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Abstract This study focuses on the degradation behaviour of metallised film capacitors, which are the essential components for the stability of converter valves in flexible ultra‐high voltage direct current (HVDC) transmission systems. Through systematic experimentation, we investigated the failure mechanisms of MFCs under HVDC fields with superimposed harmonics, considering both equipment and material perspectives. The experiments subjected capacitors to 500 h of ageing under two conditions: a DC/AC‐superimposed field with a constant DC component of 290 kV/mm and an AC ripple rate varying from 12% to 28%, and a control group aged solely under a DC field. Our findings indicate that capacitors aged under the DC/AC‐superimposed field exhibited shorter lifespans and more significant capacitance loss than those aged under only the DC field. This difference in performance is primarily attributed to the distinct electrode loss behaviours observed under each ageing condition, which are key factors in the capacitors' capacitance decay. Moreover, the biaxially oriented polypropylene films in the DC‐aged samples showed more severe deterioration, characterised by more noticeable molecular chain scission and reduced breakdown strength, compared to those aged under the DC/AC superimposed field. This difference is partly due to the moderate temperature increase caused by harmonics, which benefits the aggregation structure, and partly to the reduced molecular structure damage from the AC field.

Abstract The rapid advancements in information technology and intelligent systems within urban rail transit (URT) systems have highlighted the need for more personalized route recommendations that consider passengers’ travel habits. This study aims to address this issue by investigating passenger travel routes alongside other passengers who share similar travel preferences, utilizing collaborative filtering (CF) techniques. The approach involves analyzing historical card data to assess passenger travel profiles, including actual travel time under crowded conditions. By considering both individual passenger preferences and the preferences of similar passengers, a CF algorithm is employed to offer personalized route recommendations. The Shenzhen metro is used as a case study to illustrate the proposed method. The results demonstrate that the proposed approach surpasses traditional route recommendation methods by providing tailored suggestions that align more closely with passengers’ travel preferences. These findings emphasize the value of incorporating passenger travel preferences into route recommendation models, thereby enhancing the accuracy and effectiveness of personalized route recommendations within URT systems.

Abstract Some studies have reported that the gut microbiota can influence adrenal-related hormone levels. However, the causal effects of the gut microbiota on adrenal function remain unknown. Therefore, we employed a two-sample Mendelian randomization (MR) study to systematically investigate the impact of gut microbiota on the function of different regions of the adrenal gland. The summary statistics for gut microbiota and adrenal-related hormones used in the two-sample MR analysis were derived from publicly available genome-wide association studies (GWAS). In the MR analysis, inverse variance weighting (IVW) was used as the primary method, with MR-Egger, weighted median, and cML-MA serving as supplementary methods for causal inference. Sensitivity analyses such as the MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis were used to assess pleiotropy and heterogeneity. We identified 27 causal relationships between 23 gut microbiota and adrenal function using the IVW method. Among these, Sellimonas enhanced the function of the adrenal cortex reticularis zone (beta = 0.008, 95% CI: 0.002–0.013, P = 0.0057). The cML-MA method supported our estimate (beta = 0.009, 95% CI: 0.004–0.013, P = 2 × 10− 4). Parasutterella, Sutterella, and Anaerofilum affect the functioning of different regions of the adrenal gland. Notably, pleiotropy was not observed. Our findings revealed that the gut microbiota is causally associated with adrenal function. This enhances our understanding of the gut-microbiota-brain axis and provides assistance in the early diagnosis and treatment of adrenal-related diseases in clinical practice.

Abstract Immune cells-enhanced immunotherapy exhibits unprecedented overall survival-prolongation even curable in some cancer patients. Although so, most of the patients show no response. Tumor microenvironment (TME) where immune cells settle down has multi-faceted influences, but usually creates an immunosuppressive niche that facilitating tumor cells escape from immune attack. The metabolites and malnutrition of TME exert enormous effects on the resident immune cells, but the underlying mechanism is largely unknown. The stromal interaction molecules 2 (STIM2) is an endoplasmic reticulum (ER) calcium (Ca2+) sensor to maintain Ca2+ homeostasis. Notably, the cytosol STIM2 C-terminus is long with various domains that are available for the combination or/and molecular modification. This distinct structure endows STIM2 with a high susceptibility to numerous permeable physico-chemical molecules or protein interactions. STIM2 and its variants are extensively expressed in various immune cells, especially in T immune cells. STIM2 was reported closely correlated with the function of immune cells via regulating Ca2+ signaling, energy metabolism and cell fitness. Herein, we sum the latest findings on the STIM2 structure, focusing on its distinct characteristics and profound effect on the regulation of Ca2+ homeostasis and multi-talented functionality. We also outline the advancements on the underlying mechanism how STIM2 anomalies influence the function of immune cells and on the turbulent expression or/and amenably modification of STIM2 within the tumor niches. Then we discuss the translation of these researches into antitumor approaches, emphasizing the potential of STIM2 as a therapeutic target for direct inhibition of tumor cells or more activation towards immune cells driving to flare TME. This review is an update on STIM2, aiming to rationalize the potential of STIM2 as a therapeutic target for immunomodulation, engaging immune cells to exert the utmost anti-tumor effect.

Abstract Dynamic tracking of spinal instrumentation could facilitate real-time evaluation of hardware integrity and in so doing alert patients/clinicians of potential failure(s). Critically, no method yet exists to continually monitor the integrity of spinal hardware and by proxy the process of spinal arthrodesis; as such hardware failures are often not appreciated until clinical symptoms manifest. Accordingly, herein, we report on the development and engineering of a bio-adhesive metal detector array (BioMDA), a potential wearable solution for real-time, non-invasive positional analyses of osseous implants within the spine. The electromagnetic coupling mechanism and intimate interfacial adhesion enable the precise sensing of the metallic implants position without the use of radiation. The customized decoupling models developed facilitate the precise determination of the horizontal and vertical positions of the implants with incredible levels of accuracy (e.g.,

The Huagang Formation of the central inverted structural tectonic belt structure A in the Xihu Depression has abundant hydrocarbon resources. This is an important area for exploration and development in the East China Sea Basin (ECSB). Due to the high drilling costs in offshore basins and limited drilling in Structure A, the mechanism of hydrocarbon accumulation is complex, and the laws and processes of hydrocarbon accumulation in structure A are still unclear. To clarify the hydrocarbon accumulation process in the Huagang Formation of structure A, this study used thin section observation, scanning electron microscopy, natural gas carbon isotopes, fluid inclusions, and micro thermometry. Combined with structural evolution and burial history, the origin and source of natural gas, hydrocarbon accumulation periods, and processes were systematically analyzed. The research shows that the sandstone reservoir in the Huagang Formation has satisfactory reservoir, trapping, and preservation conditions; the primary hydrocarbon accumulation period was the Longjing Movement. Therefore, early natural gas filling existed before the Longjing Movement. Based on the evolutionary and fluid potential characteristics of the top tectonic peak of the Pinghu Formation, the natural gas generated in the early stage of structure A is stored in the paleo-structural high point of the Pinghu Formation (paleo convergence area). In the early stage of the Longjing Movement, natural gas migrated from the paleo convergence area of the Pinghu Formation to the current tectonic high point and accumulated in the trap. Therefore, three different “convergence ahead of accumulation” reservoir formation models were developed. These reservoir formation models are critical for guiding the exploration of a series of large traps, including structure A and its surrounding structures, and they have important reference significance for natural gas exploration in other basins.

Abstract Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO2 memristor including endurance (>1012), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (

Abstract A 27-year-old man was admitted to the hospital after a year of marriage due to infertility. During laparoscopic exploratory surgery, the presence of a retrovesical uterus was clearly observed, and the gonadal organs were visible on both sides. However, the testicles or ovaries were not identifiable, nor were the spermatic vessels and fallopian tubes at the joint. Intraoperative bilateral gonad biopsy was performed. Cryopreservation of the right gonadal gland revealed gonadoblastoma and malignant germinoma (asexual tumor/seminoma) with sclerosis and atrophy of testicular tissue. No proliferation of germ cells and sertoli cells was observed in spermatic tubule. The left gonad was diagnosed as a gonadoblastoma. Finally, total hysterectomy and bilateral gonadal tumor organectomy were performed to seal the vaginal stump. Local radiotherapy was administered after surgery. In general, tumors were found on both sides of the gonads, especially gonadoblastoma and malignant germinoma on the right side and gonadoblastoma on the left side.

High-performance dielectric tunable materials with both high dielectric tunability and low dielectric loss are urgently needed for new-generation electronic tunable devices. In the present study, a new system, (Ba0.675Sr0.325)1−xLaxTi1−xMnxO3 (x = 0.25%, 0.5%, 0.75%, and 1.0%), was designed. The acceptor dopant Mn was added to lower dielectric loss, while the donor dopant La was introduced to enhance dielectric tunability. The samples were prepared using the conventional solid-state (CS) reaction method and the semi-solution (SS) method. The experimental results showed that the morphology of the ceramics was optimized by further improving the processing procedure. Dense microstructures, homogeneous grains, and uniform dopant distributions could be achieved successfully by the semi-solution method. Moreover, a significant enhancement in the tunable properties was realized owing to the improved microstructure mentioned above. The optimum tunable properties occurred in the samples prepared by the semi-solution method at x = 0.75%, with a high dielectric tunability of 85.0%, a low dielectric loss of 0.0011, and an excellent figure of merit (FOM) of 773. The tunable properties of (Ba,Sr)TiO3 (BST) ceramics were even superior to those of lead-based materials, with an FOM of approximately 700. All the results suggested that the semi-solution method rendered BST ceramics more promising for applications in tunable devices.

Abstract Background The glandular trichomes of tobacco (Nicotiana tabacum) can efficiently produce secondary metabolites. They act as natural bioreactors, and their natural products function to protect plants against insect-pests and pathogens and are also components of industrial chemicals. To clarify the molecular mechanisms of tobacco glandular trichome development and secondary metabolic regulation, glandular trichomes and glandless trichomes, as well as other different developmental tissues, were used for RNA sequencing and analysis. Results By comparing glandless and glandular trichomes with other tissues, we obtained differentially expressed genes. They were obviously enriched in KEGG pathways, such as cutin, suberine, and wax biosynthesis, flavonoid and isoflavonoid biosynthesis, terpenoid biosynthesis, and plant–pathogen interaction. In particular, the expression levels of genes related to the terpenoid, flavonoid, and wax biosynthesis pathway mainly showed down-regulation in glandless trichomes, implying that they lack the capability to synthesize certain exudate compounds. Among the differentially expressed genes, 234 transcription factors were found, including AP2-ERFs, MYBs, bHLHs, WRKYs, Homeoboxes (HD-ZIP), and C2H2-ZFs. These transcription factor and genes that highly expressed in trichomes or specially expressed in GT or GLT. Following the overexpression of R2R3-MYB transcription factor Nitab4.5_0011760g0030.1 in tobacco, an increase in the number of branched glandular trichomes was observed. Conclusions Our data provide comprehensive gene expression information at the transcriptional level and an understanding of the regulatory pathways involved in glandular trichome development and secondary metabolism.

Abstract Background Despite fractures of Isolated Weber B being prevalent, there is a lack of clarity regarding the relative effectiveness of surgical versus conservative treatment. This systematic review and meta-analysis aimed to investigate the clinical effects and complications of surgical versus conservative treatment of the Isolated Weber B ankle fractures. Methods This study involved thorough searches across multiple electronic databases, including PubMed, Cochrane, Embase, and Web of Science, to identify all relevant publications on Isolated Weber B ankle fractures repaired through surgical versus conservative treatment. Through a comprehensive meta-analysis, several outcomes were evaluated, including post-operative function, complications and reoperation rate. Result Six articles involving 818 patients who met the inclusion criteria. Among these participants, 350 were male and 636 were female. 651 patients received conservative treatment, while 396 underwent surgical intervention. The findings indicate no significant differences in OMAS, FAOQ, PCS, MCS scores, and return to work between surgical and non-surgical treatments for isolated Weber B ankle fractures. However, compared with surgical treatment, non-surgical treatment has a higher AOFAS score(MD = -5.31, 95% CI = [-9.06, -1.55], P = 0.20, I2 = 39%), lower VAS score(MD = 0.72, 95% CI = [0.33, 1.10], P = 0.69, I2 = 0%), lower complication rate (RR = 3.06, 95% CI = [1.58, 6.01], P = 0.05, I2 = 54%), and lower reoperation rate(RR = 8.40, 95% CI = [1.57, 45.06], P = 0.05, I2 = 67%). Conclusion

This study investigated an extreme sea surface warming in the midlatitude western North Pacific (MLWNP) during the summer of 2012. The 2012 extreme event was characterized by warm sea surface temperature anomaly (SSTA) extending from the East/Japan Sea to central North Pacific. The SSTA box–averaged over the MLWNP (130–180°E, 33–50°N) in 2012 ranked as the third warmest in recent four decades, which has caused intense marine heatwaves in this region. During the summer of 2012, a positive Indian Ocean Dipole event co-occurred with El Niño, favoring anomalous moisture transport between the two basins that caused enhanced convection in the South China and Philippine Seas and western–to–central subtropical Pacific. The enhanced convective activities triggered two meridional atmospheric Rossby wave trains to form strong atmospheric blocking high–pressure systems in the MLWNP. This reduced the total cloud cover and surface wind speed, enhancing insolation and reducing the release of latent heat flux. In addition, the weakened wind strengthened the stratification and shoaled the mixed layer. As a result, the increased net heat flux into the ocean accompanied by a shallower mixed layer contributed to the upper ocean warming in the MLWNP. Meanwhile, the North Pacific was dominated by a negative phase of Pacific Decadal Oscillation (PDO), significantly contributing to warm SSTAs in the MLWNP in 2012. Consequently, the 2012 extreme warming in the MLWNP was the results of the combination of atmospheric Rossby waves and PDO. Our study highlighted the roles of high–frequency atmospheric teleconnection and low–frequency PDO in extreme sea surface warming in the MLWNP.

Microbial secondary metabolites are a rich source for pharmaceutical discoveries and play crucial ecological functions. While tools exist to identify secondary metabolite clusters in genomes, precise sequence-to-function mapping remains challenging because neither function nor substrate specificity of biosynthesis enzymes can accurately be predicted. Here, we developed a knowledge-guided bioinformatic pipeline to solve these issues. We analyzed 1928 genomes of Pseudomonas bacteria and focused on iron-scavenging pyoverdines as model metabolites. Our pipeline predicted 188 chemically different pyoverdines with nearly 100% structural accuracy and the presence of 94 distinct receptor groups required for the uptake of iron-loaded pyoverdines. Our pipeline unveils an enormous yet overlooked diversity of siderophores (151 new structures) and receptors (91 new groups). Our approach, combining feature sequence with phylogenetic approaches, is extendable to other metabolites and microbial genera, and thus emerges as powerful tool to reconstruct bacterial secondary metabolism pathways based on sequence data.

Abstract Tomographic synthetic aperture radar is an advanced multi‐channel interferometric technique for retrieving 3‐D spatial information. It can be regarded as an inherently sparse reconstruction problem and can be solved using compressive sensing algorithms. However, the performances are limited by the number of acquisitions and suffer from computational burdens in practice. This paper proposes a novel method based on deep learning, which is carried out and optimized in an end‐to‐end manner by the generative adversarial neural networks. The proposed method applies the cascaded U‐Net architectures to achieve the reconstruction of full‐channel synthetic aperture radar images and the refinement of obtained tomographic results, respectively. The proposed network is trained using simulated data and validate the technique on simulated and real data. The tests show promising results with the limited number of acquisitions while reducing the computation time.

The study focuses on the critically endangered Dabry’s sturgeon (Acipenser dabryanus), a species on the brink of extinction in the wild. This research investigates the role of the Forkhead box protein L2 (foxl2) in the gonadal development and differentiation of this species. The foxl2 gene, known for its role in various physiological processes, including sexual maturation, is hypothesized to play a significant role in the sex differentiation of Dabry’s sturgeon. This study cloned the full-length cds sequence of the foxl2 gene and analyzed its expression across various tissues, focusing on its response to estradiol treatment. Our findings indicate that foxl2 is predominantly expressed in ovaries and shows a dose-dependent response to estradiol, suggesting its potential role in ovarian differentiation. This research underscores the importance of foxl2 in understanding reproductive biology and offers a foundation for future conservation strategies.

The composites of Mg20Pr1Sm3Y1Ni10 as-quenched alloy and 3 wt.% M (M = CoS, CoS2, MoS2) catalyst were prepared by high-speed vibration ball mill. The effects of metal sulfides on the hydrogenation and dehydrogenation dynamics of alloys were compared. The results show that the as-milled composites contain a large number of amorphous embedded by a small amount of nanocrystals, and there are many point defects. After ball milling, the crystal grain size in the composites containing CoS is relatively larger, followed by CoS2 and MoS2 again. After hydrogenation, the amorphous phase is crystallized to form Mg2NiH4, YH3, Pr8H18.96, Sm3H7, Mg, Co or Mo phases, however, Mg2Ni, YH2, PrH2 and Ni3Y phases appeared after dehydrogenation. The maximum hydrogenation capacity of the composites containing CoS, CoS2 and MoS2 are 3.939, 4.265 and 4.507 wt.%, respectively. The hydrogenation saturation ratio of composite containing MoS2 is higher than that of the composites containing CoS and CoS2. The dehydrogenation activation energy of the composites containing CoS, CoS2 and MoS2 is 107.76, 68.43 and 63.28 kJ.mol−1.H2. On the improvement of hydrogen storage performance of Mg20Pr1Sm3Y1Ni10 alloy, the catalytic effect of MoS2 sulfide is better than that of CoS2 sulfide, and which is better than CoS sulfide.

Abstract For decades, studies on learner emotions in foreign language learning have been predominantly focused on foreign language anxiety, and recent years have witnessed a surge in the exploration of other positive and negative emotions such as enjoyment and boredom. The current study aimed to extend this line of inquiry and explored the bidirectional relations between foreign language achievements and learner emotions through a questionnaire study among English learners (n = 756) in Chinese senior secondary schools, an understudied population in foreign language emotion research. Results indicated that the three emotions were significantly related to each other. Further analyses suggested that anxiety and boredom were significant predictors of achievements, and that language achievements predicated all the three emotions. The study confirms the reciprocal relationships between language achievements and emotions and provides pedagogical implications for language teaching.

Abstract Antigen-specific T cell receptor-engineered T cell (TCR-T) based immunotherapy has proven to be an effective method to combat cancer. In recent years, cross-talk between the innate and adaptive immune systems may be requisite to optimize sustained antigen-specific immunity, and the stimulator of interferon genes (STING) is a promising therapeutic target for cancer immunotherapy. The level of expression or presentation of antigen in tumor cells affects the recognition and killing of tumor cells by TCR-T. This study aimed at investigating the potential of innate immune stimulation of T cells and engineered T cells to enhance immunotherapy for low-expression antigen cancer cells. We systematically investigated the function and mechanism of cross-talk between STING agonist diABZI and adaptive immune systems. We established NY-ESO-1 full knockout Mel526 cells for this research and found that diABZI activated STING media and TCR signaling pathways. In addition, the results of flow cytometry showed that antigens presentation from cancer cells induced by STING agonist diABZI also improved the affinity of TCR-T cells function against tumor cells in vitro and in vivo. Our findings revealed that diABZI enhanced the immunotherapy efficacy of TCR-T by activating STING media and TCR signaling pathways, improving interferon-γ expression, and increasing antigens presentation of tumor cells. This indicates that STING agonist could be used as a strategy to promote TCR-T cancer immunotherapy.

Abstract Background Dendrobium spp. comprise a group of tropical orchids with ornamental and medicinal value. Dendrobium spp. are sensitive to low temperature, and the underlying cold response regulatory mechanisms in this group are unclear. To understand how these plants respond to cold stress, we compared the transcriptomic responses of the cold-tolerant cultivar ‘Hongxing’ (HX) and the cold-sensitive cultivar ‘Sonia Hiasakul’ (SH) to cold stress. Results Chemometric results showed that the physiological response of SH in the later stages of cold stress is similar to that of HX throughout the cold treatment. Orthogonal partial least squares discriminant analysis (OPLS–DA) revealed that soluble protein content and peroxidase activity are key physiological parameters for assessing the cold tolerance of these two Dendrobium spp. cultivars. Additionally, weighted gene co-expression network analysis (WGCNA) results showed that many cold response genes and metabolic pathways significantly associated with the physiological indices were enriched in the 12 detected modules. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses of the 105 hub genes showed that Dendrobium spp. adapt to cold stress by regulating signal transduction, phytohormones, transcription factors, protein translation and modification, functional proteins, biosynthesis and metabolism, cell structure, light, and the circadian clock. Hub genes of the cold stress response network included the remorin gene pp34, the abscisic acid signaling pathway-related genes PROTEIN PHOSPATASE 2 C (PP2C), SNF1-RELATED PROTEIN KINASE 2 (SnRK2), ABRE-BINDING FACTOR 1 (ABF1) and SKI-INTERACTING PROTEIN 17 (SKIP17), the Ca2+ signaling-related GTP diphosphokinase gene CRSH1, the carbohydrate-related gene STARCH SYNTHASE 2 (SS2), the cell wall biosynthesis gene CINNAMYL ALCOHOL DEHYDROGENASE (CAD7), and the endocytosis-related gene VACUOLAR PROTEIN SORTING-ASSOCIATED PROTEIN 52 A (VPS52A). Conclusions The cold-responsive genes and metabolic pathways of Dendrobium spp. revealed in this study provide important insight to enable the genetic enhancement of cold tolerance in Dendrobium spp., and to facilitate cold tolerance breeding in related plants.

BackgroundWith the aging of China's population, cognitive impairment in the elderly is receiving increasing public attention. Screening and intervention of people with mild cognitive impairment (MCI) are of great significance to prevent and reduce the occurrence of cognitive impairment.ObjectiveTo understand the prevalence and explore potential influencing factors of MCI in the elderly in Songjiang District, Shanghai, and to provide scientific basis for promoting early screening of cognitive impairment and precise intervention of MCI in the elderly in this area. MethodsA cross-sectional study design was adopted. From August to October 2022, using multi-stage random sampling, 1800 elderly residents aged 60 years and above were screened for cognitive impairment in 6 neighborhood/village committees in 6 towns in Songjiang District. The survey questionnaires included a sociodemographic questionnaire, a health status and lifestyle questionnaire, the Instrumental Activities of Daily Living (IADL), the Patient Health Questionnaire (PHQ-9), and the Mini-Mental State Examination (MMSE). Prevalence rates of MCI among the elderly by selected social demographic characteristics, health status, and lifestyle were estimated, and potential influencing factors of MCI were evaluated by binary logistic regression. ResultsA total of 209 elderly residents with MCI and 1591 healthy elderly residents were detected, and the prevalence of MCI in the elderly aged 60 and above was 11.6% in Songjiang District. Being physically active (OR=0.556, 95%CI: 0.399, 0.774) reduced the risk of MCI. Illiteracy (OR=1.810, 95%CI: 1.239, 2.644), primary school education level (OR=3.454, 95%CI: 2.342, 5.092), non-participation in social activities (OR=1.945, 95%CI: 1.360, 2.781), IADL damaged (OR=3.173, 95%CI: 2.137, 4.712), and depression (OR=1.957, 95%CI: 1.112, 3.443) increased the risk of MCI (P＜0.05). ConclusionThe prevalence of MCI among the elderly in Songjiang District is lower than the national average. Educational level, physical activity, participation in social activities, IADL, and depression may be the influencing factors of MCI in the elderly. It is recommended to carry out early screening, early detection, and early intervention for cognitive impairment in the elderly. Improving involvement in physical exercise and increasing participation in social activities are encouraged. Special attention should be paid to the needs of vulnerable groups such as low education level and disabled elderly during a community MCI intervention program.

Abstract Background Oral microbial dysbiosis contributes to the development of oral squamous cell carcinoma (OSCC). Our previous study showed that Prevotella intermedia (P. intermedia) were enriched in the oral mucosal surface, plaque, and saliva of patients with OSCC. Intratumoral microbiome could reshape the immune system and influence the development of various tumors. However, the invasion status of human OSCC tissues by P. intermedia and the pathway through which intratumoral P. intermedia potentiates tumor progression remain unexplored. Methods P. intermedia in human OSCC or normal tissues was detected by FISH. A mouse OSCC cell line SCC7 was adopted to investigate the effects of heat-killed P. intermedia treatment on cell proliferation, invasion, and cytokine release by using CCK-8 assay, transwell invasion assay and ELISA. Moreover, we established a mouse transplanted tumor model by using SCC7 cells, injected heat-killed P. intermedia into tumor tissues, and investigated the effects of heat-killed P. intermedia on tumor growth, invasion, cytokine levels, immune cell infiltrations, and expression levels by using gross observation, H&E staining, ELISA, immunohistochemistry, mRNA sequencing, and transcriptomic analysis. Results Our results indicated that P. intermedia were abundant in OSCC and surrounding muscle tissues. Heat-killed P. intermedia promoted SCC7 cell proliferation, invasion and proinflammatory cytokine secretions, accelerated transplanted tumor growth in mice, exacerbate muscle and perineural invasion of OSCC, elevated the serum levels of IL-17A, IL-6, TNF-α, IFN-γ, and PD-L1, induced Treg cells M2 type macrophages in mouse transplanted tumors. The data of transcriptomic analysis revealed that heat-killed P. intermedia increased the expression levels of inflammatory cytokines and chemokines while reduced the expression levels of some tumor suppressor genes in mouse transplanted tumors. Additionally, IL-17 signaling pathway was upregulated whereas GABAergic system was downregulated by heat-killed P. intermedia treatment. Conclusions Taken together, our results suggest that P. intermedia could inhibit the expression of tumor suppressors, alter the tumor microenvironment, and promote the progression of OSCC.

Peanut (Arachis hypogaea L.) is one of the most important oil crops worldwide. Cadmium (Cd), a heavy metal that is nonessential and toxic, has the potential to significantly impacted the quality and safety of peanut. Despite the known importance of Pleiotropic Drug Resistance (PDR) genes in heavy metal accumulation and transport in plants, there is a lack of comprehensive research on the systematic identification and functional characterization of AhPDRs in peanut. In this study, a total of 38 AhPDR genes were discovered within the peanut genome. Among these, AhPDR24, AhPDR30, and AhPDR33 displayed notable variations in expression levels in response to Cd stress. Particularly noteworthy was the observation that AhPDR33, localized in the plasma membrane, exhibited a significant increase in expression (approximately 3.8-fold) and heightened promoter activity (approximately 4.1-fold) following exposure to Cd (75 μM CdCl2). Furthermore, the study found that the overexpression of AhPDR33 in Arabidopsis resulted in increased root elongation and decreased Cd accumulation (approximately 0.42-fold) compared to wild-type plants. This suggests that AhPDR33 may have a beneficial role in facilitating Cd efflux and tolerance in plants. Additionally, transient silencing of AhPDR33 in peanut demonstrated its positive regulation of Cd tolerance through the promotion of reactive oxygen species (ROS) scavenging and membrane permeability reduction. These findings contribute to the understanding of the molecular mechanisms involved in AhPDR33-mediated Cd tolerance and detoxification in peanut. Furthermore, this study provides comprehensive information to understand the AhPDR gene family, its features, and its expression, which will hold a promising utility as an excellent candidate in the genetic improvement of peanut Cd stress tolerance.

ObjectiveSymptomatic carotid artery disease is indicative of an elevated likelihood of experiencing a subsequent stroke, with the morphology of plaque and its specific features being closely linked to the risk of stroke occurrence. Our study based on the characteristics of carotid plaque assessed by optical coherence tomography (OCT), the plaque morphology evaluated by digital subtraction angiography (DSA) and clinical laboratory indicators were combined, develop a combined predictive model to identify symptomatic carotid plaque.MethodsPatients diagnosed with carotid atherosclerotic stenosis who underwent whole-brain DSA and OCT examination at the Affiliated Hospital of Jining Medical University from January 2021 to November 2023 were evaluated. Clinical features, as well as DSA and OCT plaque characteristics, were analyzed for differences between symptomatic and asymptomatic cohorts. An analysis of logistic regression was carried out to identify factors associated with the presence of symptomatic carotid plaque. A multivariate binary logistic regression equation was established with the odds ratio (OR) serving as the risk assessment parameter. The receiver operating characteristic curve was utilized to assess the combined predictive model and independent influencing factors.ResultsA total of 52 patients were included in the study (symptomatic: 44.2%, asymptomatic: 55.8%). Symptomatic carotid stenosis was significantly linked to four main factors: low-density lipoprotein-cholesterol >3.36 mmol/L [OR, 6.400; 95% confidence interval (CI), 1.067–38.402; p = 0.042], irregular plaque (OR, 6.054; 95% CI, 1.016–36.083; p = 0.048), ruptured plaque (OR, 6.077; 95% CI, 1.046–35.298; p = 0.048), and thrombus (OR, 6.773; 95% CI, 1.194–38.433; p = 0.044). The combined predictive model generated using four indicators showed good discrimination (Area Under Curve, 0.924; 95% CI, 0.815–0. 979). The p value was 3.36 mmol/L, irregular plaque, ruptured plaque, and thrombus could help in the detection of symptomatic carotid plaque. Further research conducted on additional independent cohorts is necessary to confirm the clinical significance of the predictive model for symptomatic carotid plaque.

IntroductionAutism spectrum disorders (ASD) are a set of heterogeneous neurodevelopmental disorders characterized by impaired social interactions and stereotypic behaviors. Current clinical care is palliative at the most and there remains huge unmet medical need to fully address the core symptoms of ASD. Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) are emerging as a promising candidate for ASD treatment, but the precise mechanism remains controversial.MethodsIn vitro studies we performed the transwell migration assay to explore the interaction between hUC-MSCs and the primary-cultured cortical neurons. Then we determined the therapeutic effects of intravenous administration of hUC-MSCs in rats challenged with valproic acid (VPA) during gestation, a well-defined rat model of autism.ResultsOur studies showed that hUC-MSCs promoted the growth of primary-cultured cortical neurons. Furthermore, our results demonstrated that hUC-MSCs significantly alleviated microglial activation in the brain, especially in the anterior cingulate cortex, and effectively improved the sociability of the VPA-exposed rats.DiscussionThese results offer valuable insights for clinical translation and further research on the mechanisms of hUC-MSCs in psychiatric disorders characterized by microglial activation, particularly in cases of autism, shall be warranted.

Background Musculoskeletal (MSK) disorders, affecting billions of people worldwide, pose significant challenges to the healthcare system and require effective management models. The rapid development of digital healthcare technologies (DHTs) has revolutionized the healthcare industry. DHT-based interventions have shown promising clinical benefits in managing MSK disorders, alleviating pain, and improving functional impairment. There is, however, no bibliometric analysis of the overall trends on this topic. Methods We extracted all relevant publications from the Web of Science Core Collection (WoSCC) database until April 30, 2023. We performed bibliometric analysis and visualization using CiteSpace, VOSviewer, and R software. Annual trends of publications, countries/regions distributions, funding agencies, institutions, co-cited journals, author contributions, references, core journals, and keywords and research hotspots were analyzed. Results A total of 6810 papers were enrolled in this study. Publications have increased drastically from 16 in 1995 to 1198 in 2022, with 4067 articles published in the last five years. In all, 53 countries contributed with publications to this research area. The United States, the United Kingdom, and China were the most productive countries. Harvard University was the most contributing institution. Regarding keywords, research focuses include artificial intelligence, deep learning, machine learning, telemedicine, rehabilitation, and robotics. Conclusion The COVID-19 pandemic has further accelerated the adoption of DHTs, highlighting the need for remote care options. The analysis reveals the positive impact of DHTs on improving physician productivity, enhancing patient care and quality of life, reducing healthcare expenditures, and predicting outcomes. DHTs are a hot topic of research not only in the clinical field but also in the multidisciplinary intersection of rehabilitation, nursing, education, social and economic fields. The analysis identifies four promising hotspots in the integration of DHTs in MSK pain management, biomechanics assessment, MSK diagnosis and prediction, and robotics and tele-rehabilitation in arthroplasty care.

Marine heatwaves (MHWs) are anomalously warm events that profoundly affect climate change and local ecosystem. During the summer of 2012 (June–September), intense MHWs occurred in the tropical Indian Ocean (TIO) concurrently with an unseasonable positive Indian Ocean Dipole (pIOD) event. The MHW metrics (duration, frequency, cumulative intensity and maximum intensity) were characterized by northwestward–slanted patterns from west Australia to the Somalia coast. The analysis confirmed that these MHWs were closely associated with the unseasonable pIOD 2012. The weakening of Western North Pacific Subtropical High and strengthening of Australian High in spring induced an interhemispheric pressure gradient that drove two anticyclonic circulation patterns over the eastern TIO. The first anticyclonic circulation featured cross–equatorial wind anomalies from south of Java to the South China Sea/Philippine Sea, which led to strong upwelling off Sumatra–Java during the subsequent summer. The second anticyclonic circulation excited downwelling Rossby waves that propagated from the southeastern TIO to the western TIO. Thus, downwelling in the western pole and upwelling in the eastern pole led to a strong pIOD event peaking in summer, namely, the unseasonable pIOD 2012. These downwelling Rossby waves deepened the thermocline by more than 60 m and caused anomalous surface warming, thereby contributing to the occurrences of MHWs. With the development and peak of the unseasonable pIOD 2012, anomalous atmospheric circulation transported moisture from the TIO to the subtropical Western North Pacific (WNP), favoring a strong cyclonic anomaly that profoundly affected the summer monsoon rainfall over the subtropical WNP. This study provides some perspectives on the role of pIOD events in summer climate over the Indo–Northwest Pacific region.

Key Clinical Message Ultrasound‐guided core needle biopsy combined with immunohistochemistry and molecular testing could improve the diagnostic accuracy of bone metastases from follicular thyroid carcinoma, help to predict distant metastasis and prognosis. Abstract Metastatic thyroid follicular carcinoma presenting initially with bone lesion is uncommon, its prime symptom is gradual onset, localized pain. Patient with bone metastasis who were diagnosed before thyroidectomy had a higher rate of mortality, clinician should be cautious in eliciting the clinical history and this insidious symptom in middle age group, carry out further examination. We are presenting two case reports of a follicular thyroid carcinoma with bone metastasis, ultrasound‐guided core needle biopsy combined with immunohistochemistry (IHC) were carried out by our clinical team to determine the source and nature of the tumor, relevant literature was reviewed, molecular testing was discussed, we believe core needle biopsy combined with IHC and molecular testing improve the diagnostic accuracy of bone metastases from follicular thyroid carcinoma.

Fusion of point cloud from different platforms is crucial for enhancing spatial information completeness in large-scale scenes, particularly in urban 3D modeling. To address redundancy, noise, and accuracy degradation in direct registration of point cloud across platforms, we propose an adaptive fusion method utilizing supervoxels. Initially, a high-precision point cloud is selected as the reference point cloud (RPC),and we apply a coarse-to-fine registration approach to unify the RPC and the target point cloud (TPC). Registration parameters are optimized using Improved Particle Swarm Optimization (IPSO), enhancing automation and precision of the fine registration. Subsequently, supervoxels are constructed for the registered RPC and TPC. Finally, within each corresponding supervoxel, redundancy and noise are eliminated by applying alpha-shape and Laplacian, considering the data quality and density distribution of the RPC. Experimental validation was conducted with data acquired from three distinct platforms. The proposed method significantly enhances registration precision. Compared to RANSAC-ICP, our method reduced average RMSE by 36.35%, average MAE by 34.85%, and average Frobenius Norm by 84.48% across three experimental groups. The proposed fusion method improves data completeness, reducing the point cloud count by about 30% compared to direct registration. Moreover, it effectively preserves the detailed features of the fused point cloud, serving as accurate data sources for constructing 3D urban models.

Abstract Background Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting. Methods We constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People’s Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets. Results The results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process. Conclusions Our diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.

Abstract Background Esophageal cancer (EC) is a global canker notorious for causing high mortality due to its relentless incidence rate, convoluted with unyielding recurrence and metastasis. However, these intricacies of EC are associated with an immoderate expression of NY-ESO-1 antigen, presenting a lifeline for adoptive T cell therapy. We hypothesized that naturally isolated higher-affinity T cell receptors (TCRs) that bind to NY-ESO-1 would allow T lymphocytes to target EC with a pronounced antitumor response efficacy. Also, targeting TRPV2, which is associated with tumorigenesis in EC, creates an avenue for dual-targeted therapy. We exploited the dual-targeting antitumor efficacy against EC. Methods We isolated antigen-specific TCRs (asTCRs) from a naive library constructed with TCRs obtained from enriched cytotoxic T lymphocytes. The robustness of our asTCRs and their TCR-T cell derivatives, Tranilast (TRPV2 inhibitor), and their bivalent treatment were evaluated with prospective cross-reactive human-peptide variants and tumor cells. Results Our study demonstrated that our naive unenhanced asTCRs and their TCR-Ts perpetuated their cognate HLA-A*02:01/NY-ESO-1(157–165) specificity, killing varying EC cells with higher cytotoxicity compared to the known affinity-enhanced TCR (TCRe) and its wild-type (TCR0) which targets the same NY-ESO-1 antigen. Furthermore, the TCR-Ts and Tranilast bivalent treatment showed superior EC killing compared to any of their monovalent treatments of either TCR-T or Tranilast. Conclusion Our findings suggest that dual-targeted immunotherapy may have a superior antitumor effect. Our study presents a technique to evolve novel, robust, timely therapeutic strategies and interventions for EC and other malignancies.

In order to better test the virtual training system for automotive safety driving, ensuring that the system can efficiently and comprehensively cover all application scenarios, this paper defines the syntax and semantics of formal graphical symbols for testing. Based on this, a method for designing test cases for the virtual training system for automotive safety driving using formal activity diagrams is proposed. The example demonstrates that after using this method for test case design, the designed test cases can efficiently test all necessary application scenarios and eliminate invalid scenarios. This method effectively addresses the issues of strong randomness and low testing efficiency in system testing.

Blind deblurring of optical remote sensing images has been a longstanding challenge. In recent years, many learning-based deblurring algorithms have been greatly developed. However, these methods often suffer from losing image texture details and some artificial artifacts under low signal-to-noise ratio (SNR) conditions. To tackle this challenge, we introduce an innovative end-to-end noise-robust blind deblurring algorithm based on the diffusion model joined with a denoising module and a wavelet-enhanced conditional embedding mechanism. Experiments have verified the effectiveness of our method. Compared to the image blind deblurring algorithms based on the diffusion model, the proposed algorithm demonstrates better performance in terms of quantitative metrics peak signal-to-noise ratio and structural similarity index. Compared to all the comparative algorithms, the proposed algorithm exhibits significant advantages in quantitative metrics learned perceptual image patch similarity, Fréchet inception distance, and natural image quality evaluator and shows superior visual effects in restoring texture details in the restored images, especially in challenging low SNR conditions.

Pulse signals are often corrupted by noise, compromising signal integrity for downstream analysis. This paper presents an automated denoising technique for pulse waveforms using ensemble empirical mode decomposition (EEMD). The EEMD algorithm decomposes the signal into intrinsic mode functions (IMFs). Statistical metrics of IMF energy and entropy identify noise components for targeted removal via nonlinear filtering. Experiments on simulated pulse echoes demonstrated the approach of accurately eliminated noise regions. Compared to wavelet decomposition and Monte Carlo methods, the EEMD technique exhibited superior noise reduction and over 90% faster processing. This ensemble empirical mode decomposition approach provides an efficient, data-driven methodology for denoising pulse waveforms with applications in biomedical signal analysis.

Abstract In this work, a new type of the unique continuation property for time-fractional diffusion equations is studied. The proof is mainly based on the Laplace transform and the properties of Bessel functions. As an application, the uniqueness of the inverse problem in the simultaneous determination of spatially dependent source terms and fractional order from sparse boundary observation data is established.

The Ludwieg tube wind tunnel serves as a crucial testing ground for fundamental studies of hypersonic aerodynamics. However, the quick-opening valve-started hypersonic Ludwieg tube wind tunnel has been affected by the quick-opening valve for a long time, resulting in different types of flow disturbance modes. The double-throat aerodynamic configuration can effectively eliminate the disturbance source of the upstream components of the quick-opening valve-started hypersonic Ludwieg tube wind tunnel, but it will lead to a significant reduction in the effective running time of the wind tunnel. In order to solve this problem, the unsteady numerical simulation was used to study the start-up characteristics of the hypersonic Ludwieg tube wind tunnel with double-throat aerodynamic configuration. Then, a fusion design was carried out for the first nozzle expansion section and the stable section. The effects of different combinations of expansion angles and stable sections on the start-up time and flow field quality of the wind tunnel were studied. The results show that the effective running time of the hypersonic Ludwieg tube wind tunnel with double-throat aerodynamic configuration can be increased by nearly 20% by using the combined design of reducing the expansion angle, and the static flow field quality in the downstream experimental section is almost unaffected, which effectively improves the experimental ability of the wind tunnel. At the same time, compared with the larger expansion angle combination, the smaller expansion angle design can reduce the total pressure loss by about 10%.

The dynamic fracture behavior of mass concrete is crucial to the dynamic analysis and safety evaluation of concrete dams subjected to strong earthquake shocks in the framework of fracture mechanics. In the presented research, cylindrical specimens with a ring of preset cracks were cast by three-graded mass concrete, and direct tension tests were performed with two loading rates considered, i.e., 10−6/s for quasi-static loading and 10−3/s for dynamic loading. The load–crack mouth opening displacement (P-CMOD) curves were obtained, from which the fracture toughness, fracture energy, and characteristic length of the mass concrete were obtained. In this process, the influence of the eccentricity in the tests was compensated by the numerical modeling of the tests. Next, the crack propagation process of the mass concrete was modeled using the extended finite element method. From the test results, it is found that, under quasi-static loading, the crack generally propagates along the interface between the aggregates and the matrix, while, under dynamic loading, more aggregates are fractured. As compared to the case of quasi-static loading, the energy absorption capacity, fracture energy, and fracture toughness increase for dynamic loading, while the characteristic length decreases. Moreover, the numerically predicted P-CMOD curves agree reasonably well with the experimental measurements.

Fungal secondary metabolites (SMs) represent an invaluable source of therapeutic drugs. Genomics-based approaches to SM discovery have revealed a vast and largely untapped biosynthetic potential within fungal genomes. Here, we used the publicly available fungal genome sequences from the NCBI public database, as well as tools such as antiSMASH, BIG-SLiCE, etc., to analyze a total of 11,598 fungal genomes, identifying 293,926 biosynthetic gene clusters (BGCs), which were subsequently categorized into 26,825 gene cluster families (GCFs). It was discovered that only a tiny fraction, less than 1%, of these GCFs could be mapped to known natural products (NPs). Some GCFs that only contain a single BGC internally are crucial for the biodiversity of fungal biosynthesis. Evident patterns emerged from our analysis, revealing popular taxa as prominent sources of both actual and potential biosynthetic diversity. Our study also suggests that the genus rank distribution of GCF is generally consistent with NP diversity. It is noteworthy that genera Xylaria, Hypoxylon, Colletotrichum, Diaporthe, Nemania, and Calonectria appear to possess a higher potential for SM synthesis. In addition, 7213 BGCs match possible known compound structures, and homologous gene clusters of well-known drugs can be located in different genera, facilitating the development of derivatives that share structural similarity to these drugs and may potentially possess similar biological activity. Our study demonstrated the various types of fungi with mining potential, assisting researchers in prioritizing their research efforts and avoiding duplicate mining of known resources to further explore fungal NP producers.

Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants. Previous studies have applied deep learning models to learn the brain structural connectome (SC) to predict neurodevelopmental deficits in the preterm population. However, none of these models are specifically designed for graph-structured data, and thus may potentially miss certain topological information conveyed in the brain SC. In this study, we aim to develop deep learning models to learn the SC acquired at term-equivalent age for early prediction of neurodevelopmental deficits at 2 years corrected age in VPT infants. We directly treated the brain SC as a graph, and applied graph convolutional network (GCN) models to capture complex topological information of the SC. In addition, we applied the supervised contrastive learning (SCL) technique to mitigate the effects of the data scarcity problem, and enable robust training of GCN models. We hypothesize that SCL will enhance GCN models for early prediction of neurodevelopmental deficits in VPT infants using the SC. We used a regional prospective cohort of ∼280 VPT infants who underwent MRI examinations at term-equivalent age from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS). These VPT infants completed neurodevelopmental assessment at 2 years corrected age to evaluate cognition, language, and motor skills. Using the SCL technique, the GCN model achieved mean areas under the receiver operating characteristic curve (AUCs) in the range of 0.72∼0.75 for predicting three neurodevelopmental deficits, outperforming several competing models. Our results support our hypothesis that the SCL technique is able to enhance the GCN model in our prediction tasks.

Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

Hyperspectral target detection (HTD) aims to identifying targets within a hyperspectral image (HSI) based on provided target spectra. In the current HTD field, representation-based detectors have attracted much attention. However, there are two prominent challenges that are particularly noteworthy. First, the background class encompasses diverse land covers, making its accurate representation challenging. Second, the detection ability can be significantly influenced by the abnormalities and noise in HSI. To tackle these concerns, we propose a novel background covariance discriminative dictionary learning (BCDDL) model for HTD. To enhance the background representation ability and overcome the sparse noise, we combine the dictionary learning with spectral covariance descriptors and undertake background reconstruction in regional scale. Specifically, the input HSI is pre-processed into superpixels, the spectral covariance of each superpixel is used to provide a compact and flexible description of local regional statistical properties. Further, a novel spatial clustering-based dictionary learning method is proposed to learn the background discriminative covariances dictionary. The collaborative representation model within symmetric positive definite (SPD) manifold is utilized to reconstruct background region and get the residual. By merging the background residual with pixel-wise target reconstruction residual, we derive final detection output. Comprehensive experiments on two public hyperspectral datasets and two novel GaoFen-5 datasets demonstrate the superiority of our BCDDL approach over 10 state-of-the-art methods, especially in terms of suppressing background.

Abstract Implementing arbitrary single-qubit gates with near perfect fidelity is among the most fundamental requirements in gate-based quantum information processing. In this work, we fabricate a transmon qubit with long coherence times and demonstrate single-qubit gates with the average gate error below 10−4, i.e. (7.42 ± 0.04) × 10−5 by randomized benchmarking (RB). To understand the error sources, we experimentally obtain an error budget, consisting of the decoherence errors lower bounded by (4.62 ± 0.04) × 10−5 and the leakage rate per gate of (1.16 ± 0.04) × 10−5. Moreover, we reconstruct the process matrices for the single-qubit gates by the gate set tomography (GST), with which we simulate RB sequences and obtain single-qubit fidelities consistent with experimental results. We also observe non-Markovian behavior in the experiment of long-sequence GST, which may provide guidance for further calibration. The demonstration extends the upper limit that the average fidelity of single-qubit gates can reach in a transmon-qubit system, and thus can be an essential step towards practical and reliable quantum computation in the near future.

Abstract Background Disulfidptosis and the disulfidptosis-related gene SLC7A11 have recently attracted significant attention for their role in tumorigenesis and tumour management. However, its association with adrenocortical carcinoma (ACC) is rarely discussed. Methods Differential analysis, Cox regression analysis, and survival analysis were used to screen for the hub gene SLC7A11 in the TCGA and GTEx databases and disulfidptosis-related gene sets. Then, we performed an association analysis between SLC7A11 and clinically relevant factors in ACC patients. Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic value of SLC7A11 and clinically relevant factors. Weighted gene coexpression analysis was used to find genes associated with SLC7A11. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and the LinkedOmics database were used to analyse the functions of SLC7A11-associated genes. The CIBERSORT and Xcell algorithms were used to analyse the relationship between SLC7A11 and immune cell infiltration in ACC. The TISIDB database was applied to search for the correlation between SLC7A11 expression and immune chemokines. In addition, we performed a correlation analysis for SLC7A11 expression and tumour mutational burden and immune checkpoint-related genes and assessed drug sensitivity based on SLC7A11 expression. Immunohistochemistry and RT‒qPCR were used to validate the upregulation of SLC7A11 in the ACC. Results SLC7A11 is highly expressed in multiple urological tumours, including ACC. SLC7A11 expression is strongly associated with clinically relevant factors (M-stage and MYL6 expression) in ACC. SLC7A11 and the constructed nomogram can accurately predict ACC patient outcomes. The functions of SLC7A11 and its closely related genes are tightly associated with the occurrence of disulfidptosis in ACC. SLC7A11 expression was tightly associated with various immune cell infiltration disorders in the ACC tumour microenvironment (TME). It was positively correlated with the expression of immune chemokines (CXCL8, CXCL3, and CCL20) and negatively correlated with the expression of immune chemokines (CXCL17 and CCL14). SLC7A11 expression was positively associated with the expression of immune checkpoint genes (NRP1, TNFSF4, TNFRSF9, and CD276) and tumour mutation burden. The expression level of SLC7A11 in ACC patients is closely associated withcthe drug sensitivity. Conclusion In ACC, high expression of SLC7A11 is associated with migration, invasion, drug sensitivity, immune infiltration disorders, and poor prognosis, and its induction of disulfidptosis is a promising target for the treatment of ACC.

Abstract We aimed to investigate acute changes before and after low-intensity continuous and intermittent blood flow restriction (BFR) deep-squat training on thigh muscle activation characteristics and fatigue level under suitable individual arterial occlusion pressure (AOP). Twelve elite male handball players were recruited. Continuous (Program 1) and intermittent (Program 2) BFR deep-squat training was performed with 30% one-repetition maximum load. Program 1 did not include decompression during the intervals, while Program 2 contained decompression during each interval. Electromyography (EMG) was performed before and after two BFR training programs in each period. EMG signals of the quadriceps femoris, posterior femoral muscles, and gluteus maximus, including the root mean square (RMS) and normalized RMS and median frequency (MF) values of each muscle group under maximum voluntary contraction (MVC), before and after training were calculated. The RMS value under MVC (RMSMVC) of the rectus femoris (RF), vastus medialis (VM), vastus lateralis (VL), and gluteus maximus (GM) decreased after continuous and intermittent BFR training programs, and those of the biceps femoris (BF) and semitendinosus (SEM) increased; The RMS standard values of the VL, BF, and SEM were significantly increased after continuous and intermittent BFR training (P

Abstract Background Kidney organoids derived from human pluripotent stem cells (HiPSCs) hold huge applications for drug screening, disease modeling, and cell transplanting therapy. However, these applications are limited since kidney organoid cannot maintain complete morphology and function like human kidney. Kidney organoids are not well differentiated since the core of the organoid lacked oxygen, nutrition, and vasculature, which creates essential niches. Hypoxia-inducible factor-1 α (HIF-1α) serves as a critical regulator in vascularization and cell survival under hypoxia environment. Less is known about the role of HIF-1α in kidney organoids in this regard. This study tried to investigate the effect of HIF-1α in kidney organoid vascularization and related disease modeling. Methods For the vascularization study, kidney organoids were generated from human induced pluripotent stem cells. We overexpressed HIF-1α via plasmid transfection or treated DMOG (Dimethyloxallyl Glycine, an agent for HIF-1α stabilization and accumulation) in kidney progenitor cells to detect the endothelium. For the disease modeling study, we treated kidney organoid with cisplatin under hypoxia environment, with additional HIF-1α transfection. Result HIF-1α overexpression elicited kidney organoid vascularization. The endothelial cells and angiotool analysis parameters were increased in HIF-1α plasmid-transfected and DMOG-treated organoids. These angiogenesis processes were partially blocked by VEGFR inhibitors, semaxanib or axitinib. Cisplatin-induced kidney injury (Cleaved caspase 3) was protected by HIF-1α through the upregulation of CD31 and SOD2. Conclusion We demonstrated that HIF-1α elicited the process of kidney organoid vascularization and protected against cisplatin-induced kidney organoid injury in hypoxia environment.

To regulate the transactional activities on the public blockchain involving account balance models, it is necessary to conduct research on address classification for transactions on such blockchains.A blockchain address classification method, named AJKGS-ABCM (attention jumping knowledge graph SAGE account-based blockchain classification model), was proposed to categorize blockchain addresses, providing effective support for blockchain transaction tracking.Blockchain transaction data was represented as a graph structure, with addressed as nodes and transactions as edges.The AJK-GraphSAGE algorithm was introduced to learn embedded representations of the graph, where the model’s input required only nodes and their sampled neighboring node sets.Simultaneously, attention mechanisms and skip-connection knowledge integration strategies were incorporated into the model, allowing for adaptive weight allocation across different layers and information sharing between various levels, thereby enhancing training speed and generalization capabilities.Finally, experimental comparisons are conducted, demonstrating superior performance in terms of accuracy, recall, and F1 score compared to other methods.

The goal of carbon emission peak and carbon neutrality requires China to vigorously develop renewable energy. However, renewable energy has obvious randomness and volatility. Therefore, it is necessary to configure energy storage systems for renewable energy stations to ensure the safe and stable operation of power systems. Given the problem of energy storage system configuration in renewable energy stations, it is necessary to consider the system load characteristics and design appropriate principles to formulate planned output curves for renewable energy stations, so that the joint output curve of renewable energy and energy storage system can match the system load curve as much as possible, which will reduce the system peak shaving pressure and promote the utilization of renewable energy. This paper first considers the impact of renewable energy stations with the different installed scales on the power system and designs the standardized supply curves differentially, and defines the supply curve deviation index to characterize the difference between the renewable energy–energy storage system joint output curve and the standardized supply curve. Then, to minimize energy storage system investment costs and supply deviation costs, an optimization model for energy storage system configuration in renewable energy stations is established, and output deviation control constraints are set to ensure that the operation of energy storage systems conforms to actual conditions. Finally, case studies analyze the energy storage system configuration results and the typical scenario operation results of a single renewable energy station and a renewable energy power generation base, which verify the rationality and effectiveness of the method proposed in this paper.

In recent years, renewable energy (RE) has developed rapidly due to its environmentally friendly characteristics. However, RE has strong intermittency and volatility, and a high proportion of RE integration will have a huge impact on power systems. Moreover, some power systems also put forward requirements for RE utilization, which requires more flexible resources. Conventional units are limited in development under the carbon emission reduction policies. The energy storage system (ESS) can stabilize the volatility of RE power and alleviate transmission congestion. Therefore, to promote the energy transformation of power systems, it is necessary to jointly consider transmission network, ESS, and RE in power system planning. Considering that the planning decision-makers are different, this paper proposes a tri-level expansion planning model considering carbon emission limitation and RE development. In the upper-level (UL) problem, the transmission system operator constructs transmission lines and grid-side ESS to ensure load supply and carbon emission requirements. On the premise that the RE curtailment rate is within a certain range, the middle-level (ML) problem constructs RE units to maximize the RE installed capacity. Based on the construction results of the UL and ML problems, lower-level (LL) problems simulate economic dispatching from the perspective of the independent system operator. To solve the tri-level model, an equivalent bi-level model is transformed according to strong duality theory, and then the column and constraint generation (C&CG) algorithm is adopted to solve the bi-level problem. In case studies, the numerical results analyze the impacts of carbon emissions, RE curtailment rate, and ESS parameters on the planning results, which verifies the rationality of the approach proposed in this paper.

The co-planning problem of transmission and energy storage system (ESS) requires a large amount of historical and forecasted input data to account for the volatility of renewable energy and loads. However, the large input data usually make the planning problem difficult to solve, so time series aggregation is often used to reduce the computational complexity. Nevertheless, it is difficult to guarantee the reliability of operation on the whole input data. Therefore, this paper proposes an iterative method to select extreme scenarios, and designs two indicators to select extreme scenarios, considering the system power balance and peak shaving capacity situation. Based on these two indicators, the periods of maximum load shedding and the periods of maximum renewable energy curtailment will be selected as extreme scenarios in the results of the operational optimization problem. We iteratively add extreme scenarios to the set of scenarios of the planning problem until the reliability of system operation can be adequately met. At the same time, in order to ensure the effectiveness of extreme scenarios, the operation statuses of thermal units in those periods are also taken into account. Our method is tested on an IEEE RTS-24 system with some modification. The results show that our method can guarantee the reliability of the whole system and is superior to the method that simply selects extreme scenarios. Meanwhile, we also perform a sensitivity analysis of the price of energy storage.

This paper proposes a “planning and control” scheme for a compliant mechanism (CM) based on a pneumatic actuator (PAC) with hysteresis nonlinearity and full-state constraints. In the planning part, a novel direct inverse model is presented to compensate for hysteresis nonlinearity, enabling more accurate planning of the desired air pressure based on the desired contact force. In the control part, by fusing fuzzy logic systems (FLSs) and an extended state observer (ESO), a fuzzy ESO is developed to observe the external disturbance and the rate of change of the air pressure. Additionally, the challenges in the controller design caused by full-state constraints are overcome by constructing barrier Lyapunov functions (BLFs). It is proved that all signals of the closed-loop system are bounded, and the tracking error of the air pressure can converge to a small neighborhood of the origin. Finally, the effectiveness and robustness of the proposed method are verified by hardware experiments, which also show that the root mean square errors of force control accuracies are within 2N, achieving satisfactory force control effects.