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Background: Increasing evidence suggests that heroin addiction may be related to the dysfunction among the triple brain network (default mode network [DMN], salience network [SN] and executive control network [ECN]). However, the characteristics of glucose metabolism and metabolic connectivity among core regions of the triple brain network remain unknown. Therefore, we hypothesized that individuals with heroin dependence would show abnormal glucose metabolism and accompanied abnormal metabolic connectivity within the triple brain network. Methods: Individuals with heroin dependence and healthy controls matched for age and sex underwent integrated positron emission tomography/magnetic resonance imaging (PET/MRI). Differences in glucose metabolism and metabolic connectivity among the DMN, SN and ECN were analyzed based on [sup.18]F-fluorodeoxyglucose PET and resting-state fMRI data. Results: We included 36 individuals with heroin dependence and 30 matched healthy controls in our study. The heroin dependence group showed a significant reduction of glucose metabolism in the bilateral anterior insula (AI) and inferior parietal lobule (IPL), and a significantly decreased metabolic connectivity between the right AI and the left dorsolateral prefrontal cortex (DLPFC). The daily dose of methadone was negatively correlated with glucose metabolism of the right AI and right IPL. Limitations: The results revealed the glucose metabolism alterations and metabolic connectivity only within the triple brain network in individuals with heroin dependence; additional brain networks should be investigated in future studies. Although methadone is an opioid with a similar neurophysiological mechanism as heroin, the specific chronic effects of methadone on cerebral metabolism and metabolic connectivity should also be investigated in future studies. Conclusion: Our findings suggest that long-term opioid use might, to some extent, be associated with reduced synergistic ability between the SN and ECN, which may be associated with the dysfunction of cognitive control. In particular, the right AI, which showed hypometabolism and related reduction in SN-ECN metabolic connectivity, should receive increasing attention in future studies., Introduction Heroin addiction is characterized by excessive drug-seeking and drug-taking behaviours despite serious adverse consequences, and it manifests clinically as a chronic relapsing cycle of binging, intoxication, withdrawal and craving. [...]

The deep decision tree transfer learning Boosting method (DTrBoost) can only adapt to the training data of one source domain and one target domain, and can not adapt to the samples of several different distribution source domains. In addition, the DTrBoost method synchronously learns data from the source domain to the target domain model, and does not quantify the weight of the learned knowledge according to the degree of importance. In practice, the distribution of data divided according to one or some characteristics of a certain dataset is often inconsistent, the importance of these different distributions to the final model is also inconsistent, and the weight of knowledge transfer is therefore not equal. To solve this problem, a transfer learning method of multi-source domain weight optimization is proposed. The main idea is to calculate the KL divergence distance to the target domain according to the source domain space of different distributions, and calculate the learning weight proportion parameters of the source domain samples of different distributions by using the ratio of KL divergence, so as to optimize the overall gradient function and make the learning direction towards the direction of the fastest gradient decline. The gradient descent algorithm can make the model converge quickly, and ensure the learning speed as well as the transfer learning effect. Experimental results show that the algorithm proposed in this paper adaptively achieves better average performance on the whole. The average classification error rate on all the adopted datasets decreases by 0.013 and even 0.030 on OCR dataset.

The conserved MAP3K Dual-Leucine-Zipper Kinase (DLK) and Leucine-Zipper-bearing Kinase (LZK) can activate JNK via MKK4 or MKK7. These two MAP3Ks share similar biochemical activities and undergo auto-activation upon increased expression. Depending on cell-type and nature of insults DLK and LZK can induce pro-regenerative, pro-apoptotic or pro-degenerative responses, although the mechanistic basis of their action is not well understood. Here, we investigated these two MAP3Ks in cerebellar Purkinje cells using loss- and gain-of function mouse models. While loss of each or both kinases does not cause discernible defects in Purkinje cells, activating DLK causes rapid death and activating LZK leads to slow degeneration. Each kinase induces JNK activation and caspase-mediated apoptosis independent of each other. Significantly, deleting CELF2, which regulates alternative splicing of Map2k7, strongly attenuates Purkinje cell degeneration induced by LZK, but not DLK. Thus, controlling the activity levels of DLK and LZK is critical for neuronal survival and health.

Designing a ship hull to keep the hydrodynamic performance meet the requirements is an indispensable step in the ship's preliminary design. However, in the traditional design process, the design workload is labor-intensive and time-consuming due to the need to estimate a large number of design schemes. To address this issue, this work developed a Neural Network Proxy Model (NNPM) to assist the hydrodynamic resistance prediction for the ship hull design process. The data used in the NNPM construction was supported by two Computational Fluid Dynamic (CFD) methods with different fidelity where the low-fidelity one for tuning and pre-training, and the high-fidelity one for fine-training. To mitigate the risk of overfitting stemming from disparities in data volumes, the parameter freezing strategy is adopted during the high-fidelity dataset-based fine-training. The results obtained from validation numerical experiments indicated that the NNPM exhibited a forecast error of less than 1% when compared to the high-fidelity CFD method. Importantly, this high accuracy is achieved while maintaining a low construction workload, demonstrating the potential of Artificial Intelligence (AI) to predict the hydrodynamic load of hulls in the ship's preliminary design, which can further advance the artificial intelligence-assisted design (AIAD) technology for various marine structures. [ABSTRACT FROM AUTHOR]

Neural circuits utilize a coordinated cellular machinery to form and eliminate synaptic connections, with the neuronal cytoskeleton playing a prominent role. During larval development of Caenorhabditis elegans, synapses of motor neurons are stereotypically rewired through a process facilitated by dynamic microtubules (MTs). Through a genetic suppressor screen on mutant animals that fail to rewire synapses, and in combination with live imaging and ultrastructural studies, we find that intermediate filaments (IFs) stabilize MTs to prevent synapse rewiring. Genetic ablation of IFs or pharmacological disruption of IF networks restores MT growth and rescues synapse rewiring defects in the mutant animals, indicating that IF accumulation directly alters MT stability. Our work sheds light on the impact of IFs on MT dynamics and axonal transport, which is relevant to the mechanistic understanding of several human motor neuron diseases characterized by IF accumulation in axonal swellings.

Reactive astrocytes influence post-injury recovery, repair, and pathogenesis of the mammalian CNS. Much of the regulation of astrocyte reactivity, however, remains to be understood. Using genetic loss and gain-of-function analyses in vivo, we show that the conserved MAP3K13 (also known as leucine zipper-bearing kinase [LZK]) promotes astrocyte reactivity and glial scar formation after CNS injury. Inducible LZK gene deletion in astrocytes of adult mice reduced astrogliosis and impaired glial scar formation, resulting in increased lesion size after spinal cord injury. Conversely, LZK overexpression in astrocytes enhanced astrogliosis and reduced lesion size. Remarkably, in the absence of injury, LZK overexpression alone induced widespread astrogliosis in the CNS and upregulated astrogliosis activators pSTAT3 and SOX9. The identification of LZK as a critical cell-intrinsic regulator of astrocyte reactivity expands our understanding of the multicellular response to CNS injury and disease, with broad translational implications for neural repair.