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Abstract Background Bladder cancer (BLCA) is a common malignancy characterized by dysregulated transcription and a lack of effective therapeutic targets. In this study, we aimed to identify and evaluate novel targets with clinical potential essential for tumor growth in BLCA. Methods CRISPR-Cas9 screening was used to identify transcription factors essential for bladder cancer cell viability. The biological functions of KLF16 in bladder cancer were investigated both in vitro and in vivo. The regulatory mechanism between KLF16 and MYC was elucidated through a series of analyses, including RNA sequencing, quantitative polymerase chain reaction (qPCR), RNA immunoprecipitation, Western blotting, Mass spectrometry, Dual-luciferase reporter assays, Cleavage Under Targets and Tagmentation (CUT&Tag) sequencing, OptoDroplets assays, and RNA stability assay. The clinical relevance of KLF16 and MYC in bladder cancer was evaluated through analyses of public databases and immunohistochemistry. Results Krüppel-like factor 16 (KLF16) was essential for BLCA cell viability. Elevated expression of KLF16 was observed in bladder cancer tissues, and higher expression levels of KLF16 were correlated with poor progression-free survival (PFS) and cancer-specific survival (CSS) probabilities in BLCA patients. Mechanistically, KLF16 mRNA competed with the mRNA of dual-specificity phosphatase 16 (DUSP16) for binding to the RNA-binding protein, WW domain binding protein 11 (WBP11), resulting in destabilization of the DUSP16 mRNA. This, in turn, led to activation of ERK1/2, which stabilized the MYC protein. Furthermore, KLF16 interacted with MYC to form nuclear condensates, thereby enhancing MYC’s transcriptional activity. Additionally, MYC transcriptionally upregulated KLF16, creating a positive feedback loop between KLF16 and MYC that amplified their oncogenic functions. Targeting this loop with bromodomain inhibitors, such as OTX015 and ABBV-744, suppressed the transcription of both KLF16 and MYC, resulting in reduced BLCA cell viability and tumor growth, as well as increased sensitivity to chemotherapy. Conclusions Our study revealed the crucial role of the KLF16/MYC regulatory axis in modulating tumor growth and chemotherapy sensitivity in BLCA, suggesting that combining bromodomain inhibitors, such as OTX015 or ABBV-744, with DDP or gemcitabine could be a promising therapeutic intervention for BLCA patients.

Abstract Previous studies have shown that a high prevalence of depression and anxiety is a key factor leading to a decrease in student satisfaction with university life. Therefore, this study used two waves of longitudinal data to investigate the longitudinal relationships among depression, anxiety, and student satisfaction with university life among college students. We employed correlation analysis and cross-lagged models to analyze the correlation and cross-lagged relationships among depression, anxiety, and student satisfaction with university life. The results indicate a significant negative correlation between depression and student satisfaction with university life. The cross-lagged models indicate that depression (Time 1) negatively predicts student satisfaction with university life (Time 2). Anxiety (Time 1) does not have a significant predictive effect on student satisfaction with university life (Time 2). Moreover, student satisfaction with university life negatively predicts both depression (Time 2) and anxiety (Time 2). Improving student satisfaction with university life has a significant impact on reducing levels of depression and anxiety among college students. The research results can provide valuable information for mental health professionals, school administrators, and policymakers, enabling them to take more targeted measures to reduce depression and anxiety symptoms among university students and enhance student satisfaction with university life.

Background: Minimal residual disease (MRD) testing is a promising approach to tailor the treatment of multiple myeloma (MM). However, several major concerns remain to be addressed before moving it into daily practice, most of which stem from the dynamic nature of the MRD status. Thus, it is crucial to understand the MRD dynamics and propose its clinical implications. Methods: We retrospectively analysed the data of patients with newly diagnosed MM (NDMM) who had flow cytometry-based MRD tests at multiple time points after initiation of therapy. The impact of undetectable MRD (including attainment, duration and loss) on clinical outcomes was analysed. Results: In a cohort of 220 patients with NDMM, attainment of MRD− offered favourable outcomes (P < 0.0001 for both progression-free survival (PFS) and overall survival (OS)), regardless of baseline risk factors. Notably, MRD− duration ≥12 months was associated with an 83 % (95 % confidence interval (CI), 0.09−0.34; P < 0.0001) or 69 % (95 % CI, 0.13−0.76; P = 0.0098) reduction in risk of progression/death or death, while the longer MRD− was sustained, the better the outcome was. Loss of MRD− led to poor PFS (hazard ratio (HR) 0.01, 95 % CI 0–0.06, P < 0.0001) and OS (HR 0.03, 95 % CI 0–0.24, P = 0.0008). Most patients (70 %) who lost MRD− status carried high-risk cytogenetic abnormalities (HRCAs). While MRD− was temporally inconsistent with conventional therapeutic responses (eg ≥ complete remission or very good partial response), it predicted disease progression or recurrence more robustly than the latter. Last, the predictive value of the MRD status was independent of baseline risk factors (eg high-risk cytogenetic abnormality, International Staging System (ISS) or Revised (R-)ISS staging). Conclusions: Longitudinal assessment of MRD during the treatment course and follow-up is required for monitoring disease progression or relapse, to guide treatment decisions. Accordingly, a prospective study is currently ongoing to investigate the feasibility and benefit of the MRD-tailored therapy according to the longitudinal changes of the MRD status.

IntroductionInfection by pathogenic bacteria during weaning is a common cause of diarrhea and intestinal inflammation in piglets. Supplementing the diet with synbiotics is beneficial for animal health. The strain of Lactiplantibacillus plantarum L47 (L47) isolated in our lab exhibited good probiotic properties when combined with inulin. Here, the effectiveness of combining L47 and inulin (CLN) in protecting against enterotoxigenic Escherichia coli (ETEC) induced colon and liver inflammation in weaned piglets was evaluated.MethodsTwenty-eight piglets aged 21 days were randomly assigned into 4 groups: CON (control), LI47 (oral CLN culture fluid, 1010 CFU/d of L47 and 1 g/d of inulin), ECON (oral ETEC culture fluid, 1010 CFU/d), and ELI47 (oral CLN and ETEC culture fluid). After 24 days, the colon and liver samples were collected for further analysis.Results and discussionCLN alleviated colon damage caused by ETEC challenge, as evidenced by an increase of colonic crypt depth, mRNA expression of tight junction Claudin-1 and Occludin, GPX activity, the concentration of IL-10 and sIgA (p

Abstract It remains a substantial challenge to balance treatment efficacy and toxicity in geriatric patients with multiple myeloma (MM), primarily due to the dynamic nature of frailty. Here, we conducted a prospective study to evaluate the feasibility and benefits of dynamic frailty-tailored therapy (DynaFiT) in elderly patients. Patients with newly diagnosed MM (aged ≥ 65 years) received eight induction cycles of bortezomib, lenalidomide, and dexamethasone (daratumumab was recommended for frail patients), with treatment intensity adjusted according to longitudinal changes in the frailty category (IMWG-FI) at each cycle. Of 90 patients, 33 (37%), 16 (18%), and 41 (45%) were fit, intermediate fit, and frail at baseline, respectively. Of 75 patients who had geriatric assessment at least twice, 28 (37%) experienced frailty category changes at least once. At analysis, 15/26 (58%) frail patients improved (27% became fit and 31% became intermediate fit), 4/15 (27%) intermediate fit patients either improved or deteriorated (two for each), and 6/30 (20%) fit patients deteriorated. During induction, 34/90 (38%) patients discontinued treatment, including 10/33 (30%) fit, 4/16 (25%) intermediate fit, and 20/41 (49%) frail; 14/40 (35%) frail patients discontinued treatment within the first two cycles, mainly because of non-hematologic toxicity (mostly infections). For fit, intermediate-fit, and frail patients, the overall response rate was 100%, 93%, and 73%, respectively; one-year overall survival was 90%, 75%, and 54%, respectively. Therefore, the individualized DynaFiT is feasible and promising for heterogeneous elderly patients.

Abstract GCaMP is a genetically encoded calcium indicator (GECI) widely used in neuroscience research. It measures intracellular Ca2+ level by fluorescence changes as it directly binds to Ca2+. In this process, the effect of this calcium buffer on the intracellular calcium signaling and cell physiology is often not taken into consideration. However, growing evidence from calcium imaging studies shows GCaMP expression under certain conditions can generate aberrant activity, such as seizures. In this study, we examined the effect of GCaMP6 expression in the dentate gyrus (DG) on epileptogenesis. We found that viral expression of GCaMP6s but not GCaMP6f in the DG induces tonic–clonic seizures several weeks after viral injection. Cell-type specific expression of GCaMP6s revealed the granule cells (GCs) as the key player in GCaMP6s-induced epilepsy. Finally, by using slice electrophysiology, we demonstrated that GCaMP6s expression increases neuronal excitability in the GCs. Together, this study highlights the ability of GCaMP6s in DG-associated epileptogenesis.

Abstract Microvesicles (MVs) containing proteins, nucleic acid or organelles are shed from the plasma membrane. Although the mechanisms of MV budding are well elucidated, the connection between endosomal trafficking and MV formation remains poorly understood. In this report, RAB22A is revealed to be crucial for EGFR‐containing MVs formation by the RAB GTPase family screening. RAB22A recruits TBC1D2B, a GTPase‐activating protein (GAP) of RAB7A, to inactivate RAB7A, thus preventing EGFR from being transported to late endosomes and lysosomes. RAB22A also engages SH3BP5L, a guanine‐nucleotide exchange factor (GEF) of RAB11A, to activate RAB11A on early endosomes. Consequently, EGFR is recycled to the cell surface and packaged into MVs. Furthermore, EGFR can phosphorylate RAB22A at Tyr136, which in turn promotes EGFR‐containing MVs formation. Our findings illustrate that RAB22A acts as a sorter on early endosomes to sort EGFR to recycling endosomes for MV shedding by both activating RAB11A and inactivating RAB7A.

Bi(Te, Se)-based compounds have attracted lots of attention for nearly two centuries as one of the most successful commercial thermoelectric (TE) materials due to their high performance at near room temperature. Compared with 3D bulks, 2D thin films are more compatible with modern semiconductor technology and have unique advantages in the construction of micro- and nano-devices. For device applications, high average TE performance over the entire operating temperature range is critical. Herein, highly c-axis-oriented N-type Bi(Te, Se) epitaxial thin films have been successfully prepared using the pulsed laser deposition technology by adjusting the deposition temperature. The film deposited at ∼260 °C demonstrate a remarkable average power factor (PFave) of ∼24.4 μW·cm−1·K−2 over the temperature range of 305–470 K, higher than most of the state-of-the-art Bi(Te, Se)-based films. Moreover, the estimated average zT value of the film is as high as ∼0.81. We then constructed thin-film TE devices by using the above oriented Bi(Te, Se) films, and the maximum output power density of the device can reach up to ∼30.1 W/m2 under the temperature difference of 40 K. Predictably, the outstanding average TE performance of the highly oriented Bi(Te, Se) thin films will have an excellent panorama of applications in semiconductor cooling and power generation.

Industrial parks (IPs) play a crucial role in facilitating economic efficiency and comprehensive energy utilization in the industrial age. At the same time, multi-energy coupling and management of various types of energy in IP have become serious challenges. In this paper, combined heat and power unit (CHP) model considering operation mode switching characteristics is formulated by exploring its internal composition to improve output flexibility of the energy supply side. Then, heat and electricity integrated energy system (HE-IES) optimal dispatch and pricing model are established, taking electricity and heat demand response strategy and steam thermal inertia property into account. Based on the above models, a mixed-integer bilinear programming framework is designed to coordinate the day-ahead operation and pricing strategy of the HE-IES in the IP. The scenario study is carried out on a practical industrial park in Southern China. Numerical results indicate the proposed mechanism can effectively improve IP's energy utilization and economic efficiency.

Background: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and challenging to interpret in clinical practice, requiring significant time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry, utilizing sophisticated sensors, is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. Objective: This study aims to investigate the use of sensor-acquired isokinetic muscle strength data, combined with machine learning techniques, to predict the GDI in hemiplegic patients. This study utilizes data captured from sensors embedded in the Biodex dynamometry system and the Vicon 3D motion capture system, highlighting the integration of sensor technology in clinical gait analysis. Methods: This study was a cross-sectional, observational study that included a cohort of 150 post-stroke hemiplegic patients. The sensor data included measurements such as peak torque, peak torque/body weight, maximum work of repeated actions, coefficient of variation, average power, total work, acceleration time, deceleration time, range of motion, and average peak torque for both flexor and extensor muscles on the affected side at three angular velocities (60°/s, 90°/s, and 120°/s) using the Biodex System 4 Pro. The GDI was calculated using data from a Vicon 3D motion capture system. This study employed four machine learning models—Lasso Regression, Random Forest (RF), Support Vector regression (SVR), and BP Neural Network—to model and validate the sensor data. Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R2), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Results: The RF model outperformed others in predicting GDI, with an MSE of 16.18, an R2 of 0.89, and an MAE of 2.99. In contrast, the Lasso Regression model yielded an MSE of 22.29, an R2 of 0.85, and an MAE of 3.71. The SVR model had an MSE of 31.58, an R2 of 0.82, and an MAE of 7.68, while the BP Neural Network model exhibited the poorest performance with an MSE of 50.38, an R2 of 0.79, and an MAE of 9.59. SHAP analysis identified the maximum work of repeated actions of the extensor muscles at 60°/s and 120°/s as the most critical sensor-derived features for predicting GDI, underscoring the importance of muscle strength metrics at varying speeds in rehabilitation assessments. Conclusions: This study highlights the potential of integrating advanced sensor technology with machine learning techniques in the analysis of complex clinical data. The developed GDI prediction model, based on sensor-acquired isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application.

This study aims to examine the effects of various aluminum alloy constitutive models on the behavior of concrete-filled aluminum tubular stub columns under axial compression. The bi-linear model with hardening, the Baehre model, and the Ramberg-Osgood (R-O) model, which follow the European standard (EC9) were analyzed and compared in terms of their ability to describe the stress-strain behavior of aluminum alloy tensile coupons over the full range, and their respective application scenarios were discussed. A total of 74 sets of experimental data results were collected to examine the effects of these three models on the ultimate load of concrete-filled aluminum tubular stub columns. Furthermore, a full-scale model was constructed to analyze the effect of the hardening exponent n in the R-O model on the load-displacement curves. The results show that, apart from the bi-linear model with hardening, the other two aluminum alloy constitutive models are capable of accurately predicting the stress-strain behavior of aluminum alloys throughout the full range. The accuracy of the R-O model is significantly influenced by the calculation methods of n. The Baehre model is found to be more suitable for non-heat-treated aluminum alloys. The simulated ultimate load values obtained from the three constitutive models fall within a deviation range of ±10%, indicating their suitability for practical engineering applications. Among the three models, the R-O model exhibits the highest stability, as changes in the hardening exponent n do not affect the ultimate load but have a significant effect on the load-displacement curves beyond the ultimate load.

PFAS (per- and polyfluoroalkyl substances) have been extensively used across numerous industries and consumer goods. Due to their high persistence and mobility, they are ubiquitous in the environment. Exposure to PFAS occurs in people via multiple pathways such as dermal contact, water supply, air inhalation, and dietary intake. Even if some PFAS are being phased out because of their persistent presence in the environment and harmful impacts on human health, mixes of replacement and legacy PFAS will continue to pollute the ecosystem. Numerous toxicological investigations have revealed harmful effects of PFAS exposure on female reproductive health, e.g., polycystic ovaries syndrome, premature ovarian failure, endometriosis, reproductive system tumors, pregnancy complications, and adverse pregnancy outcomes. Despite extensive epidemiological studies on the reproductive toxicity of PFAS, research findings remain inconsistent, and the underlying mechanisms are not well understood. In this review, we give an in-depth description of the sources and pathways of PFAS, and then review the reproductive toxicity of PFAS and its possible mechanisms.

Berberine hydrochloride (BH) is a versatile bioactive compound derived from the plants of the Berberis genus, known for its various pharmacological effects. However, its oral bioavailability is low due to its high hydrophilicity and limited permeability. To enhance its clinical efficacy and oral bioavailability, this study designed and prepared a BH-loaded self-microemulsifying drug delivery system (BH-SMEDDS), and characterized its in vitro and in vivo properties. Firstly, the optimal formulation of BH-SMEDDS was selected using solubility evaluations, pseudo-ternary phase diagrams, and particle size analysis. The formulation containing 55% Capmul MCM, 22.5% Kolliphor RH 40, and 22.5% 1,2-propanediol was developed. BH-SMEDDS exhibited stable physicochemical properties, with an average particle size of 47.2 ± 0.10 nm and a self-emulsification time of 26.02 ± 0.24 s. Moreover, in vitro dissolution studies showed significant improvements in BH release in simulated intestinal fluid, achieving 93.1 ± 2.3% release within 300 min. Meanwhile, BH-SMEDDS did not exhibit cytotoxic effects on the Caco-2 cells. Additionally, BH-SMEDDS achieved a 1.63-fold increase in oral bioavailability compared to commercial BH tablets. Therefore, SMEDDS presents a promising strategy for delivering BH with enhanced oral bioavailability, demonstrating significant potential for clinical application.

Abstract INTRODUCTION The prevalence of poor sleep quality and sleep apnea differs by race and ethnicity and may contribute to racial disparities in cognitive aging. We investigated whether sleep quality and sleep apnea risk were associated with cognitive function and decline and whether the associations differed by race/ethnicity. METHODS Participants from the Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE; N = 1690; mean age: 75.7 years) study, a cohort of Asian, Black, Latino, and White participants, completed a modified Pittsburgh Sleep Quality Index assessing subjective sleep quality, latency, duration, disturbances, sleep medication use, and daytime dysfunction. Sleep apnea risk was measured by questions about snoring, tiredness, and whether apnea was observed. Executive function and verbal episodic memory were assessed at three time points over an average of 2.7 years with the Spanish and English Neuropsychological Assessment Scale. We fit linear mixed‐effect models and stratified analyses by race/ethnicity. RESULTS Higher sleep apnea risk was associated with faster declines in verbal episodic memory (β̂sleep apnea = −0.02, 95% confidence interval [CI], −0.04, −0.001) but not in executive function. Poorer sleep quality was associated with lower levels of and faster decline in executive function but not in verbal episodic memory. Race/ethnicity modified these associations: compared to estimated effects among White participants, poorer global sleep quality (β̂sleep*time = −0.02, 95% CI, −0.02, −0.01) was associated with larger effects on decline in executive function among Black participants. Estimated effects of some individual sleep quality components were also modified by race/ethnicity; for example, sleep medication use was associated with faster declines in executive function (β̂sleep*time = −0.05, 95% CI, −0.07, −0.03) and verbal episodic memory β̂sleep*time = −0.04, 95% CI, −0.07, −0.02) among Black participants compared to White participants. DISCUSSION Observational evidence indicates sleep quality is a promising target for addressing racial/ethnic disparities in cognitive aging, especially among Black older adults. Highlights Sleep apnea risk was associated with faster declines in verbal episodic memory but not executive function among all participants. Global sleep quality was associated with lower levels of and faster decline in executive function but not verbal episodic memory among all participants. Black older adults were particularly susceptible to the estimated adverse cognitive impacts of global sleep quality, particularly the use of sleep medication.

Abstract Background RecQ‐mediated genome instability 2 (RMI2) maintains genome stability by promoting DNA damage repair. It has been reported to accelerate the progression of several tumors. However, the functional mechanism of RMI2 in breast cancer remains unclear. Methods Gene expression profiles were obtained from TCGA, GTEx, and GEO databases. The expression of RMI2 and its prognostic value in breast cancer was explored. In addition, we calculated pooled standardized mean deviation (SMD) and performed a summary receiver operating characteristic (sROC) curve analysis to further determine RMI2 expression status and diagnostic significance. The functions and related signaling pathways were investigated based on GO and KEGG analyses. The PPI network was constructed by combining the STRING database and Cytoscape software. Subsequently, in vitro assays were conducted to detect the effect of RMI2 on the proliferation and migration of breast cancer cells. Results The expression of RMI2 was markedly upregulated in breast cancer tissues relative to that in normal tissues. Moreover, pooled SMD further confirmed the overexpression of RMI2 in breast cancer (SMD = 1.29, 95% confidence interval (CI): 1.18–1.41, p = 0.000). The sROC curve analysis result suggested that RMI2 had a relatively high diagnostic ability in breast cancer (AUC = 0.87, 95% CI: 0.84–0.90). High RMI2 expression was associated with poor prognosis. GO and KEGG analyses revealed that RMI2 was closely related to cell adhesion, various enzyme activities, and PI3K/AKT signaling pathway. PPI analysis showed that RMI2 had interactions with proteins involved in DNA damage repair. knockdown of RMI2 remarkably inhibited the proliferation and migration of breast cancer cells, while overexpression of RMI2 exerted the opposite effects. Furthermore, we identified that RMI2 accelerates the proliferation and migration of breast cancer cells via activation of the PI3K/AKT pathway. Conclusion The results suggest that RMI2 is a potential diagnostic and prognostic biomarker associated with cell proliferation and migration, and may be used as a novel therapeutic target for breast cancer in the future.

The story of Futijinhu 浮提金壺 (“Golden Pot of Futi”) in Shiyiji 拾遺記 (Record of Gleanings), whose authorship is traditionally attributed to Wang Jia (王嘉) from the Eastern Jin Dynasty, appears to be a Daoist rendition at first glance, reimagining the legend of Laozi’s Daodejing. However, upon closer examination of the depiction of “Golden Pot of Futi”, the characters with “Shentong Shanshu” (神通善書, supranormal cognition and exceptional writing ability) and the narrative of writing and its outcomes, it becomes evident that this tale harbors a multifaceted Buddhist essence. In the tale, one can observe the changes and diversity in the early methods of translating Buddhist scriptures into Chinese, the references and adaptations of Buddhist imagery and narratives by Daoists, the understanding and imagination of materials used for writing Buddhist scriptures and early iconographic forms, and even the author’s insights and responses to the evolving religious landscape of their era. When placed in a broader historical context, exploring the Buddhist elements in this tale further aids in understanding the dynamic interactions between Buddhism and Daoism during the Wei and Jin periods.

Deep models may have disappointing performance in real applications due to the domain shifts in data distributions between the source and target domain. Although a few unsupervised domain adaptation methods have been proposed to make the pre-train models effective on target domain datasets, constraints like data privacy, security, and transmission limits restrict access to VHR remote sensing images, making existing unsupervised domain adaptation methods almost ineffective in specific change detection areas. Therefore, we propose a source-free unsupervised domain adaptation change detection structure to complete specific change detection tasks, using only the pre-trained source model and unlabelled target data. The GAN-based source generation component is designed to generate synthetic source data, which, to some extent, reflects the distribution of the source domain. Moreover, these data can be utilised in model knowledge transfer. The model adaptation component facilitates knowledge transfer between models by minimising the differences between deep features, using AAM (Attention Adaptation Module) to extract the difference between high-level features, meanwhile we proposed ISM (Intra-domain Self-supervised Module) to train target model in a self-supervised strategy in order to improve the knowledge adaptation. Our SFDA-CD framework demonstrates superior accuracy over existing unsupervised domain adaptation change detection methods, which has 0.6% cIoU and 1.5% F1 score up in cross-regional tasks and 1.4% cIoU and 1.9% F1 score up in cross-scenario tasks, proving that it can effectively reduce the domain shift between the source and target domains even without access to source data. Additionally, it can facilitate knowledge transfer from the source model to the target model.

The prediction of binding affinities between target proteins and small molecule drugs is essential for speeding up the drug research and design process. To attain precise and effective affinity prediction, computer-aided methods are employed in the drug discovery pipeline. In the last decade, a variety of computational methods has been developed, with deep learning being the most commonly used approach. We have gathered several deep learning methods and classified them into convolutional neural networks (CNNs), graph neural networks (GNNs), and Transformers for analysis and discussion. Initially, we conducted an analysis of the different deep learning methods, focusing on their feature construction and model architecture. We discussed the advantages and disadvantages of each model. Subsequently, we conducted experiments using four deep learning methods on the PDBbind v.2016 core set. We evaluated their prediction capabilities in various affinity intervals and statistically and visually analyzed the samples of correct and incorrect predictions for each model. Through visual analysis, we attempted to combine the strengths of the four models to improve the Root Mean Square Error (RMSE) of predicted affinities by 1.6% (reducing the absolute value to 1.101) and the Pearson Correlation Coefficient (R) by 2.9% (increasing the absolute value to 0.894) compared to the current state-of-the-art method. Lastly, we discussed the challenges faced by current deep learning methods in affinity prediction and proposed potential solutions to address these issues.

The increased deployment of electricity-based hydrogen production strengthens the coupling of power distribution system (PDS) and hydrogen energy system (HES). Considering that power to hydrogen (PtH) has great potential to facilitate the usage of renewable energy sources (RESs), the coordination of PDS and HES is important for planning purposes under high RES penetration. To this end, this paper proposes a multi-stage co-planning model for the PDS and HES. For the PDS, investment decisions on network assets and RES are optimized to supply the growing electric load and PtHs. For the HES, capacities of PtHs and hydrogen storages (HSs) are optimally determined to satisfy hydrogen load considering the hydrogen production, tube trailer transportation, and storage constraints. The overall planning problem is formulated as a multistage stochastic optimization model, in which the investment decisions are sequentially made as the uncertainties of electric and hydrogen load growth states are revealed gradually over periods. Case studies validate that the proposed co-planning model can reduce the total planning cost, promote RES consumption, and obtain more flexible decisions under long-term load growth uncertainties.

Coriander (Coriandrum sativum L.) is prized for its aroma and medicinal properties and is extensively employed in various cuisines. Light intensity and photoperiod greatly impact its phenological development. The application of light-emitting diodes (LEDs) in facility cultivation systems enables precise control of lighting conditions, leading to enhanced energy efficiency in coriander cultivation. This study investigated three levels of light intensity (133, 200, and 400 μmol·m−2·s−1) and three photoperiods (8L/16D, 16L/8D, and 24L) to comprehensively assess their effects on coriander’s morphological development, photosynthetic characteristics, and energy utilization efficiency. The objective was to identify a combination conducive to efficient and energy-saving coriander cultivation in PFALs. Results indicated that high light intensity (400 μmol·m−2·s−1) with continuous lighting (24L) reduces coriander’s photosynthetic capacity, while 24-h of continuous lighting can boost yield at the expense of energy efficiency. An 8-h photoperiod significantly decreases the yield compared to 16 h. Low light intensity inhibits plant development, indicating that 133 μmol·m−2·s−1 is suboptimal. For optimal efficiency and yield, a light intensity of 200 μmol·m−2·s−1 and a 16-h photoperiod are recommended in coriander PFAL cultivation. These findings advocate for the adoption of these specific conditions for the indoor cultivation of coriander within PFAL systems.

Background: Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to image size, image format, image dimensionality, and other factors. Between the acquisition of the CT scan and feeding the data into the deep learning model, there are several steps including data use permission, data access and download, data annotation, and data preprocessing. This paper aims to recommend a complete and detailed guide for researchers who want to engage in interdisciplinary lung nodule research of CT images and Artificial Intelligence (AI) engineering.Methods: The data preparation pipeline used the following four popular large-scale datasets: LIDC-IDRI (Lung Image Database Consortium image collection), LUNA16 (Lung Nodule Analysis 2016), NLST (National Lung Screening Trial) and NELSON (The Dutch-Belgian Randomized Lung Cancer Screening Trial). The dataset preparation is presented in chronological order.Findings: The different data preparation steps before deep learning were identified. These include both more generic steps and steps dedicated to lung nodule research. For each of these steps, the required process, necessity, and example code or tools for actual implementation are provided.Discussion and conclusion: Depending on the specific research question, researchers should be aware of the various preparation steps required and carefully select datasets, data annotation methods, and image preprocessing methods. Moreover, it is vital to acknowledge that each auxiliary tool or code has its specific scope of use and limitations. This paper proposes a standardized data preparation process while clearly demonstrating the principles and sequence of different steps. A data preparation pipeline can be quickly realized by following these proposed steps and implementing the suggested example codes and tools.

Polycystic ovary syndrome (PCOS) is the most common reproductive endocrine disorder affecting women, which can lead to infertility. Infertility, obesity, hirsutism, acne, and irregular menstruation are just a few of the issues that PCOS can be linked to. PCOS has a complicated pathophysiology and a range of clinical symptoms. Chronic low-grade inflammation is one of the features of PCOS. The inflammatory environment involves immune and metabolic disturbances. Numerous organ systems across the body, in addition to the female reproductive system, have been affected by the pathogenic role of immunological dysregulation in PCOS in recent years. Insulin resistance and hyperandrogenism are associated with immune cell dysfunction and cytokine imbalance. More importantly, obesity is also involved in immune dysfunction in PCOS, leading to an inflammatory environment in women with PCOS. Hormone, obesity, and metabolic interactions contribute to the pathogenesis of PCOS. Hormone imbalance may also contribute to the development of autoimmune diseases. The aim of this review is to summarize the pathophysiological role of immune dysregulation in various organ systems of PCOS patients and provide new ideas for systemic treatment of PCOS in the future.

Objective • This case report aims to present a rare case of thoracic lordosis and lumbar kyphosis and describe the posterior instrumented scoliosis correction performed. Case presentation • A 59-year-old female presented with low back pain. She had undergone ventriculoperitoneal shunt placement 8 years ago. I scored 76 on the Wechsler Adult Intelligence Scale. MRI of the lumbar spine showed spinal canal stenosis at L3/4, L4/5, and L5/S1. Full spine X-ray revealed thoracic lordosis and lumbar kyphoscoliosis, the coronal imbalance, and the sagittal compensatory balance. In order to avoid the risk of brain swelling and paraplegia, pedicle subtraction osteotomies (PSO) in the L2 and lumbar posterior instrumented scoliosis correction were performed under electroencephalogram and neuro electrophysiological monitoring. Shoulder imbalance was observed 1 year after surgery, but there was no loss of lumbar correction. Conclusion • In future cases of complex spinal deformity, it is important to observe whether there is cerebral ventricular dilatation on MRI before the operation. If severe thoracic lordosis is combined with lumbar scoliosis, over-correcting the lumbar scoliosis should be avoided to prevent shoulder imbalance. [ABSTRACT FROM AUTHOR]

Abstract Strawberry vein banding virus (SVBV) is one of the serious viral pathogens infecting strawberry worldwide. To understand the molecular characterization of SVBV from China, complete genome sequences of sixteen SVBV isolates were cloned and sequenced. Sequence comparison showed they shared high nucleotide sequence identity (93.6–99.5%) with isolates from China and Japan (96.6–98.4%), while relatively low identity with the isolates from Canada (91.9–93.7%) and USA (85.5–85.9%). Phylogenetic analyses based on the complete genome sequence or coat protein (CP) gene showed the SVBV isolates clustered into three clades correlated with geographic distribution. Recombination analyses identified 13 recombinants and 21 recombinant events, indicating frequent and multiple recombinations in SVBV evolution. Furthermore, a sensitive loop-mediated isothermal amplification (LAMP) method was developed for rapid detection of SVBV isolates, which could be especially suitable for seedling propagation, virus-free culture and routine diagnostics in field investigation. This study offers new understanding of the molecular evolution and may help to improve the management of SVBV.

Timely detection of an evolving event of an infectious disease with superspreading potential is imperative for territory-wide disease control as well as preventing future outbreaks. While the reproduction number (R) is a commonly-adopted metric for disease transmissibility, the transmission heterogeneity quantified by dispersion parameter k, a metric for superspreading potential is seldom tracked. In this study, we developed an estimation framework to track the time-varying risk of superspreading events (SSEs) and demonstrated the method using the three epidemic waves of COVID-19 in Hong Kong. Epidemiological contact tracing data of the confirmed COVID-19 cases from 23 January 2020 to 30 September 2021 were obtained. By applying branching process models, we jointly estimated the time-varying R and k. Individual-based outbreak simulations were conducted to compare the time-varying assessment of the superspreading potential with the typical non-time-varying estimate of k over a period of time. We found that the COVID-19 transmission in Hong Kong exhibited substantial superspreading during the initial phase of the epidemics, with only 1 % (95 % Credible interval [CrI]: 0.6–2 %), 5 % (95 % CrI: 3–7 %) and 10 % (95 % CrI: 8–14 %) of the most infectious cases generated 80 % of all transmission for the first, second and third epidemic waves, respectively. After implementing local public health interventions, R estimates dropped gradually and k estimates increased thereby reducing the risk of SSEs to approaching zero. Outbreak simulations indicated that the non-time-varying estimate of k may overlook the possibility of large outbreaks. Hence, an estimation of the time-varying k as a compliment of R as a monitoring of both disease transmissibility and superspreading potential, particularly when public health interventions were relaxed is crucial for minimizing the risk of future outbreaks.

The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs. For such a complex situation, this paper analyses adversarial attackers’ ability to deceive IDSs used in the IIoT and proposes the evaluation of an IDS with function-discarding adversarial attacks in the IIoT (EIFDAA), a framework that can evaluate the defence performance of machine learning-based IDSs against various adversarial attack algorithms. This framework is composed of two main processes: adversarial evaluation and adversarial training. Adversarial evaluation can diagnose IDS that is unfitting in adversarial environments. Then, adversarial training is used to treat the weak IDS. In this framework, five well-known adversarial attacks, the fast-gradient sign method (FGSM), basic iterative method (BIM), projected gradient descent (PGD), DeepFool and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) are used to convert attack samples into adversarial samples to simulate the adversarial environment. This study evaluates the capability of mainstream machine learning techniques as intrusion detection models to defend against adversarial attacks, and retrains these detectors to improve the robustness of IDSs through adversarial training. In addition, the framework includes an adversarial attack model that discards the attack function of the attack samples in the IIoT. Through the experimental results on the X-IIoTID dataset, the dropped adversarial detection rate of these detectors to nearly zero demonstrates that an adversarial attack has black-box attack capabilities for these IDSs. Additionally, the improved IDSs retrained with adversarial samples can effectively defend against adversarial attackers while maintaining the original detection rate for the attack samples. EIFDAA is expected to be a solution that can be applied to IDS for improving the robustness in the IIoT.

Today’s power system is facing various challenges brought by large-scale renewable energy (RE) integration, which brings higher demand for flexibility. With the energy network gradually showing its distributed structural characteristics, multi-energy microgrids (MEMG) become an important component to effectively utilize distributed energy sources and supplement the flexibility of power distribution system (PDS). To effectively harness the operational flexibility of distributed MEMGs, we propose in this paper an evaluation method to quantify the flexibility capability of MEMG. A virtually established MG flexibility bus (MG-FB) is endowed with MG flexibility parameters (MG-FPs), which can reflect the flexibility characteristics of MEMG. To consider the impact of operational uncertainty on MG-FPs, a two-stage adaptive robust optimization (ARO) model is proposed, which can be solved by the C&CG algorithm. The results of a typical test system show the influence of system configuration, operator’s risk preference, and other factors on the values of MG-FPs. Besides, we illustrate the effectiveness and applicability of the proposed framework in modeling and quantifying the operational flexibility of MEMG to support the operation of the upstream network.

Summary: Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP−BEZ235, GDC−0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM.

Public education finance in China plays an important role in education equality. This study investigated two mediation effects with a generalized structural equation model that comprised the mediation effect of shadow education at the school, family, and individual levels and the moderating role of education finance. There was a strong association among heterogeneity factors, shadow education, and educational results, with shadow education playing a mediating role in math and English courses. Individual heterogeneity differences had a negative impact on equality in educational results through access to additional shadow education opportunities, while heterogeneous factors were mediated through shadow education, causing financial moderation effects, in turn affecting inequality in educational results. Finally, the moderation degree and direction of education finance varied significantly, with a greater moderation effect on household-level factors that lead to unequal educational results. Targeted efforts are required to regulate shadow education, which is key to the development of the education system.

To respond to emergencies in a timely manner, emergency engineering equipment has been an important tool to implement emergency strategies. However, random failures of the equipment may occur during operation. Therefore, appropriate maintenance and spare parts inventory strategies are crucial to ensure the smooth operation of the equipment. Furthermore, the urgency degree of emergencies varies in practice. Nevertheless, existing studies rarely consider the impact of urgency degree and demand priorities on the service order of the equipment. To bridge the research gaps, this paper establishes a joint optimization model of maintenance and spare parts inventory strategies for emergency engineering equipment considering demand priorities. The proposed model includes two types of emergency engineering equipment with different service rates. The more urgent demand can be fulfilled by the equipment with a higher priority. Corrective maintenance and spare parts inventory policies are simultaneously performed for the equipment. The Markov process imbedding method is utilized to derive the probabilistic indexes of the system. To maximize the system availability, the number of maintenance engineers and the spare parts inventory strategy is optimized via the construction of the joint optimization model. The optimal solution for the optimization problem is obtained using the branch-and-bound method. Finally, this study presents practical examples to verify the effectiveness of the model and methods.

The entropy method measures the development level of green finance in 30 provinces and cities in China, and the spatial analysis method is used to describe the difference. Spatial pattern of the development level of green finance in China and empirically analyze the influencing factors of the development level of green finance. The results of the spatial econometric model show that the absolute difference in the development level of China’s green finance shows an overall increasing trend, and the relative difference shows a downward trend year by year. The level of financial development and environmental governance promote the development of green finance, while the level of economic growth and energy consumption inhibit the development of green finance. On this basis, we put forward countermeasures and suggestions from strengthening government functions and adjusting industrial structure according to economic growth, so as to promote the development of green finance in China.

Abstract Background Pompe disease (PD) is a rare inherited disorder caused by the deficiency of acid-α glucosidase, which leads to the impairment of organ and tissue functions and causes disabilities. As the first national survey on patients with late-onset PD (LOPD) in China, this study investigated the quality of life (QOL) of adult patients with LOPD in China and explored its contributors. Methods Data were derived from a nation-based, cross-sectional, self-response survey on rare diseases (RDs) in early 2018. Answers from 68 adult Chinese patients with LOPD were used for data analysis. QOL was measured using the World Health Organization Quality of Life: Brief Version. Covariates included age, gender, education, employment, reliance on assistive devices, medication history, social support, and disease economic burden. Data were analyzed using linear regression in R. Results For adult patients with LOPD, the average scores and standard deviations (SD) of the four dimensions of QOL were physical health = 33.77 (SD = 18.28), psychological health = 43.81 (SD = 21.70), environmental health = 39.43 (SD = 16.93), and social relationship = 46.20 (SD = 19.76); the scoring for each dimension was evaluated on a scale of 0 to 100. At the significance level of p

We optimized an ultrasound-assisted extraction process of Phellinus linteus mycelium polysaccharides (PLPs) and studied their monosaccharide composition and bacteriostatic properties. Based on a single-factor experiment, a three-factor, three-level Box–Behnken design was used to optimize the ultrasound-assisted extraction process of PLP, using the yield of PLP as the index. The chemical composition and monosaccharide composition of PLP were determined by chemical analysis and HPLC analysis, respectively. Microscopic morphological analysis of the surface of PLP was performed via swept-surface electron microscopy. The bacteriostatic properties of PLP were determined using the spectrophotometric turbidimetric method. The results showed that the best extraction process of PLP with ultrasonic assistance achieved a result of 1:42 g/mL. In this method, the ultrasonic temperature was 60 °C, ultrasonic extraction was performed for 20 min, and the yield of PLP was 12.98%. The monosaccharide composition of PLP mainly contains glucose (Glc), mannose (Man), galactose (Gal), and glucuronic acid (GlcA). The intracellular polysaccharide of Phellinus igniarius Mycelia (PIP) is an irregular spherical accumulation, the surface is rough and not smooth, and the extracellular polysaccharide (PEP) is a crumbly accumulation. PIP has a stronger inhibitory ability for S. aureus and E. coli and a slightly weaker inhibitory effect for B. subtilis; the inhibitory effect of PEP on S. aureus, E. coli, and B. subtilis is slightly inferior to that of PIP.

Long fibre-reinforced metal matrix composite materials, which are widely used in industry, have complex and diverse damage modes due to their structural characteristics. In this study, the progressive damage process and failure mode analysis of the SiCf/TC4 composite shafts were thoroughly investigated under single torsional loads. A bearing performance test was carried out, the damage process was monitored using acoustic emissions, and the fracture specimens were analysed using a scanning electron microscope (SME). More specifically, under reverse torque loading, the damage process was slow-varying, the interface was subjected to tensile force, and fracture occurred mostly in the form of interface cracking; further, the breaking load of the specimen was 11,812 Nm. Under forward loading, the damage process was fast-varying. The fibres were subjected to tensile forces, and the fracture form was mostly fibre fracture; the breaking load of the specimen was 10,418 Nm. Under torque loading, the first damage to the specimens appeared in the outermost layer of the composite material’s reinforced section, and the initial cracking position was at the interface, expanding from the outside to the inside. Based on the principles of macro-mechanics and micro-mechanics theory, the cross-scale models were proposed, which contain the shaft with the same dimensions as the specimen and a micro-mechanics representative volume element (RVE) model. The initial interface damage load was 6552 Nm under reverse torque loading. Under forward loading, the initial interface damage load was 9108 Nm. In comparison to the acoustic emission test results, the main goal was to calculate the progressive damage process under the same conditions as the experiment, verifying the effectiveness of the cross-scale models.

Let A be a unital * -algebra. Let pn(A1, A2, …, An) be the polynomial defined by n indeterminates A1, A2, …, An ∈ A and their multiple skew Lie product, and ℕ be the set of non-negative integers. In this paper, under some mild conditions on A, it is shown that if D = {dm}m ∈ ℕ is the family of maps dm : A → A such that d0 = idA, the identity map on A satisfying dm(pn(A1, A2, …, An)) = ∑ /i1 + i2+ …1in=m pn(di1 (A1), di2 (A2), …, din (An)) for all A1, A2, …, An ∈ A and for each m ∈ ℕ, then D = {dm}m ∈ ℕ is an additive * -higher derivation. Moreover, we apply the above result to prime * -algebras, von Neumann algebras with no central summands of type I1, factor von Neumann algebras and standard operator algebras. [ABSTRACT FROM AUTHOR]