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For substation secondary circuit terminal strip wiring, low efficiency, less easy fault detection and inspection, and a variety of other issues, this study proposes a text detection and identification model based on improved YOLOv7-tiny and MAH-CRNN+CTC terminal lines. First, the YOLOv7-tiny target detection model is improved by the introduction of the spatially invariant multi-attention mechanism (SimAM) and the weighted bidirectional feature pyramid network (BiFPN). This also improves the feature enhancements and feature fusion ability of the model, balances various scales of characteristic information, and increases the positioning accuracy of the text test box. Then, a multi-head attention hybrid (MAH) mechanism is implemented to optimize the convolutional recurrent neural network with connectionist temporal classification (CRNN+CTC) so that the model could learn data features with larger weights and increase the recognition accuracy of the model. The findings indicate that the enhanced YOLOv7-tiny model achieves 97.39%, 98.62%, and 95.07% of precision, recall, and mean average precision (mAP), respectively, on the detection dataset. The improved MAH-CRNN+CTC model achieves 91.2% character recognition accuracy in the recognition dataset.

As an essential part of the future national energy system, hydrogen energy has the advantages of clean, long-time scale energy storage and good complementary characteristics to electrical power, making it an important player in the low-carbon transformation of energies. However, the long timescale storage characteristics and tradability of hydrogen energy are rarely considered in existing capacity allocation optimization methods for hydrogen integrated energy systems(HIES). Meanwhile, the impact of the physical characteristics of the hydrogen storage equipment is rarely considered in the construction of HIES. Therefore, this paper proposes a new capacity allocation method for HIES in industrial park considering hydrogen trading and long-term hydrogen storage. The proposed capacity allocation optimization method is a bilevel mixed-integer linear programming model, which is solved by the reconfiguration decomposition algorithm. The nonlinear constraint problem due to the physical characteristics of the hydrogen storage device is solved by the Big-M method and the binary method. The proposed method can effectively improve the economy of HIES and reduce the cost of hydrogen production. Meanwhile, the reconstruction decomposition algorithm can effectively solve the bilevel mixed integer programing model. Case studies demonstrate that the proposed method can reduce hydrogen production economics by 28%. Considering hydrogen trading, the total investment and operating cost of HIES is reduced by 25%, while long-term hydrogen storage can reduce the cost of hydrogen production for HIES and reduce the impact of hydrogen trading fluctuations.

Metastasis is the primary culprit behind cancer‐related fatalities in multiple cancer types, including prostate cancer. Despite great advances, the precise mechanisms underlying prostate cancer metastasis are far from complete. By using a transgenic mouse prostate cancer model (TRAMP) with and without Phf8 knockout, we have identified a crucial role of PHF8 in prostate cancer metastasis. By complexing with E2F1, PHF8 transcriptionally upregulates SNAI1 in a demethylation‐dependent manner. The upregulated SNAI1 subsequently enhances epithelial‐to‐mesenchymal transition (EMT) and metastasis. Given the role of the abnormally activated PHF8/E2F1‐SNAI1 axis in prostate cancer metastasis and poor prognosis, the levels of PHF8 or the activity of this axis could serve as biomarkers for prostate cancer metastasis. Moreover, targeting this axis could become a potential therapeutic strategy for prostate cancer treatment. © 2024 The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

AbstractBackgroundNeuroinflammation is associated with virtually all major neurodegenerative disorders, including Alzheimer’s disease (AD). Although it remains unclear whether neuroinflammation is the driving force behind these disorders, compelling evidence implicates its role in exacerbating disease progression, with a key player being the potent proinflammatory cytokine TNF-α. Elevated TNF-α levels are commonly detected in the clinic and animal models of AD.MethodsThe potential benefits of a novel TNF-α-lowering agent, 3,6′-dithiothalidomide, were investigated in cellular and rodent models of neuroinflammation with a specific focus on AD. These included central and systemic inflammation induced by lipopolysaccharide (LPS) and Aβ1–42 challenge, and biochemical and behavioral assessment of 3xTg-AD mice following chronic 3,6′-dithiothaliodmide.Results3,6′-Dithiothaliodmide lowered TNF-α, nitrite (an indicator of oxidative damage) and secreted amyloid precursor protein (sAPP) levels in LPS-activated macrophage-like cells (RAW 264.7 cells). This translated into reduced central and systemic TNF-α production in acute LPS-challenged rats, and to a reduction of neuroinflammatory markers and restoration of neuronal plasticity following chronic central challenge of LPS. In mice centrally challenged with Aβ1–42 peptide, prior systemic 3,6′-dithiothalidomide suppressed Aβ-induced memory dysfunction, microglial activation and neuronal degeneration. Chronic 3,6′-dithiothalidomide administration to an elderly symptomatic cohort of 3xTg-AD mice reduced multiple hallmark features of AD, including phosphorylated tau protein, APP, Aβ peptide and Aβ-plaque number along with deficits in memory function to levels present in younger adult cognitively unimpaired 3xTg-AD mice. Levels of the synaptic proteins, SNAP25 and synaptophysin, were found to be elevated in older symptomatic drug-treated 3xTg-AD mice compared to vehicle-treated ones, indicative of a preservation of synaptic function during drug treatment.ConclusionsOur data suggest a strong beneficial effect of 3,6′-dithiothalidomide in the setting of neuroinflammation and AD, supporting a role for neuroinflammation and TNF-α in disease progression and their targeting as a means of clinical management.

Abstract Background Chronic neuroinflammation is a hallmark of several neurological disorders associated with cognitive loss. Activated microglia and secreted factors such as tumor necrosis factor (TNF)-α are key mediators of neuroinflammation and may contribute to neuronal dysfunction. Our study was aimed to evaluate the therapeutic potential of a novel analog of thalidomide, 3,6'-dithiothalidomide (DT), an agent with anti-TNF-α activity, in a model of chronic neuroinflammation. Methods Lipopolysaccharide or artificial cerebrospinal fluid was infused into the fourth ventricle of three-month-old rats for 28 days. Starting on day 29, animals received daily intraperitoneal injections of DT (56 mg/kg/day) or vehicle for 14 days. Thereafter, cognitive function was assessed by novel object recognition, novel place recognition and Morris water maze, and animals were euthanized 25 min following water maze probe test evaluation. Results Chronic LPS-infusion was characterized by increased gene expression of the proinflammatory cytokines TNF-α and IL-1β in the hippocampus. Treatment with DT normalized TNF-α levels back to control levels but not IL-1β. Treatment with DT attenuated the expression of TLR2, TLR4, IRAK1 and Hmgb1, all genes involved in the TLR-mediated signaling pathway associated with classical microglia activation. However DT did not impact the numbers of MHC Class II immunoreactive cells. Chronic neuroinflammation impaired novel place recognition, spatial learning and memory function; but it did not impact novel object recognition. Importantly, treatment with DT restored cognitive function in LPS-infused animals and normalized the fraction of hippocampal neurons expressing the plasticity-related immediate-early gene Arc. Conclusion Our data demonstrate that the TNF-α synthesis inhibitor DT can significantly reverse hippocampus-dependent cognitive deficits induced by chronic neuroinflammation. These results suggest that TNF-α is a critical mediator of chronic neuroinflammation-induced neuronal dysfunction and cognitive impairment and targeting its synthesis could provide an effective therapeutic approach to several human neurodegenerative diseases.

Different techniques have been used to study the reduction process of iron ore. In this paper, the reduction process of iron ore by CO at 200~1200°C is calculated. The effects of reducing agent overdose, reducing temperature and VCO2/(VCO+VCO2) on reducing degree were studied. The results show that the reduction degree increases with the increase of reducing agent and slows down gradually. During the reduction process, the reduction degree decreased significantly with the increase of VCO2/(VCO+VCO2). In this temperature segment, the reduction degree has a peak.