexpression of Egr-1 in RASMCs strongly stimulated Rad expression while the Egr-1 corepressor, NGFI-A binding protein 2 (NAB2), repressed PDGF-induced Rad up-regulation in a dose-dependent manner. Direct binding of Egr-1 to the Rad promoter region was further confirmed by chromatin immunoprecipitation assays. CONCLUSIONS: Our results demonstrate that Rad is regulated by PDGF through the transcriptional factor Egr-1 in RASMCs.

In traditional Chinese medicine (TCM), artificial intelligence (AI)-assisted syndrome differentiation and disease diagnoses primarily confront the challenges of accurate symptom identification and classification. This study introduces a multi-label entity extraction model grounded in TCM symptom ontology, specifically designed to address the limitations of existing entity recognition models characterized by limited label spaces and an insufficient integration of domain knowledge. This model synergizes a knowledge graph with the TCM symptom ontology framework to facilitate a standardized symptom classification system and enrich it with domain-specific knowledge. It innovatively merges the conventional bidirectional encoder representations from transformers (BERT) + bidirectional long short-term memory (Bi-LSTM) + conditional random fields (CRF) entity recognition methodology with a multi-label classification strategy, thereby adeptly navigating the intricate label interdependencies in the textual data. Introducing a multi-associative feature fusion module is a significant advancement, thereby enabling the extraction of pivotal entity features while discerning the interrelations among diverse categorical labels. The experimental outcomes affirm the model's superior performance in multi-label symptom extraction and substantially elevates the efficiency and accuracy. This advancement robustly underpins research in TCM syndrome differentiation and disease diagnoses.