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Cystathionine γ-lyase (CSE) is the second enzyme in the trans-sulfuration pathway that converts cystathionine to cysteine. It is also one of three major enzymes responsible for the biosynthesis of hydrogen sulfide (H₂S). CSE is believed to be the major source of endogenous H₂S in the cardiovascular system, and the CSE/H₂S system plays a crucial role in a variety of physiological and pathological processes. However, the regulatory mechanism of the CSE/H₂S system is less well understood, especially at the post-translational level. Here, we demonstrated that the persulfidation of CSE inhibits its activity by ~2-fold in vitro. The loss of this post-translational modification in the presence of dithiothreitol (DTT) results in a reversal of basal activity. Cys137 was identified as the site for persulfidation by combining mass spectrometry, mutagenesis, activity analysis and streptavidin–biotin pull-down assays. To test the physiological relevance of the persulfidation regulation of CSE, human aortic vascular smooth muscle cells (HA-VSMCs) were incubated with vascular endothelial growth factor (VEGF), which is known to enhance endogenous H₂S levels. Under these conditions, consistent with the change tendency of the cellular H₂S level, the CSE persulfidation levels increased transiently and then gradually decreased to the basal level. Collectively, our study revealed a negative feedback regulation mechanism of the CSE/H₂S system via the persulfidation of CSE and demonstrated the potential for maintaining cellular H₂S homeostasis under oxidative stress conditions, particularly in tissues where CSE is a major source of H₂S. Full article

Background: The Neuro-2a cell line, derived from a murine neuroblastoma (NB), was established as early as 1969 and originates from a transplantable tumor that arose spontaneously in an A/Jax male mouse in 1940. Since then, it has been applied in over 10,000 studies and is used by the World Organization for Animal Health for the routine diagnosis of rabies. Surprisingly, however, Neuro-2a has never been genetically characterized in detail; this study fills that gap. Methods: The Neuro-2a cell line and two of its derivatives, Neuro-2a TR-alpha and Neuro-2a TR-beta, were analyzed for their chromosomal constitution using molecular cytogenetic approaches. Array comparative genomic hybridization was performed to characterize copy number alterations. Results: Neuro-2A has a hyper-tetraploid karyotype with 70 to 97 chromosomes per cell, and the karyotypes of its two examined derivatives were quite similar. Neither of them had a Y-chromosome. The complex karyotype of Neuro-2a includes mitotically stable dicentres, neocentrics, and complex rearrangements resembling chromothripsis events. Although no amplification of euchromatin or oncogenes was detected, there are five derivative chromosomes with the amplification of centromere-near heterochromatic material and 1–5 additional derivatives consisting only of such material. Conclusions: Since satellite DNA amplification has recently been found in advanced human tumors, this finding may be the corresponding equivalent in mice. An in silico translation of the obtained results into the human genome indicated that Neuro-2A is suitable as a model for advanced human NB. Full article

Fetal growth restriction (FGR) increases the risk of cardiovascular disease. There are currently no treatment options available; however, antioxidants have shown potential to improve cardiovascular deficits associated with FGR. This systematic review aimed to determine whether antenatal antioxidant intervention can effectively protect the developing cardiovascular system in FGR. We searched for interventional studies that used an antenatal antioxidant intervention to improve cardiac and/or vascular outcomes in FGR published between 01/1946 and 09/2024 using MEDLINE and Embase (PROSPERO: CRD42024503756). The risk of bias was assessed with SYRCLE. The studies were assessed for cardiovascular protection based on the percentage of cardiac and/or vascular deficits that were restored with the antioxidant treatment. Studies were characterised as showing strong cardiovascular protection (≥50% restoration), mild cardiovascular protection (>0% but <50% restoration), an antioxidant-only effect (this did not include control group which showed a change with antioxidant intervention compared to FGR) or no cardiovascular protection (0% restoration). Thirty-eight publications met the inclusion criteria, encompassing 43 studies and investigating 15 antioxidant interventions. Moreover, 29/43 studies (71%) reported the restoration of at least one cardiac or vascular deficit with antioxidant intervention, and 21/43 studies (51%) were classified as strong cardiovascular protection. An ex vivo analysis of the arterial function in seven studies revealed endothelial dysfunction in growth-restricted offspring and antioxidant interventions restored the endothelial function in all cases. Additionally, four studies demonstrated that antioxidants reduced peroxynitrite-mediated oxidative stress. Notably, only 13/43 studies (32%) delayed antioxidant administration until after the induction of FGR. Antenatal antioxidant interventions show promise for providing cardiovascular protection in FGR. Melatonin was the most frequently studied intervention followed by nMitoQ, vitamin C and N-acetylcysteine, all of which demonstrated a strong capacity to reduce oxidative stress and improve nitric oxide bioavailability in the cardiovascular system of growth-restricted offspring; however, this systematic review highlights critical knowledge gaps and inconsistencies in preclinical research, which hinder our ability to determine which antioxidant treatments are currently suitable for clinical translation. Full article

In response to the shortcomings of current spatiotemporal prediction models, which frequently encounter difficulties in temporal feature extraction and the forecasting of medium to high echo intensity regions over extended sequences, this study presents a novel model for radar echo extrapolation that combines a translator encoder-decoder architecture with a spatiotemporal dual-discriminator conditional generative adversarial network (STD-TranslatorNet). Initially, an image reconstruction network is established as the generator, employing a combination of a temporal attention unit (TAU) and an encoder–decoder framework. Within this architecture, both intra-frame static attention and inter-frame dynamic attention mechanisms are utilized to derive attention weights across image channels, thereby effectively capturing the temporal evolution of time series images. This approach enhances the network’s capacity to comprehend local spatial features alongside global temporal dynamics. The encoder–decoder configuration further bolsters the network’s proficiency in feature extraction through image reconstruction. Subsequently, the spatiotemporal dual discriminator is crafted to encapsulate both temporal correlations and spatial attributes within the generated image sequences. This design serves to effectively steer the generator’s output, thereby augmenting the realism of the produced images. Lastly, a composite multi-loss function is proposed to enhance the network’s capability to model intricate spatiotemporal evolving radar echo data, facilitating a more comprehensive assessment of the quality of the generated images, which in turn fortifies the network’s robustness. Experimental findings derived from the standard radar echo dataset (SRAD) reveal that the proposed radar echo extrapolation technique exhibits superior performance, with average critical success index (CSI) and probability of detection (POD) metrics per frame increasing by 6.9% and 7.6%, respectively, in comparison to prior methodologies. Full article

Biblical Scholars working with ancient texts are engaged in the daily task of Bible translation. A commitment to Biblical Performance Criticism (BPC) can be transformative in the task of translation. It is argued in this paper that responsible translation will work towards replicating the artistry of original transmitters of texts in order to draw out traces of the original orality embedded in the texts. Examples of performance-sensitive translations of texts predominantly from the Hebrew Bible are provided. This e-paper also demonstrates that translations that draw out performative elements contribute to interpretation, especially when such scripts are staged before audiences. Subsequent analysis of the performance including audience response contributes to such interpretation. Performance-sensitive translation and actual performance thus become tools for embodiment and the interpretation of biblical texts in our own time. Full article

To cope with the challenges posed by the complex linguistic structure and lexical polysemy in ancient texts, this study proposes a two-stage translation model. First, we combine GujiBERT, GCN, and LSTM to categorize ancient texts into historical and non-historical categories. This categorization lays the foundation for the subsequent translation task. To improve the efficiency of word vector generation and reduce the limitations of the traditional Word2Vec model, we integrated the entropy weight method in the hopping lattice training process and spliced the word vectors with GujiBERT. This improved method improves the efficiency of word vector generation and enhances the model’s ability to accurately represent lexical polysemy and grammatical structure in ancient documents through dependency weighting. In training the translation model, we used a different dataset for each text category, significantly improving the translation accuracy. Experimental results show that our categorization model improves the accuracy by 5% compared to GujiBERT. In contrast, the Entropy-SkipBERT improves the BLEU scores by 0.7 and 0.4 on historical and non-historical datasets. Ultimately, the proposed two-stage model improves the BLEU scores by 2.7 over the baseline model. Full article

Milk-derived extracellular vesicles (mEVs) are emerging as promising therapeutic candidates due to their unique properties and versatile functions. These vesicles play a crucial role in immunomodulation by influencing macrophage differentiation and cytokine production, potentially aiding in the treatment of conditions such as bone loss, fibrosis, and cancer. mEVs also have the capacity to modulate gut microbiota composition, which may alleviate the symptoms of inflammatory bowel diseases and promote intestinal barrier integrity. Their potential as drug delivery vehicles is significant, enhancing the stability, solubility, and bioavailability of anticancer agents while supporting wound healing and reducing inflammation. Additionally, bovine mEVs exhibit anti-aging properties and protect skin cells from UV damage. As vaccine platforms, mEVs offer advantages including biocompatibility, antigen protection, and the ability to elicit robust immune responses through targeted delivery to specific immune cells. Despite these promising applications, challenges persist, including their complex roles in cancer, effective antigen loading, regulatory hurdles, and the need for standardized production methods. Achieving high targeting specificity and understanding the long-term effects of mEV-based therapies are essential for clinical translation. Ongoing research aims to optimize mEV production methods, enhance targeting capabilities, and conduct rigorous preclinical and clinical studies. By addressing these challenges, mEVs hold the potential to revolutionize vaccine development and targeted drug delivery, ultimately improving therapeutic outcomes across various medical fields. Full article

A sub-micron two-dimensional displacement sensor based on a segment of multi-core fiber is presented in this paper. Light at the wavelengths of 1520 nm, 1530 nm, and 1540 nm was introduced separately into three cores of a seven-core fiber (SCF). They were independently transmitted in their respective cores, and after being emitted from the other end of the SCF, they were irradiated onto the end-face of a single-mode fiber (SMF). The SMF received light at three different wavelengths, the power of which was related to the relative position between the SCF and the SMF. When the SMF moved within a two-dimensional plane, the direction of displacement could be determined based on the changes in power at different wavelengths. As a benefit of the high sensitivity of the spectrometer, the sensor could detect displacements at the sub-micron level. When the SMF was translated in 200 nm steps over a range from 5.2 μm to 6.2 μm, the sensitivities at the wavelengths of 1520 nm, 1530 nm, and 1540 nm were 0.34 dB/μm, 0.40 dB/μm, and 0.36 dB/μm, respectively. The two-dimensional displacement sensor proposed in this paper offers the advantages of high detection precision, simple structure, and ease of implementation. Full article

This study focuses on the development of a wearable real-time Humming Detector Device (HDD) aimed at enhancing the control of assistive devices through humming. As the need for portable user-friendly tools in assistive technology grows, the HDD offers a non-invasive solution to detect vocal cord vibrations. Vibrations, detected thanks to an accelerometer worn on the neck, are processed in real time using a Fast Fourier Transform (FFT) to identify specific humming frequencies, which are then translated into commands for controlling assistive devices via Bluetooth Low Energy (BLE) transmission. The device was tested with 13 healthy subjects to validate its potential and determine the optimal number of distinct commands that users can achieve. The HDD’s portability and precision make it a promising alternative to traditional voice recognition systems, particularly for individuals with speech impairments. Full article

There are several factors to account for marine growth including but not limited to temperature, salinity, chlorophyll-a content, existing species in the environment and predating. This paper proposes a model of biological growth for hard species on marine structures, which can be compatible with site-specific and realistic ecology while also being able to translate the results for analyses linked to lifetime hydrodynamic or structural effects via commercial software or computing. The model preserves fundamentals of ecological aspects rather than using heuristics or random sampling to data fitting on sparsely collected information. The coefficients used in the proposed model align to the real world, with location-specific values, and can be adapted to new information. The growth model is demonstrated for Mythulis Edulis (blue mussel) colonisation to assess the lifetime hydrodynamic effects for the West Coast of Ireland and the Gulf of Guinea. The model can be extended to any hard growth approach. Full article

In rigid lower-mobility parallel manipulators the motion of the end-effector is partially constrained due to a combination of passive kinematic pairs and rigid components. Translational mechanisms, such as the Delta manipulator, are the most common ones among this type of mechanisms. When flexible elements are introduced, as in Parallel Continuum Manipulators, the constraint is no longer rigid, and new challenges arise in performing certain motions depending on the degree of compliance. Mobility analysis shifts from being purely a geometric issue to one that heavily relies on force distribution within the mechanism. Simply converting classical lower-mobility rigid parallel mechanisms into Parallel Continuum Mechanisms may yield unexpected outcomes. This work, making use of a planar parallel continuum Delta manipulator, on the one hand, presents two different approaches to solve the Forward Kinematics of planar continuum manipulators, and, on the other hand, explores some challenges and issues in assessing the resultant workspace for different design alternatives of this kind of flexible manipulators. Full article

The action of mRNA-based vaccines requires the expression of the antigen in cells targeted by lipid nanoparticle–mRNA complexes. When the vaccine antigen is not fully retained by the producer cells, its local and systemic diffusion can have consequences depending on both the levels of antigen expression and its biological activity. A peculiarity of mRNA-based COVID-19 vaccines is the extraordinarily high amounts of the Spike antigen expressed by the target cells. In addition, vaccine Spike can be shed and bind to ACE-2 cell receptors, thereby inducing responses of pathogenetic significance including the release of soluble factors which, in turn, can dysregulate key immunologic processes. Moreover, the circulatory immune responses triggered by the vaccine Spike is quite powerful, and can lead to effective anti-Spike antibody cross-binding, as well as to the emergence of both auto- and anti-idiotype antibodies. In this paper, the immunologic downsides of the strong efficiency of the translation of the mRNA associated with COVID-19 vaccines are discussed together with the arguments supporting the idea that most of them can be avoided with the advent of next-generation, mucosal COVID-19 vaccines. Full article

Arabic raw audio datasets were initially gathered to produce a corresponding signal spectrum, which was further used to extract the Mel-Frequency Cepstral Coefficients (MFCCs). The pronunciation dictionary, language model, and acoustic model were further derived from the MFCCs’ features. These output data were processed into Baidu’s Deep Speech model (ASR system) to attain the text corpus. Baidu’s Deep Speech model was implemented to precisely identify the global optimal value rapidly while preserving a low word and character discrepancy rate by attaining an excellent performance in isolated and end-to-end speech recognition. The desired outcome in this work is to forecast the next word and character in a sequential and systematic order that applies under natural language processing (NLP). This work combines the trained Arabic language model ARABERT with the potential of Long Short-Term Memory (LSTM) networks to predict the next word and character in an Arabic text. We used the pre-trained ARABERT embedding to improve the model’s capacity and, to capture semantic relationships within the language, we educated LSTM + CNN and Markov models on Arabic text data to assess the efficacy of this model. Python libraries such as TensorFlow, Pickle, Keras, and NumPy were used to effectively design our development model. We extensively assessed the model’s performance using new Arabic text, focusing on evaluation metrics like accuracy, word error rate, character error rate, BLEU score, and perplexity. The results show how well the combined LSTM + ARABERT and Markov models have outperformed the baseline models in envisaging the next word or character in the Arabic text. The accuracy rates of 64.9% for LSTM, 74.6% for ARABERT + LSTM, and 78% for Markov chain models were achieved in predicting the next word, and the accuracy rates of 72% for LSTM, 72.22% for LSTM + CNN, and 73% for ARABERET + LSTM models were achieved for the next-character prediction. This work unveils a novelty in Arabic natural language processing tasks, estimating a potential future expansion in deriving a precise next-word and next-character forecasting, which can be an efficient utility for text generation and machine translation applications. Full article

The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed and efficiency, thereby limiting success. This review explores how recent developments in machine learning (ML) are significantly impacting target-based drug discovery, particularly in small-molecule approaches. The Simplified Molecular Input Line Entry System (SMILES), which translates a chemical compound’s three-dimensional structure into a string of symbols, is now widely used in drug design, mining, and repurposing. Utilizing ML and natural language processing techniques, SMILES has revolutionized lead identification, high-throughput screening and virtual screening. ML models enhance the accuracy of predicting binding affinity and selectivity, reducing the need for extensive experimental screening. Additionally, deep learning, with its strengths in analyzing spatial and sequential data through convolutional neural networks (CNNs) and recurrent neural networks (RNNs), shows promise for virtual screening, target identification, and de novo drug design. Fragment-based approaches also benefit from ML algorithms and techniques like generative adversarial networks (GANs), which predict fragment properties and binding affinities, aiding in hit selection and design optimization. Structure-based drug design, which relies on high-resolution protein structures, leverages ML models for accurate predictions of binding interactions. While challenges such as interpretability and data quality remain, ML’s transformative impact accelerates target-based drug discovery, increasing efficiency and innovation. Its potential to deliver new and improved treatments for various diseases is significant. Full article

Climate risk refers to the risks associated with climate change and has already started to impact various sectors of the economy. In this work, we focus on the impact of physical risk on the probability of default for a firm in the agribusiness sector. The probability of default is estimated based on the Merton model, where the firm defaults when its asset value falls below the threshold defined by its liabilities. We study the relationship between the stock value of the firm and global surface temperature anomalies, observing that an increase in temperature negatively affects the stock value and, consequently, the asset value of the firm. A decrease in the asset value of the firm translates into an increase in its probability of default. We also propose a model to assess the exposure of the firm to transition risk. Full article