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Under partial-load conditions, the combined application of exhaust gas recirculation of heat recovery steam generator and compressor inlet guide vane adjustment (EGR-IGVC) can effectively improve the performance of gas turbine combined cycle. However, if this strategy is combined with the temperature control scheme of constant T3(turbine inlet temperature)-T4m(maximum allowable turbine exhaust temperature), which is often adopted in gas turbine combined cycles under part-load conditions, it would cause a large bottoming cycle exergy destruction and a significant decrease in bottoming cycle power output at relatively lower loads. In this paper, a constant T3-T4m-T4d (the design value of turbine exhaust temperature) scheme suitable for the EGR-IGVC strategy is proposed, the PG9351FA gas turbine combined cycle unit is taken as the research object, and the partial-load performance of combined cycle under the two temperature control schemes is compared and investigated based on energy and exergy analysis. The results show that the combination of the EGR-IGVC strategy with the constant T3-T4m scheme is still the best at the ambient temperature of 15 ℃ and the partial-load rate of above 80%. At a load of 30%—80%, compared with the constant T3-T4m scheme, the EGR-IGVC strategy combined with the constant T3-T4m-T4d scheme can increase the gas turbine efficiency by 0.15%—0.47%, and decrease the exergy destruction of the heat recovery steam generator by more than 0.51%(2.15 MW). The results also show that adopting the constant T3-T4m-T4d scheme can always obtain higher combined cycle efficiency when the ambient temperature varies between 0 and 40 ℃. In addition, the increase in partial-load efficiency becomes more evident with the rise of ambient temperature.

Abstract BackgroundDepression represents a pressing global public health concern, impacting the physical and mental well-being of hundreds of millions worldwide. Notwithstanding advances in clinical practice, an alarming number of individuals at risk for depression continue to face significant barriers to timely diagnosis and effective treatment, thereby exacerbating a burgeoning social health crisis. ObjectiveThis study seeks to develop a novel online depression risk detection method using natural language processing technology to identify individuals at risk of depression on the Chinese social media platform Sina Weibo. MethodsFirst, we collected approximately 527,333 posts publicly shared over 1 year from 1600 individuals with depression and 1600 individuals without depression on the Sina Weibo platform. We then developed a hierarchical transformer network for learning user-level semantic representations, which consists of 3 primary components: a word-level encoder, a post-level encoder, and a semantic aggregation encoder. The word-level encoder learns semantic embeddings from individual posts, while the post-level encoder explores features in user post sequences. The semantic aggregation encoder aggregates post sequence semantics to generate a user-level semantic representation that can be classified as depressed or nondepressed. Next, a classifier is employed to predict the risk of depression. Finally, we conducted statistical and linguistic analyses of the post content from individuals with and without depression using the Chinese Linguistic Inquiry and Word Count. ResultsWe divided the original data set into training, validation, and test sets. The training set consisted of 1000 individuals with depression and 1000 individuals without depression. Similarly, each validation and test set comprised 600 users, with 300 individuals from both cohorts (depression and nondepression). Our method achieved an accuracy of 84.62%, precision of 84.43%, recall of 84.50%, and F1F1 ConclusionsThe research results indicate the feasibility and effectiveness of using deep learning methods to detect the risk of depression. These findings provide insights into the potential for large-scale, automated, and noninvasive prediction of depression among online social media users.

Introduction: Although inguinal hernia (IH) is prevalent in elderly males, research on its specific diagnostic biomarkers is limited. Protein N-glycosylation is one of the most important and ubiquitous post-translational modifications and often results in a remarkable heterogeneity of protein glycoforms. Protein N-glycosylation often changes in a disease and holds great potential for discovering non-invasive biomarkers. This study aimed to gain insights into total serum protein N-glycosylation of IH to identify candidate non-invasive biomarkers for diagnosis and subtype classification of IH. Methods: Linkage-specific sialylation derivatization combined with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry detection was used to analyze serum protein N-glycosylation patterns in IH patients and healthy controls. Results: IH patients had abnormal glycan fucosylation and sialylation compared to healthy controls (HC), of which two glycan traits representing linkage-specific sialylation within monoantennary glycans showed high potential as diagnostic biomarkers for IH with an area under the curve (AUC) of 0.75. Additionally, serum N-glycans were different between indirect IH and direct IH in glycosylation features, namely complexity, fucosylation, galactosylation, sialylation, and α2,6‐linked sialylation. Four distinctive glycans between the two subtypes showed good performance with AUC >0.8, suggesting that these glycan traits have potential as biomarkers for subtype classification. Conclusions: We first reported the serum N-glycomic features of IH patients. Furthermore, we identified several potential biomarkers for the diagnosis and subtype classification of IH. These findings can deepen the understanding of IH.

Summary: Prioritizing electric power system decarbonization is crucial for meeting global carbon neutrality targets. However, the role of energy technology cost reduction driven by innovation in advancing carbon neutrality in the electric power system has not been well studied. To fill this gap, an integrated investment planning and operation model is developed to simulate the carbon neutral pathway in the electric power system over a 30-year period from 2020 to 2050. The learning curves with different learning rates are incorporated into the model to represent different energy technology innovation scenarios. According to our results, the advanced innovation scenario is projected to achieve carbon neutrality in the electric power system five years earlier compared with the conservative innovation scenario, with a cost savings of 465 billion CNY. In addition, both inter-regional and intra-regional collaboration facilitate the achievement of carbon neutrality in the electric power system at a reduced cost. Science for society: Reducing carbon emissions and achieving carbon neutrality is a critical global challenge. Previous studies have formulated pathways for energy transition in the electric power system to meet carbon reduction targets. However, many of them overlook the role of energy technology cost reduction driven by innovation in advancing carbon neutrality in the electric power system. In this study, an integrated investment planning and operation model is developed to simulate the transition pathway in the electric power system from 2020 to 2050 toward carbon neutrality under different innovation scenarios. Our results reveal that carbon neutrality can be achieved earlier and more cost effectively in the advanced energy technology innovation scenario. Furthermore, expanding both inter-regional and intra-regional transmission capacities can facilitate the achievement of carbon neutrality in the electric power system at a reduced cost.

To investigate the effects of initial protein, fat content, and storage temperature on the shelf life of UHT pure milk packaging, three types of UHT pure milk were used as research objects to experimentally measure sample browning index and protein hydrolysis index during storage at 23, 30, and 37 ℃. Integrate the dataset and determine specific input parameters based on its performance on the prediction set, and carry out UHT pure milk packaging shelf life prediction based on BP neural network. The results showed that the fitting degrees of the BP neural network model for the browning index and protein hydrolysis index of UHT milk were 0.9412 and 0.9527, respectively, and compared with traditional multiple linear regression model’s number of 0.8799 and 0.9211, the BP neural network model with optimized hidden layer neuron numbers had higher prediction accuracy for the changes in characteristic indicators during the storage period of UHT pure milk, providing technical support for rapid and accurate prediction of the shelf life of UHT pure milk with different formulas.

Neutrinoless double beta decay (0νββ) provides a way to probe physics beyond the Standard Model of particle physics. The upcoming nEXO experiment will search for 0νββ decay in 136Xe with a projected half-life sensitivity exceeding 1028 years at the 90% confidence level using a liquid xenon (LXe) Time Projection Chamber (TPC) filled with 5 tonnes of Xe enriched to ∼90% in the ββ-decaying isotope 136Xe. In parallel, a potential future upgrade to nEXO is being investigated with the aim to further suppress radioactive backgrounds and to confirm ββ-decay events. This technique, known as Ba-tagging, comprises extracting and identifying the ββ-decay daughter 136Ba ion. One tagging approach being pursued involves extracting a small volume of LXe in the vicinity of a potential ββ-decay using a capillary tube and facilitating a liquid-to-gas phase transition by heating the capillary exit. The Ba ion is then separated from the accompanying Xe gas using a radio-frequency (RF) carpet and RF funnel, conclusively identifying the ion as 136Ba via laser-fluorescence spectroscopy and mass spectrometry. Simultaneously, an accelerator-driven Ba ion source is being developed to validate and optimize this technique. The motivation for the project, the development of the different aspects, along with the current status and results, are discussed here.

In power systems, failures of vulnerable lines can trigger large-scale cascading failures, and vulnerability assessment is dedicated to locating these lines and reducing the risks of such failures. Based on a structure and attribute network embedding (SANE) algorithm, a novel quantitative vulnerability analysis method is proposed to identify vulnerable lines in this research. First, a two-layered random walk network with topological and electrical properties of transmission lines is established. Subsequently, based on the weighted degree of nodes in the two-layered network, the inter-layer and intra-layer walking transition probabilities are developed to obtain walk sequences. Then, a Word2Vec algorithm is applied to obtain low-dimension vectors representing transmission lines, according to obtained walk sequences for calculating the vulnerability index of transmissions lines. Finally, the proposed method is compared with three widely used methods in two test systems. Results show the network embedding based method is superior to those comparison methods and can provide guidance for identifying vulnerable lines.

Abstract Osteoporosis is a progressive bone disease caused by impaired function of endogenous bone marrow-derived mesenchymal stem cells (BMSCs). Herein, we investigated the mechanism of lncRNA SNHG14 in osteoporosis progression. BMSCs were isolated from BALB/c mice. The osteogenic ability of BMSCs was assessed by Alkaline phosphatase (ALP) and Alizarin Red S Staining (ARS) staining. The interaction between miR-493-5p and SNHG14 or myocyte enhancer factor 2 C (Mef2c) was confirmed by dual-luciferase reporter assay. Bone histomorphometry changes were evaluated to analyze SNHG14’roles in osteoporosis in vivo. Our results illustrated SNHG14 and Mef2c levels were increased in a time-dependent manner in BMSCs, and miR-493-5p expression was decreased. SNHG14 knockdown inhibited osteogenic differentiation of BMSCs, and SNHG14 upregulation had the opposite effect. SNHG14 overexpression elevated bone mineral density and bone trabecular number, and alleviated osteoporosis progression in vivo. Mechanically, miR-493-5p was a target of SNHG14, and miR-493-5p targeted the Mef2c gene directly. SNHG14 overexpression reversed the inhibition of miR-493-5p on the osteogenic ability of BMSCs, and miR-493-5p silencing accelerated BMSCs osteogenesis by activating Mef2c-mediated autophagy to accelerate BMSCs osteogenesis. In short, SNHG14 activated autophagy via regulating miR-493-5p/Mef2c axis to alleviate osteoporosis progression, which might provide a new molecular target for osteoporosis treatment.

Change in pH in growth conditions is the primary stress for most neutralophilic bacteria, including model microorganism Escherichia coli. However, different survival capacities under acid stress in different bacteria are ubiquitous. Research on different acid-tolerance mechanisms in microorganisms is important for the field of combating harmful gut bacteria and promoting fermentation performance of industrial strains. Therefore, this study aimed to carry out a narrative review of acid-stress response mechanism of E. coli discovered so far, including six AR systems, cell membrane protection, and macromolecular repair. In addition, the application of acid-tolerant E. coli in industry was illustrated, such as production of industrial organic acid and developing bioprocessing for industrial wastes. Identifying these aspects will open the opportunity for discussing development aspects for subsequent research of acid-tolerant mechanisms and application in E. coli.

To investigate the recovery effect of natural self-healing of freeze-thaw damage of cementitious material-poor hydraulic concrete, we carried out a natural self-healing test for hydraulic concrete damaged by up to 200 rapid freeze-thaw cycles. The self-healing effect of macro-mechanical properties of hydraulic concrete damaged by freeze-thaw cycles was quantitatively characterized by the recovery rate of the specimens which underwent different numbers of freeze-thaw cycles then continued the standard curing to 28 days. Next, the microscopic properties of damaged hydraulic concrete after self-healing were further investigated by combining XRD, SEM and nitrogen adsorption tests. Finally, a prediction model of the recovery rate of macro-mechanical properties was established by using the microscopic pore test results. The results showed that, when the hydraulic concrete undergoes the damage of different freeze-thaw cycles and continues to standard curing for 28 days, the concrete specimens manifest natural self-healing to some extent even if damaged by up to 200 freeze-thaw cycles.

Abstract Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20 kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3% at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program which began in 2017 and elapsed for about four years. Based on this mass characterization and a set of specific requirements, a good quality of all accepted PMTs could be ascertained. This paper presents the performed testing procedure with the designed testing systems as well as the statistical characteristics of all 20-inch PMTs intended to be used in the JUNO experiment, covering more than fifteen performance parameters including the photocathode uniformity. This constitutes the largest sample of 20-inch PMTs ever produced and studied in detail to date, i.e. 15,000 of the newly developed 20-inch MCP-PMTs from Northern Night Vision Technology Co. (NNVT) and 5000 of dynode PMTs from Hamamatsu Photonics K. K.(HPK).

A fall k-coloring of a graph G is a proper k-coloring of G such that each vertex has at least one neighbor in each of the other color classes. A graph G which has a fall k-coloring is equivalent to having a partition of the vertex set V(G) in k independent dominating sets. In this paper, we first prove that for any fall k-colorable graph G with order n, the number of edges of G is at least (n(k−1)+r(k−r))/2, where r≡n(modk) and 0≤r≤k−1, and the bound is tight. Then, we obtain that if G is k-colorable (k≥2) and the minimum degree of G is at least k−2k−1n, then G is fall k-colorable and this condition of minimum degree is the best possible. Moreover, we give a simple proof for an NP-hard result of determining whether a graph is fall k-colorable, where k≥3. Finally, we show that there exist an infinite family of fall k-colorable planar graphs for k∈{5,6}.

Non-invasive biomarkers for the diagnosis and prognosis of papillary thyroid microcarcinoma (PTMC) are still urgently needed. We aimed to characterize the N-glycome of PTMC, and establish nomograms for the diagnosis of PTMC and the prediction of lymph node metastasis (LNM). N-glycome of PTMC (LNM vs. non-LNM, capsular invasion (CI) vs. non-CI (NCI)) and matched healthy controls (HC) were quantitatively analyzed based on mass spectrometry. N-glycan traits associated with PTMC/LNM were used to create binomial logistic regression models and were visualized as nomograms. We found serum N-glycome differed between PTMC and HC in high-mannose, complexity, fucosylation, and bisection, of which, four N-glycan traits (TM, CA1, CA4, and A2Fa) were significantly associated with PTMC. The nomogram based on four traits achieved good performance for the identification of PTMC. Two N-glycan traits (CA4 and A2F0S0G) showed strong associations with LNM. The nomogram based on two traits showed relatively good performance in predicting LNM. We also found differences between CI and NCI in several N-glycan traits, which were not the same as that associated with LNM. This study reported serum N-glycosylation signatures of PTMC for the first time. Nomograms constructed from aberrant glycans could be useful tools for PTMC diagnosis and stratification.

BackgroundAberrant N-glycosylation and its involvement in pathogenesis have been reported in endometrial cancer (EC). Nevertheless, the serum N-glycomic signature of EC remains unknown. Here, we investigated serum N-glycome patterns of EC to identify candidate biomarkers.MethodsThis study enrolled 34 untreated EC patients and 34 matched healthy controls (HC) from Peking Union Medical College Hospital. State-of-the-art MS-based methods were employed for N-glycans profiling. Multivariate and univariate statistical analyses were used to identify discriminative N-glycans driving classification. Receiver operating characteristic analyses were performed to evaluate classification accuracy.ResultsEC patients displayed distinct differences in serum N-glycome and had abnormal high-mannose and hybrid-type N-glycans, fucosylation, galactosylation, and linkage‐specific sialylation compared with HC. The glycan panel built with the four most discriminative and biologically important derived N-glycan traits could accurately identify EC (random forest model, the area under the curve [AUC]=0.993 [95%CI 0.955-1]). The performance was validated by two other models. Total hybrid-type N-glycans significantly associated with the differentiation types of EC could effectively stratify EC into well- or poorly-differentiated subgroups (AUC>0.8).ConclusionThis study provides the initial evidence supporting the utility of serum N-glycomic signature as potential markers for the diagnosis and phenotyping of EC.

Objectives: Epithelial-mesenchymal transition (EMT) of renal tubular epithelial cells (RTECs) is a pathogenic factor for renal interstitial fibrosis (RIF). Interleukin-33 (IL-33) is related to the occurrence and development of RIF, but the underlying mechanism remains unclear. Here, we investigated whether IL-33 mediates the EMT of RTECs by activating the NF-κB/Twist1 signalling pathway. Methods: In vivo, the RIF animal model induced by unilateral ureteral obstruction (UUO) was established. The effects of exogenous IL-33 and anti-IL-33 antibodies were evaluated. In vitro, the EMT of RTECs was induced by IL-33. The inhibition of the nuclear factor kappa-B (NF-κB) by the pyrrolidine dithiocarbamate (PDTC) and the knockdown of the suppression of tumorigenicity 2 (ST2) by small interference RNA were used to observe whether IL-33 mediates the EMT of RTECs through the NF-κB/Twist1 signaling pathway. Results: In vivo, exogenous IL-33 significantly aggravated UUO-induced pathological damage and collagen deposition, down-regulated E-cadherin expression, and up-regulated α-smooth muscle actin and Vimentin expressions. Moreover, exogenous IL-33 increased the levels of phospho-IκB-α (p-IκB-α) and phospho-NF-κB p65 (p-NF-κB p65), NF-κB p65 nuclear translocation, and Twist1 nuclear expression. However, these effects were reversed by the anti-IL-33 antibody. In vitro , the increases in the levels of p-IκB-α, p-NF-κB p65, NF-κB p65 nuclear translocation, and Twist1 nuclear expression induced by IL-33 were inhibited by the knockdown of PDTC or ST2. IL-33-mediated EMT of RTECs was also significantly reversed. However, NF-κB inhibitor PDTC had no significant effect on ST2 expression. Conclusions: The IL-33/ST2 axis may up-regulate the expression of Twist1 through the NF-κB signalling pathway, thereby inducing the EMT of RTECs and leading to RIF.

The physics potential of detecting ^8 B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model-independent manner by using three distinct channels of the charged current (CC), neutral current (NC), and elastic scattering (ES) interactions. Due to the largest-ever mass of ^13 C nuclei in the liquid scintillator detectors and the expected low background level, ^8 B solar neutrinos are observable in the CC and NC interactions on ^13 C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC, and ES channels to guarantee the observation of the ^8 B solar neutrinos. From the sensitivity studies performed in this work, we show that JUNO, with 10 yr of data, can reach the 1 σ precision levels of 5%, 8%, and 20% for the ^8 B neutrino flux, ${\sin }^{2}{\theta }_{12}$ , and ${\rm{\Delta }}{m}_{21}^{2}$ , respectively. Probing the details of both solar physics and neutrino physics would be unique and helpful. In addition, when combined with the Sudbury Neutrino Observatory measurement, the world's best precision of 3% is expected for the measurement of the ^8 B neutrino flux.

BackgroundAnxiety disorder has become a major clinical and public health problem, causing a significant economic burden worldwide. Public attitudes toward anxiety can impact the psychological state, help-seeking behavior, and social activities of people with anxiety disorder. ObjectiveThe purpose of this study was to explore public attitudes toward anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders on Sina Weibo, a Chinese social media platform that has about 582 million users, as well as the psycholinguistic and topical features in the text content of the posts. MethodsFrom April 2018 to March 2022, 325,807 Sina Weibo posts with the keyword “anxiety disorder” were collected and analyzed. First, we analyzed the changing trends in the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends in the language features of the posts, in which 20 linguistic features were selected and presented. Third, a topic model (biterm topic model) was used for semantic content analysis to identify specific themes in Weibo users’ attitudes toward anxiety. ResultsThe changing trends in the number and the total length of posts indicated that anxiety-related posts significantly increased from April 2018 to March 2022 (R2=0.6512; P

BackgroundProgrammed cell death protein-1/programmed cell death ligand-1 (PD-1/PD-L1) inhibitors works by reactivating immune cells. Considering the accessibility of noninvasive liquid biopsies, it is advisable to employ peripheral blood lymphocyte subsets to predict immunotherapy outcomes.MethodsWe retrospectively enrolled 87 patients with available baseline circulating lymphocyte subset data who received first-line PD-1/PD-L1 inhibitors at Peking Union Medical College Hospital between May 2018 and April 2022. Immune cell counts were determined by flow cytometry.ResultsPatients who responded to PD-1/PD-L1 inhibitors had significantly higher circulating CD8+CD28+ T-cell counts (median [range] count: 236 [30-536] versus 138 [36-460]/μL, p < 0.001). Using 190/μL as the cutoff value, the sensitivity and specificity of CD8+CD28+ T cells for predicting immunotherapy response were 0.689 and 0.714, respectively. Furthermore, the median progression-free survival (PFS, not reached versus 8.7 months, p < 0.001) and overall survival (OS, not reached versus 16.2 months, p < 0.001) were significantly longer in the patients with higher CD8+CD28+ T-cell counts. However, the CD8+CD28+ T-cell level was also associated with the incidence of grade 3-4 immune-related adverse events (irAEs). The sensitivity and specificity of CD8+CD28+ T cells for predicting irAEs of grade 3-4 were 0.846 and 0.667, respectively, at the threshold of CD8+CD28+ T cells ≥ 309/μL.ConclusionsHigh circulating CD8+CD28+ T-cell levels is a potential biomarker for immunotherapy response and better prognosis, while excessive CD8+CD28+ T cells (≥ 309/μL) may also indicate the emergence of severe irAEs.

IntroductionShellfish play an important role in ecological restoration and as carbon (C) sinks, but studies on their ecological carrying capacity (ECC) and C sequestration potential are sparse.MethodsIn this study, we selected a 57-hectare artificial oyster reef in a typical marine ranching in Bohai Bay, China, to evaluate the ECC and their C sequestration potential of bivalve shellfish, and projecting their impact on functional groups in the system, with an Ecopath with Ecosim (EwE) food web model. We conducted four biological surveys to obtain the biomass measurements, with one conducted in each of the summer, autumn, and winter of 2019 and one in the spring of 2020; and the functional groups included in the surveys comprised fish, cephalopods, crustaceans, snails, bivalve shellfish, annelids, other macrobenthos, meiobenthos, starfish, sea cucumbers, zooplankton, phytoplankton, and detritus.Results and DiscussionThe EwE model prediction results showed that the ECC of bivalve shellfish was established to be 282.66 t/km2, far more than the existing quantity of 187.76 t/km2. Therefore, at present, the ecosystem of the study marine ranching is not yet mature. Moreover, our ecological network analysis parameters indicated that the marine ranching ecosystem will be mature and stable when the bivalve shellfish population reaches its ECC. However, the increase in bivalve shellfish biomass will result in a decrease in the population sizes of species competing for food resources with bivalve shellfish, mainly gobiid fish such as Tridentiger bifasciatus, Tridentiger trigonocephalus, Tridentiger barbatus. Simultaneously, when the bivalve shellfish reach their ECC, 29.23 t of CO2 can be sequestrated by bivalve shellfish, comprising 14.32 t being removed from the ecosystem as prey and 14.91 t being stored on the seafloor through biodeposition.ConclusionTherefore, the research demonstrated that, within the scope of ECC, the increasing bivalve shellfish can improve the C sequestration capacity of the marine ranch ecosystem, and effective management of bivalve shellfish in marine ranching can improve the economic benefits and C sink service functions of marine ranching.