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Research on the strength prediction equation and model of cement stabilized macadam mixed with recycled construction waste aggregate
Abstract While the issue of construction waste siege is becoming increasingly serious, the road construction industry is also facing the problem of sand and stone materials shortage, and adding construction waste to the road base can effectively help address both issues. In this work, the effects of 0–9.5 and 9.5–37.5 mm recycled construction waste aggregate (RCWA) content on the mechanical properties of cement-stabilized macadam (CSM) mixed with the RCWA were investigated, with the optimal RCWA content that allows for a strength not lower than that of ordinary CSM determined. Here, the mechanical strength growth law of CSM mixed with RCWA was studied, while a prediction equation and a model of the mechanical strength of the mix were proposed and the attendant reliability verified. The results indicated that the optimal ratio of CSM mixed with RCWA is as follows: 0–9.5 mm RCWA: 9.5–37.5 mm RCWA: 19.5–37.5 mm natural aggregate: 9.5–19.5 mm natural aggregate = 45:20:29:6. The correlation coefficient R 2 of the established strength prediction equation was as high as 0.98, and when the cement content and RCWA content are known, the mechanical strength can be predicted. Meanwhile, the correlation coefficient R 2 of the proposed strength prediction model was as high as 0.99, and when the 7-day mechanical strength of CSM mixed with RCWA is known, the model can be used to predict the mechanical strength at any curing age. This was verified using laboratory tests, and it was found that the deviation between the predicted values and the actual values was small.
Intrinsic electrical activity drives small-cell lung cancer progression
Abstract Elevated or ectopic expression of neuronal receptors promotes tumour progression in many cancer types1,2; neuroendocrine (NE) transformation of adenocarcinomas has also been associated with increased aggressiveness3. Whether the defining neuronal feature, namely electrical excitability, exists in cancer cells and impacts cancer progression remains mostly unexplored. Small-cell lung cancer (SCLC) is an archetypal example of a highly aggressive NE cancer and comprises two major distinct subpopulations: NE cells and non-NE cells4,5. Here we show that NE cells, but not non-NE cells, are excitable, and their action potential firing directly promotes SCLC malignancy. However, the resultant high ATP demand leads to an unusual dependency on oxidative phosphorylation in NE cells. This finding contrasts with the properties of most cancer cells reported in the literature, which are non-excitable and rely heavily on aerobic glycolysis. Additionally, we found that non-NE cells metabolically support NE cells, a process akin to the astrocyte–neuron metabolite shuttle6. Finally, we observed drastic changes in the innervation landscape during SCLC progression, which coincided with increased intratumoural heterogeneity and elevated neuronal features in SCLC cells, suggesting an induction of a tumour-autonomous vicious cycle, driven by cancer cell-intrinsic electrical activity, which confers long-term tumorigenic capability and metastatic potential.
Multiparametric physicochemical analysis of a type 1 collagen 3D cell culture model using light and electron microscopy and mass spectrometry imaging
Abstract Three-dimensional cell culture systems underpin cell-based technologies ranging from tissue scaffolds for regenerative medicine to tumor models and organoids for drug screening. However, to realise the full potential of these technologies requires analytical methods able to capture the diverse information needed to characterize constituent cells, scaffold components and the extracellular milieu. Here we describe a multimodal imaging workflow which combines fluorescence, vibrational and second harmonic generation microscopy with secondary ion mass spectrometry imaging and transmission electron microscopy to analyse the morphological, chemical and ultrastructural properties of cell-seeded scaffolds. Using cell nuclei as landmarks we register fluorescence with label-free optical microscopy images and high mass resolution with high spatial resolution secondary ion mass spectrometry images, with an accuracy comparable to the intrinsic spatial resolution of the techniques. We apply these methods to investigate relationships between cell distribution, cytoskeletal morphology, scaffold fiber organisation and biomolecular composition in type I collagen scaffolds seeded with human dermal fibroblasts.
Gender differences in prolactin thresholds and their association with lactotroph adenoma invasiveness for potential treatment considerations
Abstract Recent trends in first-line transsphenoidal surgery (TSS) for prolactinoma patients aim to reduce long-term dependence on dopamine agonists (DA). Key factors linked to poor surgical outcomes include cavernous sinus invasiveness and high baseline serum prolactin (PRL) levels. Defining simple PRL threshold values to indicate invasiveness and inform treatment strategy is crucial. In this retrospective cohort study of 149 prolactinoma patients treated with first-line transsphenoidal surgery (TSS) or dopamine agonist (DA) therapy, we evaluated preoperative prolactin (PRL) levels and cavernous sinus invasion as factors associated with long-term remission. Bayesian modeling identified cohort-wide and gender-specific PRL thresholds associated with invasiveness. Preoperative PRL values strongly correlated with cavernous sinus invasion (AUROC = 0.95; 95% CI: 0.90–0.98). The cohort-wide PRL threshold was 431.9 µg/L (95% CI: 181.1–708.3 µg/L), with gender-specific thresholds of 280.8 µg/L (95% CI: 51.0–528.2 µg/L) for women and 1325.0 µg/L (95% CI: 667.2–2582.9 µg/L) for men. Female thresholds were lower and less affected by age and obesity, while male thresholds were influenced by these factors, particularly in young, obese men. These findings suggest that gender-specific PRL thresholds may be useful for improving specificity and sensitivity in identifying invasiveness, potentially aiding clinical decisions. Personalized treatment informed by preoperative biomarkers is essential for optimizing outcomes and reducing DA reliance, but it should be considered in conjunction with a comprehensive clinical evaluation.
The impact of earthquake preparedness training on mothers with physically disabled children: a randomized controlled study
Prediction of pressure drop in heavy oil water ring based on modified two fluid model
Enhanced brain image security using a hybrid of lifting wavelet transform and support vector machine
Abstract Thanks to technological improvements, digital picture watermarking has emerged as a useful method for preventing unlawful use and manipulation of digital photographs. Providing robustness against geometrical assault while maintaining an adequate level of security and imperceptibility is a basic challenge in digital picture watermarking. With the use of support vector machine (SVM) and lifting wavelet transform (LWT), this study offers an effective authentication approach for digital image watermarking on medical images. To distinguish between the region of interest (ROI) and the non-region of interest (NROI) in the medical image, SVM is first employed in this article. After that, LWT is used to incorporate watermark data into the medical image’s NROI section (cover image). Additionally, a shared secret key has been used to increase the suggested scheme’s resilience. A vast image database is used to test the method’s performance in various scenarios. To determine whether the current plan was acceptable, the study examined several experimental investigations. The experimental results give a PSNR value of 67.81 dB and a structural similarity index measure value of 0.9999, Where the PSNR improvement percentage is 13.9462 dB, showing durability and imperceptibility for the proposed watermarking model.
Development and validation of a scoring system to predict MASLD patients with significant hepatic fibrosis
Cortico-subcortical networks that determine behavioral memory renewal are redefined by noradrenergic neuromodulation
Abstract During spatial appetitive extinction learning (EL), rodents learn that previously rewarded behavior is no longer rewarded. Renewal of the extinguished behavior is enabled by re-exposure to the context in which rewarded learning occurred. When the renewal response (RR) is unrewarded, it is rapidly followed by response extinction (RE). Although the hippocampus is known to be engaged, whether this dynamic is supported by different brain networks is unclear. To clarify this, male rats engaged in context-dependent spatial memory acquisition, EL and RR testing in a T-Maze. Fluorescence in situ hybridization disambiguated somatic immediate early gene expression in neuronal somata engaged in RR or RE. Graph analysis revealed pronounced hippocampal connectivity with retrosplenial and prefrontal cortex (PFC) during initial RR. By contrast, RE was accompanied by a shift towards elevated coordinated activity within all hippocampal subfields. Given that β-adrenergic receptors (β-AR) regulate spatial memory, we activated β-AR to further scrutinize these network effects. This enhanced RR and prevented RE. Effects were associated with initially increased thalamic-hippocampus activity, followed by a decrease in hippocampal intraconnectivity and the predominance of network activity within PFC. Our findings highlight a critical hippocampal-cortical-thalamic network that underpins renewal behavior, with noradrenergic neuromodulation playing a pivotal role in governing this circuit’s dynamics.
Nano selenium and plant extracts supplementation enhanced reproductive performance of parity-2 sows
Localized large language model TCNNet 9B for Taiwanese networking and cybersecurity
Abstract This paper introduces TCNNet-9B, a specialized Traditional Chinese language model developed to address the specific requirements of the Taiwanese networking industry. Built upon the open-source Yi-1.5-9B architecture, TCNNet-9B underwent extensive pretraining and instruction finetuning utilizing a meticulously curated dataset derived from multi-source web crawling. The training data encompasses comprehensive networking knowledge, DIY assembly guides, equipment recommendations, and localized cybersecurity regulations. Our rigorous evaluation through custom-designed benchmarks assessed the model’s performance across English, Traditional Chinese, and Simplified Chinese contexts. The comparative analysis demonstrated TCNNet-9B’s superior performance over the baseline model, achieving a 2.35-fold improvement in Q&A task accuracy, a 37.6% increase in domain expertise comprehension, and a 29.5% enhancement in product recommendation relevance. The practical efficacy of TCNNet-9B was further validated through its successful integration into Hi5’s intelligent sales advisor system. This research highlights the significance of domain-specific adaptation and localization in enhancing large language models, providing a valuable practical reference for future developments in non-English contexts and vertical specialized fields.