Model-based dynamical properties analysis of a motorized spindle system with an adjustable preload mechanism

This paper presents a dynamical model for an especially designed motorized spindle with an adjustable preload mechanism and analyzes the effects of bearing preload on the spindle dynamical properties in both of the non-working and working states. In the model, the housing, rear bearing pedestal, shaft, drawbar and tool are taken into account using the finite element (FE) method. The effects of bearing preload are provided by this mathematical model as well as the experiments, in which the axial displacement of spindle tool, frequency response function (FRF), vibration displacement etc. are measured under all kinds of operating conditions. Various results such as bearing nonlinear stiffness, inherent modal shapes and frequencies of the system, spindle stiffness and chatter stability have been obtained under different preload. The good agreement between the calculated results and the tested data indicates that the model is capable of predicting the dynamical properties of the motorized spindle system accurately. And it is indicated that choosing an appropriate bearing preload can contribute to acquire good dynamical properties for the motorized spindle

MR imaging and outcome in neonatal HIBD models are correlated with sex: the value of diffusion tensor MR imaging and diffusion kurtosis MR imaging

ObjectiveHypoxic-ischemic encephalopathy can lead to lifelong morbidity and premature death in full-term newborns. Here, we aimed to determine the efficacy of diffusion kurtosis (DK) [mean kurtosis (MK)] and diffusion tensor (DT) [fractional anisotropy (FA), mean diffusion (MD), axial diffusion (AD), and radial diffusion (RD)] parameters for the early diagnosis of early brain histopathological changes and the prediction of neurodegenerative events in a full-term neonatal hypoxic-ischemic brain injury (HIBD) rat model.MethodsThe HIBD model was generated in postnatal day 7 Sprague-Dawley rats to assess the changes in DK and DT parameters in 10 specific brain structural regions involving the gray matter, white matter, and limbic system during acute (12 h) and subacute (3 d and 5 d) phases after hypoxic ischemia (HI), which were validated against histology. Sensory and cognitive parameters were assessed by the open field, novel object recognition, elevated plus maze, and CatWalk tests.ResultsRepeated-measures ANOVA revealed that specific brain structures showed similar trends to the lesion, and the temporal pattern of MK was substantially more varied than DT parameters, particularly in the deep gray matter. The change rate of MK in the acute phase (12 h) was significantly higher than that of DT parameters. We noted a delayed pseudo-normalization for MK. Additionally, MD, AD, and RD showed more pronounced differences between males and females after HI compared to MK, which was confirmed in behavioral tests. HI females exhibited anxiolytic hyperactivity-like baseline behavior, while the memory ability of HI males was affected in the novel object recognition test. CatWalk assessments revealed chronic deficits in limb gait parameters, particularly the left front paw and right hind paw, as well as poorer performance in HI males than HI females.ConclusionsOur results suggested that DK and DT parameters were complementary in the immature brain and provided great value in assessing early tissue microstructural changes and predicting long-term neurobehavioral deficits, highlighting their ability to detect both acute and long-term changes. Thus, the various diffusion coefficient parameters estimated by the DKI model are powerful tools for early HIBD diagnosis and prognosis assessment, thus providing an experimental and theoretical basis for clinical treatment

IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers

Data augmentation has been proven effective for training high-accuracy convolutional neural network classifiers by preventing overfitting. However, building deep neural networks in real-world scenarios requires not only high accuracy on clean data but also robustness when data distributions shift. While prior methods have proposed that there is a trade-off between accuracy and robustness, we propose IPMix, a simple data augmentation approach to improve robustness without hurting clean accuracy. IPMix integrates three levels of data augmentation (image-level, patch-level, and pixel-level) into a coherent and label-preserving technique to increase the diversity of training data with limited computational overhead. To further improve the robustness, IPMix introduces structural complexity at different levels to generate more diverse images and adopts the random mixing method for multi-scale information fusion. Experiments demonstrate that IPMix outperforms state-of-the-art corruption robustness on CIFAR-C and ImageNet-C. In addition, we show that IPMix also significantly improves the other safety measures, including robustness to adversarial perturbations, calibration, prediction consistency, and anomaly detection, achieving state-of-the-art or comparable results on several benchmarks, including ImageNet-R, ImageNet-A, and ImageNet-O.Comment: NeurIPS 202

Superhumps in a Peculiar SU UMa-Type Dwarf Nova ER Ursae Majoris

We report the photometry of a peculiar SU UMa-type dwarf nova - ER UMa for ten nights during 1998 December and 1999 March covering a complete rise to the supermaximum and a normal outburst cycle. Superhumps have been found during the rise to the superoutburst. A negative superhump appeared in Dec.22 light curve, while the superhump on the next night became positive and had large amplitude and distinct waveform from that of the previous night. In the normal outburst we captured, superhumps with larger or smaller amplitudes seem to always exist, although it is not necessarily true for every normal outburst. These results show great resemblance with V1159 Ori (Patterson et al. 1995). It is more likely that superhumps occasionally exist at essentially all phases of the eruption cycles of ER UMa stars, which should be considered in modeling.Comment: 4 pages, 5 figures, Accepted by ApJ Letter

Minimal Turing Test and Children's Education

Considerable evidence proves that causal learning and causal understanding greatly enhance our ability to manipulate the physical world and are major factors that distinguish humans from other primates. How do we enable unintelligent robots to think causally, answer the questions raised with "why" and even understand the meaning of such questions? The solution is one of the keys to realizing artificial intelligence. Judea Pearl believes that to achieve human-like intelligence, researchers must start by imitating the intelligence of children, so he proposed a "causal inference engine" to help future artificial intelligence make causal inference, pass the Minimal Turing Test, and even become a moral subject who can discern good from evil. This study attempts to provide some insights into the development of children's education from basic assumptions and construction goals of artificial intelligence, and to reflect on the causal model of artificial intelligence through children's education

Application of TBSS-based machine learning models in the diagnosis of pediatric autism

ObjectiveTo explore the microstructural changes of white matter in children with pediatric autism by using diffusion kurtosis imaging (DKI), and evaluate whether the combination of tract-based spatial statistics (TBSS) and back-propagation neural network (BPNN)/support vector machine (SVM)/logistic regression (LR) was feasible for the classification of pediatric autism.MethodsDKI data were retrospectively collected from 32 children with autism and 27 healthy controls (HCs). Kurtosis fractional anisotropy (FAK), mean kurtosis (MK), axial kurtosis (KA), radial kurtosis (RK), fractional anisotropy (FA), axial diffusivity (DA), mean diffusivity (MD) and Radial diffusivity (DR) were generated by iQuant workstation. TBSS was used to detect the regions of parameters values abnormalities and for the comparison between these two groups. In addition, we also introduced the lateralization indices (LI) to study brain lateralization in children with pediatric autism, using TBSS for additional analysis. The parameters values of the differentiated regions from TBSS were then calculated for each participant and used as the features in SVM/BPNN/LR. All models were trained and tested with leave-one-out cross validation (LOOCV).ResultsCompared to the HCs group, the FAK, DA, and KA values of multi-fibers [such as the bilateral superior longitudinal fasciculus (SLF), corticospinal tract (CST) and anterior thalamic radiation (ATR)] were lower in pediatric autism group (p < 0.05, TFCE corrected). And we also found DA lateralization abnormality in Superior longitudinal fasciculus (SLF) (the LI in HCs group was higher than that in pediatric autism group). However, there were no significant differences in FA, MD, MK, DR, and KR values between HCs and pediatric autism group (P > 0.05, TFCE corrected). After performing LOOCV to train and test three model (SVM/BPNN/LR), we found the accuracy of BPNN (accuracy = 86.44%) was higher than that of LR (accuracy = 76.27%), but no different from SVM (RBF, accuracy = 81.36%; linear, accuracy = 84.75%).ConclusionOur proposed method combining TBSS findings with machine learning (LR/SVM/BPNN), was applicable in the classification of pediatric autism with high accuracy. Furthermore, the FAK, DA, and KA values and Lateralization index (LI) value could be used as neuroimaging biomarkers to discriminate the children with pediatric autism or not

Treatment with paeoniflorin increases lifespan of Pseudomonas aeruginosa infected Caenorhabditis elegans by inhibiting bacterial accumulation in intestinal lumen and biofilm formation

Paeoniflorin is one of the important components in Paeoniaceae plants. In this study, we used Caenorhabditis elegans as a model host and Pseudomonas aeruginosa as a bacterial pathogen to investigate the possible role of paeoniflorin treatment against P. aeruginosa infection in the host and the underlying mechanisms. Posttreatment with 1.25–10 mg/L paeoniflorin could significantly increase the lifespan of P. aeruginosa infected nematodes. After the infection, the P. aeruginosa colony-forming unit (CFU) and P. aeruginosa accumulation in intestinal lumen were also obviously reduced by 1.25–10 mg/L paeoniflorin treatment. The beneficial effects of paeoniflorin treatment in increasing lifespan in P. aeruginosa infected nematodes and in reducing P. aeruginosa accumulation in intestinal lumen could be inhibited by RNAi of pmk-1, egl-1, and bar-1. In addition, paeoniflorin treatment suppressed the inhibition in expressions of pmk-1, egl-1, and bar-1 caused by P. aeruginosa infection in nematodes, suggesting that paeoniflorin could increase lifespan of P. aeruginosa infected nematode by activating PMK-1, EGL-1, and BAR-1. Moreover, although treatment with 1.25–10 mg/L paeoniflorin did not show obvious anti-P. aeruginosa activity, the P. aeruginosa biofilm formation and expressions of related virulence genes (pelA, pelB, phzA, lasB, lasR, rhlA, and rhlC) were significantly inhibited by paeoniflorin treatment. Treatment with 1.25–10 mg/L paeoniflorin could further decrease the levels of related virulence factors of pyocyanin, elastase, and rhamnolipid. In addition, 2.5–10 mg/L paeoniflorin treatment could inhibit the swimming, swarming, and twitching motility of P. aeruginosa, and treatment with 2.5–10 mg/L paeoniflorin reduced the cyclic-di-GMP (c-di-GMP) level. Therefore, paeoniflorin treatment has the potential to extend lifespan of P. aeruginosa infected hosts by reducing bacterial accumulation in intestinal lumen and inhibiting bacterial biofilm formation

Predicting neurodevelopmental outcomes in extremely preterm neonates with low-grade germinal matrix-intraventricular hemorrhage using synthetic MRI

ObjectivesThis study aims to assess the predictive capability of synthetic MRI in assessing neurodevelopmental outcomes for extremely preterm neonates with low-grade Germinal Matrix-Intraventricular Hemorrhage (GMH-IVH). The study also investigates the potential enhancement of predictive performance by combining relaxation times from different brain regions.Materials and methodsIn this prospective study, 80 extremely preterm neonates with GMH-IVH underwent synthetic MRI around 38 weeks, between January 2020 and June 2022. Neurodevelopmental assessments at 18 months of corrected age categorized the infants into two groups: those without disability (n = 40) and those with disability (n = 40), with cognitive and motor outcome scores recorded. T1, T2 relaxation times, and Proton Density (PD) values were measured in different brain regions. Logistic regression analysis was utilized to correlate MRI values with neurodevelopmental outcome scores. Synthetic MRI metrics linked to disability were identified, and combined models with independent predictors were established. The predictability of synthetic MRI metrics in different brain regions and their combinations were evaluated and compared with internal validation using bootstrap resampling.ResultsElevated T1 and T2 relaxation times in the frontal white matter (FWM) and caudate were significantly associated with disability (p < 0.05). The T1-FWM, T1-Caudate, T2-FWM, and T2-Caudate models exhibited overall predictive performance with AUC values of 0.751, 0.695, 0.856, and 0.872, respectively. Combining these models into T1-FWM + T1-Caudate + T2-FWM + T2-Caudate resulted in an improved AUC of 0.955, surpassing individual models (p < 0.05). Bootstrap resampling confirmed the validity of the models.ConclusionSynthetic MRI proves effective in early predicting adverse outcomes in extremely preterm infants with GMH-IVH. The combination of T1-FWM + T1-Caudate + T2-FWM + T2-Caudate further enhances predictive accuracy, offering valuable insights for early intervention strategies

High-resolution quantification of hepatitis C virus genome-wide mutation load and its correlation with the outcome of peginterferon-alpha2a and ribavirin combination therapy

Hepatitis C virus (HCV) is a highly mutable RNA virus and circulates as a heterogeneous population in individual patients. The magnitude of such population heterogeneity has long been proposed to be linked with diverse clinical phenotypes, including antiviral therapy. Yet data accumulated thus far are fairly inconclusive. By the integration of long RT-PCR with 454 sequencing, we have built a pipeline optimized for the quantification of HCV genome-wide mutation load at 1% resolution of mutation frequency, followed by a retrospective study to examine the role of HCV mutation load in peginterferon-alpha2a and ribavirin combination antiviral therapy. Genome-wide HCV mutation load varied widely with a range from 92 to 1639 mutations and presented a Poisson distribution among 56 patients (Kolmogorov-Smirnov statistic  = 0.078, p = 0.25). Patients achieving sustained virological response (n = 26) had significantly lower mutation loads than that in null responders (n = 30) (mean and standard derivation: 524±279 vs. 805±271, p = 0.00035). All 36,818 mutations detected in 56 patients displayed a power-law distribution in terms of mutation frequency in viral population. The low-frequency mutation load, but not the high-frequency load, was proportional firmly to the total mutation load. In-depth analyses revealed that intra-patient HCV population structure was shaped by multiple factors, including immune pressure, strain difference and genetic drift. These findings explain previous conflicting reports using low-resolution methods and highlight a dominant role of natural selection in response to therapeutic intervention. By attaining its signatures from complex interaction between host and virus, the high-resolution quantification of HCV mutation load predicts outcomes from interferon-based antiviral therapy and could also be a potential biomarker in other clinical settings