Multiscale dynamically parallel shrinkage network for fault diagnosis of aviation hydraulic pump and its generalizable applications.

Aiming to address the multiscale characteristics and noise corruption problems in the vibration signals of aviation hydraulic pumps, this article develops a novel Multiscale Dynamically Parallel Shrinkage Network (MDPSN) to learn complementary and rich fault-related multiscale features, with the ultimate goal of yielding higher diagnostic accuracy. One significant property is the development of a novel dynamically parallel shrinking module (DPSM) that adaptively generates independent soft thresholds for different scales, effectively shrinking noise-related features to zeros. On one hand, DPSM aggregates and interacts with features at all scales to construct a global feature representation containing richer fault-related information, which is served as the foundation for soft thresholding generation, significantly improving the accuracy and rationality of the generated thresholds. On the other hand, DPSM can adaptively generate individual soft threshold for each scale, allowing each scale to use an independent threshold tailored to its own characteristic to eliminate noise-related information. This avoids the issues of over-denoising or under-denoising caused by the uniform application of thresholds across all scales. Finally, the effectiveness of MDPSN is validated by a series of experiment comparisons on an aviation hydraulic pump dataset and two bearing datasets with various types of noise. The experimental results demonstrate that MDPSN achieves superior diagnostic accuracy compared to five other comparison methods.

Dynamic genomic changes in methotrexate-resistant human cancer cell lines beyond DHFR amplification suggest potential new targets for preventing drug resistance.

BACKGROUND: Although DHFR gene amplification has long been known as a major mechanism for methotrexate (MTX) resistance in cancer, the early changes and detailed development of the resistance are not yet fully understood. METHODS: We performed genomic, transcriptional and proteomic analyses of human colon cancer cells with sequentially increasing levels of MTX-resistance. RESULTS: The genomic amplification evolved in three phases (pre-amplification, homogenously staining region (HSR) and extrachromosomal DNA (ecDNA)). We confirm that genomic amplification and increased expression of DHFR, with formation of HSRs and especially ecDNAs, is the major driver of resistance. However, DHFR did not play a detectable role in the early phase. In the late phase (ecDNA), increase in FAM151B protein level may also have an important role by decreasing sensitivity to MTX. In addition, although MSH3 and ZFYVE16 may be subject to different posttranscriptional regulations and therefore protein expressions are decreased in ecDNA stages compared to HSR stages, they still play important roles in MTX resistance. CONCLUSION: The study provides a detailed evolutionary trajectory of MTX-resistance and identifies new targets, especially ecDNAs, which could help to prevent drug resistance. It also presents a proof-of-principal approach which could be applied to other cancer drug resistance studies.

Improving prediction of N2O emissions during composting using model-agnostic meta-learning.

Nitrous oxide (N2O) represents a significant environmental challenge as a harmful, long-lived greenhouse gas that contributes to the depletion of stratospheric ozone and exacerbates global anthropogenic greenhouse warming. Composting is considered a promising and economically feasible strategy for the treatment of organic waste. However, recent research indicates that composting is a source of N2O, contributing to atmospheric pollution and greenhouse effect. Consequently, there is a need for the development of effective, cost-efficient methodologies to quantify N2O emissions accurately. In this study, we employed the model-agnostic meta-learning (MAML) method to improve the performance of N2O emissions prediction during manure composting. The highest R2 and lowest root mean squared error (RMSE) values achieved were 0.939 and 18.42 mg d-1, respectively. Five machine learning methods including the backpropagation neural network, extreme learning machine, integrated machine learning method based on ELM and random forest, gradient boosting decision tree, and extreme gradient boosting were adopted for comparison to further demonstrate the effectiveness of the MAML prediction model. Feature analysis showed that moisture content of structure material and ammonium concentration during composting process were the two most significant features affecting N2O emissions. This study serves as proof of the application of MAML during N2O emissions prediction, further giving new insights into the effects of manure material properties and composting process data on N2O emissions. This approach helps determining the strategies for mitigating N2O emissions.

Association between BMI and osteoporotic fractures at different sites in Chinese women: a case-control retrospective study in Changsha.

BACKGROUND: Osteoporotic fractures are a growing problem in an aging society. The association between body mass index (BMI) and osteoporotic fractures varies by fracture site and ethnicity. Limited knowledge exists regarding this association in native Chinese, particularly utilizing local databases as reference sources. OBJECTIVE: To investigate the association between BMI and osteoporotic fractures at different sites in Chinese women. METHODS: Three thousand ninety-eight female patients with radiographic fractures and 3098 age- and sex-matched healthy controls without fractures were included in the study. Both of them underwent assessment using dual-energy X-ray absorptiometry (DXA), with BMD measurements calculated using our own BMD reference database. Participants were classified into underweight (BMI < 18.5 kg/m2), normal weight (18.5 ≤ BMI < 24.0 kg/m2), overweight (24 ≤ BMI < 28 kg/m2) and obese (BMI ≥ 28 kg/m2) according to the Chinese BMI classification standard. RESULTS: There were 2296 (74.1%) vertebral fractures, 374 (12.1%) femoral neck fractures, and 428 (13.8%) other types of fractures in the case group. Bone mineral density (BMD) was almost lower in the fracture groups compared to the control groups (p = 0.048 to < 0.001). Compared with normal weight, underweight had a protective effect on total [odds ratio (OR) = 0.61; 95% confidence interval (CI), 0.49 -0.75; P< 0.001], and lumbar fractures (OR = 0.52; 95% CI, 0.41 - 0.67; P < 0.001), while obesity was associated with an increased risk for total (OR = 2.26; 95% CI, 1.85 - 2.76; P < 0.001), lumbar (OR = 2.17; 95% CI, 1.72 - 2.73; P < 0.001), and femoral neck fractures (OR = 4.08; 95% CI, 2.18 - 7.63; P < 0.001). Non-linear associations were observed between BMI and fractures: A J-curve for total, lumbar, and femoral neck fractures, and no statistical change for other types of fractures. Underweight was found to be a risk factor for other types of fracturess after adjusting for BMD (OR = 2.29; 95% CI, 1.09 - 4.80; P < 0.001). Osteoporosis and osteopenia were identified as risk factors for almost all sites of fracture when compared to normal bone mass. CONCLUSIONS: Underweight has a protective effect on total and lumbar spine fractures in Chinese women, while obesity poses a risk factor for total, lumbar, and femoral neck fractures. The effect of BMI on fractures may be mainly mediated by BMD.

Low Selenium and Low Protein Exacerbate Myocardial Damage in Keshan Disease by Affecting the PINK1/Parkin-mediated Mitochondrial Autophagy Pathway.

OBJECTIVE: Keshan disease (KD) is a myocardial mitochondrial disease closely related to insufficient selenium (Se) and protein intake. PTEN induced putative kinase 1 (PINK1)/Parkin mediated mitochondrial autophagy regulates various physiological and pathological processes in the body. This study aimed to elucidate the relationship between PINK1/Parkin-regulated mitochondrial autophagy and KD-related myocardial injury. METHODS: A low Se and low protein animal model was established. One hundred Wistar rats were randomly divided into 5 groups (control group, low Se group, low protein group, low Se + low protein group, and corn from KD area group). The JC-1 method was used to detect the mitochondrial membrane potential (MMP). ELISA was used to detect serum creatine kinase MB (CK-MB), cardiac troponin I (cTnI), and mitochondrial-glutamicoxalacetic transaminase (M-GOT) levels. RT-PCR and Western blot analysis were used to detect the expression of PINK1, Parkin, sequestome 1 (P62), and microtubule-associated proteins1A/1B light chain 3B (MAP1LC3B). RESULTS: The MMP was significantly decreased and the activity of CK-MB, cTnI, and M-GOT significantly increased in each experimental group (low Se group, low protein group, low Se + low protein group and corn from KD area group) compared with the control group (P<0.05 for all). The mRNA and protein expression levels of PINK1, Parkin and MAP1LC3B were profoundly increased, and those of P62 markedly decreased in the experimental groups compared with the control group (P<0.05 for all). CONCLUSION: Low Se and low protein levels exacerbate myocardial damage in KD by affecting the PINK1/Parkin-mediated mitochondrial autophagy pathway.

Alterations in leukocyte telomere length and mitochondrial DNA copy number in benzene poisoning patients.

OBJECTIVE: The aim of this study is to examine and evaluate the impact of benzene poisoning on the relative content of the mitochondrial MT-ND1 gene and telomere length in individuals with occupational chronic benzene poisoning (CBP) compared to a control group. The study will analyze and gather data on the mitochondrial gene content and telomere length in cases of benzene poisoning, and investigate the relationship with blood routine parameters in order to contribute scientific experimental data for the prevention and treatment of CBP. METHOD: The case group comprised 30 individuals diagnosed with occupational chronic benzene poisoning, whereas the control group consisted of 60 healthy individuals who underwent physical examinations at our hospital concurrently. Blood routine indicators were detected and analyzed, and the PCR method was employed to measure changes in mitochondrial MT-ND1 content and telomere length. Subsequently, a comparison and analysis of the aforementioned indicators was conducted. RESULT: The case group exhibited a higher mitochondrial gene content (median 366.2, IQR 90.0 rate) compared to the control group (median 101.5, IQR 12.0 rate), with a statistically significant difference between the two groups (P < 0.05). Additionally, the case group demonstrated lower white blood cell levels (3.78 ± 1.387 × 109/L) compared to the control group (5.74 ± 1.41 × 109/L), with a significant difference between the two groups (P < 0.05). Furthermore, the case group displayed lower red blood cell levels (3.86 ± 0.65 × 1012/L) compared to the control group (4.89 ± 0.65 × 1012/L), with a significant difference between the two groups (P < 0.05). The hemoglobin level in the case group (113.33 ± 16.34 g/L) was lower than that in the control group (138.22 ± 13.22 g/L). There was a significant difference between the two groups (P < 0.05). Platelet levels in the case group (153.80 ± 58.31 × 109/L) is smaller than the control group (244.92 ± 51.99 × 109/L), there was a significant difference between the two groups (P < 0.05). The average telomere length of the normal control group was 1.451 ± 0.475 (rate); The mean telomere length of individuals in the case group diagnosed with benzene poisoning was determined to be 1.237 ± 0.457 (rate). No significant correlation was observed between telomere length and three blood routine parameters, namely white blood cells (WBC), hemoglobin (HB), and platelets (PLT). However, a significant correlation was found between telomere length and red blood cell count (RBC). Additionally, a negative correlation was observed between mitochondrial gene content and white blood cell count (r = - 0.314, P = 0.026), as well as between mitochondrial gene content and red blood cell count (r = - 0.226, P = 0.032). Furthermore, a negative correlation was identified between mitochondrial gene content and hemoglobin (r = - 0.314, P = 0.028), and platelets (r = - 0.445, P = 0.001). CONCLUSION: Individuals diagnosed with occupational chronic benzene poisoning exhibit a reduction in telomere length and an elevation in the relative content of the mitochondrial MT-ND1 gene. Moreover, a negative correlation is observed between the content of the mitochondrial MT-ND1 gene and four blood routine parameters, namely white blood cells (WBC), red blood cells (RBC), hemoglobin (HB), and platelets (PLT). Consequently, benzene exposure may potentially contribute to the onset of premature aging.