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Dynamometer card-based fault diagnosis confronts critical challenges including imbalanced data distributions, noise contamination, and constrained generalization capacity. This paper proposes a novel diagnostic framework integrating similarity-guided data augmentation with wavelet-optimized lightweight neural networks to address these limitations. The methodology employs kinematic feature recombination for minority class expansion, resolving data imbalance through physically consistent sample generation. Concurrently, vertical load disparities, temporal load variations, and auxiliary operational parameters are fused into standardized dynamometer card representations, enhancing feature discriminability without architectural modifications. We integrate Discrete Wavelet Transform (DWT) layers into stride-2 inverted residual modules ofMobileNet-V2, strategically embedding spectral noise suppression during feature downsampling while preserving diagnostically critical low-frequency patterns. Experimental validation confirms the framework’s superior accuracy and noise robustness, achieving significant performance improvements over conventional approaches while maintaining computational efficiency. The proposed solution establishes a deployable framework for industrial fault diagnosis, balancing diagnostic precision with operational practicality in oilfield monitoring applications.

Nowadays, the impact of engineered nanoparticles (NPs) on human health and environment has aroused widespread attention. It is essential to assess and predict the biological activity, toxicity, and physicochemical properties of NPs. Computation-based methods have been developed to be efficient alternatives for understanding the negative effects of nanoparticles on the environment and human health. Here, a classification-based structure-activity relationship model for nanoparticles (nano-SAR) was developed to predict the cellular uptake of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2). The norm index descriptors were employed for describing the structure characteristics of the involved nanoparticles. The Random forest algorithm (RF), combining with the Recursive Feature Elimination (RFE) was employed to develop the nano-SAR model. The resulted model showed satisfactory statistical performance, with the accuracy (ACC) of the test set and the training set of 0.950 and 0.966, respectively, demonstrating that the model had satisfactory classification effect. The model was rigorously verified and further extensively compared with models in the literature. The proposed model could be reasonably expected to predict the cellular uptakes of nanoparticles and provide some guidance for the design and manufacture of safer nanomaterials.

In the first chapter, I quantify two mega airline mergers’ effects on travelers’ welfare using the most popular identification technique, the difference-in-differences (DID) approach, in retrospective analysis of mergers. I find that the DID method does not generally provide an accurate measurement when follow-up mergers are close to the merger of interest in calendar time. I then develop and estimate a two-level model of consumer demand. The result shows that fare and frequency have opposite effects on consumer welfare for both mergers in which the positive frequency effect dominates. In the short run, both mergers have little impact on consumer welfare. In the long run, however, travelers gain from both mergers. In chapter two, we design a novel quasi-experiment approach, DID-Matching with regression adjustment approach, to estimate the effects of Low-Cost Carriers’ (LCCs) expansions on fares. We decompose the overall effect of LCC entry into three effects: actual entry, potential entry and adjacent entry. For each type of entry, we select treated routes to exclude the contamination of other types of entry. The controlled routes are matched from routes that were entered by the same LCC in later years. We find that LCC entry caused at most a 20% and 30% price drop in EU and U.S. markets, respectively. In EU markets, fare reductions are mainly caused by LCCs’ actual entries. Potential entries can cause big price drop in U.S. markets. Our results also imply that concerns about market consolidation can be reduced if cabotage rights were granted. Chapter three investigates the performance of consumer reviews in addressing endogeneity in discrete choice models with an application to individuals’ choices among smartphone options. Instead of identifying unobserved product quality as a scalar, I explicitly express the control variable as a function of feature sentiments, which are extracted from reviews using a machine learning technique. I find that the estimated price coefficient is biased in the positive direction without endogeneity correction while it is adjusted in the expected way after including review variables. The findings indicate that consumer reviews provide alternative sources of information in dealing with endogeneity.

Hypertension (HTN) and osteoarthritis (OA) are frequent in middle-aged and elderly people, and the co-occurrence of these two diseases is common. However, the pathogenesis of the multimorbidity of both diseases and the relation with sleep quality, hyperlipemia, and hyperglycemia is unclear. We conducted a cross-sectional study to make sense of the multimorbidity of HTN and OA and the relation with sleep quality, hyperlipemia, and hyperglycemia. The relation between sleep quality and OA and its joint effect with hyperlipemia or hyperglycemia was evaluated with logistic regression models. The additive interaction was assessed with the relative excess risk due to interaction (REEI), the attributable proportion (AP), and the synergy index (S). According to this research in a remote rural area, approximately 34.2% of HTN patients are accompanied with OA and 49.1% are suffering poor sleep. Both hyperlipemia/hyperglycemia and sleep quality were related to OA prevalence with crude ORs of 1.43 (95% CI 1.014–2.029) and 1.89 (95% CI 1.411–2.519, P  < 0.001) respectively. An observed additive effect was found greater than the sum of the effects of sleep quality and hyperlipemia/hyperglycemia posed on OA prevalence alone. This additive interaction was observed in females (OR = 3.19, 95% CI 1.945–5.237) as well as males ≥ 65 years old (OR = 2.78, 95% CI 1.693–4.557), with RERI, AP, and S significant. Therefore, poor sleep and hyperlipemia/hyperglycemia are associated with OA, and further studies on the additive interaction among females and males ≥ 65 are warranted.

Traditional statistical models for debris-flow susceptibility often overlook critical triggering mechanisms and geotechnical parameters. To address this, we propose an innovative framework that couples the Maximum Entropy (MaxEnt) statistical model with the TRIGRS physical model, which simulates transient rainfall infiltration and grid-based regional slope stability. Focusing on seven towns in Beichuan County, China, we integrated thirteen environmental factors, geotechnical parameters, and historical hazard records to build a dual-driven “statistical–physical” evaluation framework. Our methodology consists of three steps: (1) Use TRIGRS to compute rainfall-induced safety factors (FS) and identify unstable zones (FS < 1), which serve as the positive-sample database for MaxEnt; (2) Employ the MaxEnt model—using the TRIGRS-derived positive samples and historical debris-flow factors—to predict the spatial distribution of susceptibility; (3) Integrate both outputs spatially in GIS using dynamic weighting. Validation shows that the hybrid model improves prediction accuracy by 21% compared to MaxEnt alone (AUC = 0.845). Its susceptibility map corrects 34.7% of the overpredicted areas from the statistical model and enlarges stable zones by 1.8 times. Additionally, to determine the optimal weighting between machine learning and the physical model, we tested three weight combinations and found that a 0.55:0.45 ratio (MaxEnt: TRIGRS) yields the best performance. Using an independent validation set from another study area, we correctly identified 83.6% of the historical debris-flow events in Changtan, demonstrating the framework’s ability to integrate geostatistical patterns with geomechanical processes. This coupled framework offers a paradigm for multi‐hazard chain assessment in complex terrain and can be directly applied to debris-flow early warning and regional disaster mitigation planning.

Background The CRISPR/Cas12a and CRISPR/Cas13d systems are widely used for fundamental research and hold great potential for future clinical applications. However, the short half-life of guide RNAs (gRNAs), particularly free gRNAs without Cas nuclease binding, limits their editing efficiency and durability. Results Here, we engineer circular free gRNAs (cgRNAs) to increase their stability, and thus availability for Cas12a and Cas13d processing and loading, to boost editing. cgRNAs increases the efficiency of Cas12a-based transcription activators and genomic DNA cleavage by approximately 2.1- to 40.2-fold for single gene editing and 1.7- to 2.1-fold for multiplexed gene editing than their linear counterparts, without compromising specificity, across multiple sites and cell lines. Similarly, the RNA interference efficiency of Cas13d is increased by around 1.8-fold. In in vivo mouse liver, cgRNAs are more potent in activating gene expression and cleaving genomic DNA. Conclusions CgRNAs enable more efficient programmable DNA and RNA editing for Cas12a and Cas13d with broad applicability for fundamental research and gene therapy.

Prefabricated buildings are widely utilized due to their effectiveness in reducing carbon emissions. The construction stage has a significantly higher carbon emission rate than the other stages of their life cycle, but this is difficult to accurately quantify and predict due to the high variability. This study clarifies the system boundary of carbon emissions and the parameters of influence in carbon emissions predictions. The carbon emission quantification model was improved by using the process analysis method and the carbon emission factor method, and a modular calculation formula was proposed. Based on the machine learning algorithm, a carbon emissions prediction model for prefabricated buildings’ construction stage was established and hyperparameter optimization was conducted. A sample database for predicting prefabricated buildings’ carbon emissions during the construction stage was established using a modular quantification method, and the thin plate spline interpolation algorithm was introduced to expand this. The prediction results of carbon emission prediction models using four algorithms, SVR, BPNN, ELM, and RF, were compared and analyzed by RMSE and R2. The results show that the model based on BPNN has the highest prediction accuracy when determining the carbon emissions of prefabricated building during the construction stage, and this method can provide a more accurate reference for subsequent quantitative research on carbon emissions from prefabricated buildings.

Introduction Radiation-induced intestinal injury (IR) is a common complication in patients with colorectal cancer (CRC) after radiotherapy. Currently, the search for effective treatments has become an urgent priority. Nicotinamide riboside (NR), the precursor of vitamin B3 derivative and NAD (+), was enriched in IR after treatment with commensal Bacteroides fragilis , suggesting that NR may perform a protective role. However, the specific role of NR in IR remains unclear. In this study, we investigated the therapeutic effects of NR on IR. Methods C57BL/6J, Gsdme ΔIEC (lacking GSDME in intestinal epithelial cells, IECs), and wild-type littermates were treated with NR after abdominal radiation exposure at 10 Gy. The small intestinal crypt-derived organoids and cell lines FHC, NCM460, and IEC-6 were irradiated with or without administration of the SIRT1 inhibitor EX527 under NR stimulation. A series of assays were subsequently carried out to explore the therapeutic effects, the degree of IECs repair, and the underlying mechanism. Results NR significantly ameliorated IR in mice, as evidenced by reduced weight loss, shortened small intestinal and colonic length, improved histological associated index, and restored the intestinal flora, which increased the relative abundance of the commensal Muribaculaceae , and decreased the relative abundance of Escherichia_coli . Additionally, NR increased goblet cell density, increased crypt length, promoted Ki-67 and ZO-1 expression, reduced reactive oxygen species (ROS) levels, and maintained mitochondrial morphology in IECs. Mechanistically, NR activated SIRT1 and inhibited gasdermin E (GSDME)-induced pyroptosis. Further investigations using an experimental Gsdme ΔIEC mouse model (lacking GSDME in IECs) and a SIRT1 inhibitor revealed that NR promoted intestinal barrier repair through GSDME inhibition in a SIRT1-dependent manner. Compared with those in healthy controls, the GSDME level in radiation-injured colorectal mucosa from CRC patients after radiotherapy increased with the downregulation of SIRT1, Ki-67, and ZO-1. Conclusion Our study demonstrated that NR mitigates IR by inhibiting GSDME-mediated pyroptosis and restoring IECs homeostasis. These findings suggest that NR could serve as a promising therapeutic strategy for IR following radiotherapy.

Background The Western dietary pattern, characterized by high consumption of fats and sugars, has been strongly associated with an increased risk of developing Crohn’s disease (CD). However, the potential impact of maternal obesity or prenatal exposure to a Western diet on offspring’s susceptibility to CD remains unclear. Herein, we investigated the effects and underlying mechanisms of a maternal high-fat/high-sugar Western-style diet (WD) on offspring’s susceptibility to 2,4,6-Trinitrobenzenesulfonic acid (TNBS)-induced Crohn’s-like colitis. Methods Maternal dams were fed either a WD or a normal control diet (ND) for eight weeks prior to mating and continued throughout gestation and lactation. Post-weaning, the offspring were subjected to WD and ND to create four groups: ND-born offspring fed a normal diet (N–N) or Western diet (N-W), and WD-born offspring fed a normal (W–N) or Western diet (W-W). At eight weeks of age, they were administered TNBS to induce a CD model. Results Our findings revealed that the W–N group exhibited more severe intestinal inflammation than the N–N group, as demonstrated by a lower survival rate, increased weight loss, and a shorter colon length. The W–N group displayed a significant increase in Bacteroidetes , which was accompanied by an accumulation of deoxycholic acid (DCA). Further experimentation confirmed an increased generation of DCA in mice colonized with gut microbes from the W–N group. Moreover, DCA administration aggravated TNBS-induced colitis by promoting Gasdermin D (GSDMD)-mediated pyroptosis and IL-1beta (IL-1β) production in macrophages. Importantly, the deletion of GSDMD effectively restrains the effect of DCA on TNBS-induced colitis. Conclusions Our study demonstrates that a maternal Western-style diet can alter gut microbiota composition and bile acid metabolism in mouse offspring, leading to an increased susceptibility to CD-like colitis. These findings highlight the importance of understanding the long-term consequences of maternal diet on offspring health and may have implications for the prevention and management of Crohn’s disease. 5QpfrGmZ82qRXNutofRZ7n Video Abstract

Key MessageAhRt1 controlling red testa color in peanut was fine-mapped to an interval of 580 kb on chromosome A03, and one gene encoding bHLH transcriptional factor was identified as the putative candidate gene.Peanut with red testa has higher nutritional and economic value than the traditional pink testa varieties. Identification of genes controlling red testa color will accelerate the breeding program and facilitate uncovering the genetic mechanism. In this study, in order to identify gene underlying the red testa color in peanut, a F2 population derived from a cross between a pink testa peanut variety “Fuhua 8” and a red testa variety “Quanhonghua 1” was constructed. The genetic analysis for the F2 population revealed that the red testa color was controlled by one single dominant locus. This locus, named as AhRt1 (Arachis hypogaea Red Testa 1), was preliminary identified in chromosome A03 by BSA-sequencing analysis. Using a segregation mapping population, AhRt1 was fine-mapped to a 580-kb genomic region by substitution mapping strategy. Gene candidate analysis suggested that one predicted gene encoding bHLH transcriptional factor may be the possible candidate gene for AhRt1. A diagnostic marker closely linked to candidate gene has been developed for validating the fine-mapping result in different populations and peanut germplasm. Our findings will benefit the breeding program for developing new varieties with red testa color and laid foundation for map-based cloning gene responsible for red testa in peanut.