Accelerated degradation modeling considering long-range dependence and unit-to-unit variability

Accelerated degradation testing (ADT) is an effective way to evaluate the reliability and lifetime of highly reliable products. Existing studies have shown that the degradation processes of some products are non-Markovian with long-range dependence due to the interaction with environments. Besides, the degradation processes of products from the same population generally vary from each other due to various uncertainties. These two aspects bring great difficulty for ADT modeling. In this paper, we propose an improved ADT model considering both long-range dependence and unit-to-unit variability. To be specific, fractional Brownian motion (FBM) is utilized to capture the long-range dependence in the degradation process. The unit-to-unit variability among multiple products is captured by a random variable in the degradation rate function. To ensure the accuracy of the parameter estimations, a novel statistical inference method based on expectation maximization (EM) algorithm is proposed, in which the maximization of the overall likelihood function is achieved. The effectiveness of the proposed method is fully verified by a simulation case and a microwave case. The results show that the proposed model is more suitable for ADT modeling and analysis than existing ADT models

FP-PET: Large Model, Multiple Loss And Focused Practice

This study presents FP-PET, a comprehensive approach to medical image segmentation with a focus on CT and PET images. Utilizing a dataset from the AutoPet2023 Challenge, the research employs a variety of machine learning models, including STUNet-large, SwinUNETR, and VNet, to achieve state-of-the-art segmentation performance. The paper introduces an aggregated score that combines multiple evaluation metrics such as Dice score, false positive volume (FPV), and false negative volume (FNV) to provide a holistic measure of model effectiveness. The study also discusses the computational challenges and solutions related to model training, which was conducted on high-performance GPUs. Preprocessing and postprocessing techniques, including gaussian weighting schemes and morphological operations, are explored to further refine the segmentation output. The research offers valuable insights into the challenges and solutions for advanced medical image segmentation

Automated monitoring of early neurobehavioral changes in mice following traumatic brain injury

Traumatic brain injury often causes a variety of behavioral and emotional impairments that can develop into chronic disorders. Therefore, there is a need to shift towards identifying early symptoms that can aid in the prediction of traumatic brain injury outcomes and behavioral endpoints in patients with traumatic brain injury after early interventions. In this study, we used the SmartCage system, an automated quantitative approach to assess behavior alterations in mice during an early phase of traumatic brain injury in their home cages. Female C57BL/6 adult mice were subjected to moderate controlled cortical impact (CCI) injury. The mice then received a battery of behavioral assessments including neurological score, locomotor activity, sleep/wake states, and anxiety-like behaviors on days 1, 2, and 7 after CCI. Histological analysis was performed on day 7 after the last assessment. Spontaneous activities on days 1 and 2 after injury were significantly decreased in the CCI group. The average percentage of sleep time spent in both dark and light cycles were significantly higher in the CCI group than in the sham group. For anxiety-like behaviors, the time spent in a light compartment and the number of transitions between the dark/light compartments were all significantly reduced in the CCI group than in the sham group. In addition, the mice suffering from CCI exhibited a preference of staying in the dark compartment of a dark/light cage. The CCI mice showed reduced neurological score and histological abnormalities, which are well correlated to the automated behavioral assessments. Our findings demonstrate that the automated SmartCage system provides sensitive and objective measures for early behavior changes in mice following traumatic brain injury

FHF2 isoforms differentially regulate Nav1.6-mediated resurgent sodium currents in dorsal root ganglion neurons

Nav1.6 and Nav1.6-mediated resurgent currents have been implicated in several pain pathologies. However, our knowledge of how fast resurgent currents are modulated in neurons is limited. Our study explored the potential regulation of Nav1.6-mediated resurgent currents by isoforms of fibroblast growth factor homologous factor 2 (FHF2) in an effort to address the gap in our knowledge. FHF2 isoforms colocalize with Nav1.6 in peripheral sensory neurons. Cell line studies suggest that these proteins differentially regulate inactivation. In particular, FHF2A mediates long-term inactivation, a mechanism proposed to compete with the open-channel blocker mechanism that mediates resurgent currents. On the other hand, FHF2B lacks the ability to mediate long-term inactivation and may delay inactivation favoring open-channel block. Based on these observations, we hypothesized that FHF2A limits resurgent currents, whereas FHF2B enhances resurgent currents. Overall, our results suggest that FHF2A negatively regulates fast resurgent current by enhancing long-term inactivation and delaying recovery. In contrast, FHF2B positively regulated resurgent current and did not alter long-term inactivation. Chimeric constructs of FHF2A and Navβ4 (likely the endogenous open channel blocker in sensory neurons) exhibited differential effects on resurgent currents, suggesting that specific regions within FHF2A and Navβ4 have important regulatory functions. Our data also indicate that FHFAs and FHF2B isoform expression are differentially regulated in a radicular pain model and that associated neuronal hyperexcitability is substantially attenuated by a FHFA peptide. As such, these findings suggest that FHF2A and FHF2B regulate resurgent current in sensory neurons and may contribute to hyperexcitability associated with some pain pathologies

Ultrafast and Ultralight Network-Based Intelligent System for Real-time Diagnosis of Ear diseases in Any Devices

Traditional ear disease diagnosis heavily depends on experienced specialists and specialized equipment, frequently resulting in misdiagnoses, treatment delays, and financial burdens for some patients. Utilizing deep learning models for efficient ear disease diagnosis has proven effective and affordable. However, existing research overlooked model inference speed and parameter size required for deployment. To tackle these challenges, we constructed a large-scale dataset comprising eight ear disease categories and normal ear canal samples from two hospitals. Inspired by ShuffleNetV2, we developed Best-EarNet, an ultrafast and ultralight network enabling real-time ear disease diagnosis. Best-EarNet incorporates the novel Local-Global Spatial Feature Fusion Module which can capture global and local spatial information simultaneously and guide the network to focus on crucial regions within feature maps at various levels, mitigating low accuracy issues. Moreover, our network uses multiple auxiliary classification heads for efficient parameter optimization. With 0.77M parameters, Best-EarNet achieves an average frames per second of 80 on CPU. Employing transfer learning and five-fold cross-validation with 22,581 images from Hospital-1, the model achieves an impressive 95.23% accuracy. External testing on 1,652 images from Hospital-2 validates its performance, yielding 92.14% accuracy. Compared to state-of-the-art networks, Best-EarNet establishes a new state-of-the-art (SOTA) in practical applications. Most importantly, we developed an intelligent diagnosis system called Ear Keeper, which can be deployed on common electronic devices. By manipulating a compact electronic otoscope, users can perform comprehensive scanning and diagnosis of the ear canal using real-time video. This study provides a novel paradigm for ear endoscopy and other medical endoscopic image recognition applications.Comment: This manuscript has been submitted to Neural Network

Highly localized interactions between sensory neurons and sprouting sympathetic fibers observed in a transgenic tyrosine hydroxylase reporter mouse

<p>Abstract</p> <p>Background</p> <p>Sprouting of sympathetic fibers into sensory ganglia occurs in many preclinical pain models, providing a possible anatomical substrate for sympathetically enhanced pain. However, the functional consequences of this sprouting have been controversial. We used a transgenic mouse in which sympathetic fibers expressed green fluorescent protein, observable in live tissue. Medium and large diameter lumbar sensory neurons with and without nearby sympathetic fibers were recorded in whole ganglion preparations using microelectrodes.</p> <p>Results</p> <p>After spinal nerve ligation, sympathetic sprouting was extensive by 3 days. Abnormal spontaneous activity increased to 15% and rheobase was reduced. Spontaneously active cells had Aαβ conduction velocities but were clustered near the medium/large cell boundary. Neurons with sympathetic basket formations had a dramatically higher incidence of spontaneous activity (71%) and had lower rheobase than cells with no sympathetic fibers nearby. Cells with lower density nearby fibers had intermediate phenotypes. Immunohistochemistry of sectioned ganglia showed that cells surrounded by sympathetic fibers were enriched in nociceptive markers TrkA, substance P, or CGRP. Spontaneous activity began before sympathetic sprouting was observed, but blocking sympathetic sprouting on day 3 by cutting the dorsal ramus in addition to the ventral ramus of the spinal nerve greatly reduced abnormal spontaneous activity.</p> <p>Conclusions</p> <p>The data suggest that early sympathetic sprouting into the sensory ganglia may have highly localized, excitatory effects. Quantitatively, neurons with sympathetic basket formations may account for more than half of the observed spontaneous activity, despite being relatively rare. Spontaneous activity in sensory neurons and sympathetic sprouting may be mutually re-enforcing.</p