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We develop a dynamic asset pricing model of cryptocurrencies/tokens that allow users to conduct peer-to-peer transactions on digital platforms. The equilibrium price of tokens is determined by aggregating heterogeneous users’transactional demand, rather than discounting cash flows as is done in standard valuations models. Endogenous platform adoption builds on user network externality and exhibits an S-curve: it starts slow, becomes volatile, and eventually tapers off. The introduction of tokens lowers users’ transaction costs on the platform by allowing users to capitalize on platform growth. The resultant intertemporal feedback between user adoption and token price accelerates adoption and dampens user-base volatility.

Cysteine protease cathepsins have traditionally been considered as lysosome-restricted proteases that mediate proteolysis of unwanted proteins. However, studies from the past decade demonstrate that these proteases are localized not only in acidic compartments (endosomes and lysosomes), where they participate in intracellular protein degradation, but also in the extracellular milieu, plasma membrane, cytosol, nucleus, and nuclear membrane, where they mediate extracellular matrix protein degradation, cell signalling, and protein processing and trafficking through the plasma and nuclear membranes and between intracellular organelles. Studies in experimental disease models and on cathepsin-selective inhibitors, as well as plasma and tissue biomarker data from animal models and humans, have verified the participation of cysteinyl cathepsins in the pathogenesis of many cardiovascular diseases, including atherosclerosis, myocardial infarction, cardiac hypertrophy, cardiomyopathy, abdominal aortic aneurysms, and hypertension. Clinical trials of cathepsin inhibitors in chronic inflammatory diseases suggest the utility of these inhibitors for the treatment of cardiovascular diseases and associated complications. Moreover, development of cell transfer technologies that enable ex vivo cell treatment with cathepsin inhibitors might limit the unwanted systemic effects of cathepsin inhibition and provide new avenues for targeting cysteinyl cathepsins. In this Review, we summarize the available evidence implicating cysteinyl cathepsins in the pathogenesis of cardiovascular diseases, discuss their potential as biomarkers of disease progression, and explore the potential of cathepsin inhibitors for the treatment of cardiovascular diseases.

Malicious software (malware), in various forms and variants, continues to pose significant threats to user information security. Researchers have identified the effectiveness of utilizing API call sequences to identify malware. However, the evasion techniques employed by malware, such as obfuscation and complex API call sequences, challenge existing detection methods. This research addresses this issue by introducing CAFTrans, a novel transformer-based model for malware detection. We enhance the traditional transformer encoder with a one-dimensional channel attention module (1D-CAM) to improve the correlation between API call vector features, thereby enhancing feature embedding. A word frequency reinforcement module is also implemented to refine API features by preserving low-frequency API features. To capture subtle relationships between APIs and achieve more accurate identification of features for different types of malware, we leverage convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. Experimental results demonstrate the effectiveness of CAFTrans, achieving state-of-the-art performance on the mal-api-2019 dataset with an F1 score of 0.65252 and an AUC of 0.8913. The findings suggest that CAFTrans improves accuracy in distinguishing between various types of malware and exhibits enhanced recognition capabilities for unknown samples and adversarial attacks.

Blockchain technology provides decentralized consensus and potentially enlarges the contracting space through smart contracts. Meanwhile, generating decentralized consensus entails distributing information that necessarily alters the informational environment. We analyze how decentralization relates to consensus quality and how the quintessential features of blockchain remold the landscape of competition. Smart contracts can mitigate informational asymmetry and improve welfare and consumer surplus through enhanced entry and competition, yet distributing information during consensus generation may encourage greater collusion. In general, blockchains sustain market equilibria with a wider range of economic outcomes. We further discuss the implications for antitrust policies targeted at blockchain applications.

We study credit allocation across firms and its real effects during China’s economic stimulus plan of 2009–2010. We match confidential loan-level data from the nineteen largest Chinese banks with firm-level data on manufacturing firms. We document that the stimulus-driven credit expansion disproportionately favored state-owned firms and firms with a lower average product of capital, reversing the process of capital reallocation toward private firms that characterized China’s high growth before 2008. We argue that implicit government guarantees for state-connected firms become more prominent during recessions and can explain this reversal.

The rise of centralized mining pools for risk sharing does not necessarily undermine the decentralization required for blockchains: because of miners’ cross-pool diversification and pool managers’endogenous fee setting, larger pools better internalize their externality on global hash rates, charge higher fees, attract disproportionately fewer miners, and grow more slowly. Instead, mining pools as a financial innovation escalate miners’ arms race and significantly increase the energy consumption of proof-of-work-based blockchains. Empirical evidence from Bitcoin mining supports our model’s predictions. The economic insights inform other consensus protocols and the industrial organization of mainstream sectors with similar characteristics but ambiguous prior findings.

Abundant clinical, epidemiological, imaging, genetic, molecular, and pathophysiological data together indicate that there occur an unusual inflammatory reaction and a disruption of the innate-immune signaling system in Alzheimer's disease (AD) brain. Despite many years of intense study, the origin and molecular mechanics of these AD-relevant pathogenic signals are still not well understood. Here, we provide evidence that an intensely pro-inflammatory bacterial lipopolysaccharide (LPS), part of a complex mixture of pro-inflammatory neurotoxins arising from abundant Gram-negative bacilli of the human gastrointestinal (GI) tract, are abundant in AD-affected brain neocortex and hippocampus. For the first time, we provide evidence that LPS immunohistochemical signals appear to aggregate in clumps in the parenchyma in control brains, and in AD, about 75% of anti-LPS signals were clustered around the periphery of DAPI-stained nuclei. As LPS is an abundant secretory product of Gram-negative bacilli resident in the human GI-tract, these observations suggest (i) that a major source of pro-inflammatory signals in AD brain may originate from internally derived noxious exudates of the GI-tract microbiome; (ii) that due to aging, vascular deficits or degenerative disease these neurotoxic molecules may \"leak\" into the systemic circulation, cerebral vasculature, and on into the brain; and (iii) that this internal source of microbiome-derived neurotoxins may play a particularly strong role in shaping the human immune system and contributing to neural degeneration, particularly in the aging CNS. This \" \" paper will further highlight some very recent developments that implicate GI-tract microbiome-derived LPS as an important contributor to inflammatory-neurodegeneration in the AD brain.

Increasing drug resistance has brought enormous challenges to the management of Trichosporon spp. infections. The in vitro antifungal activities of non-steroidal anti-inflammatory drugs (NSAIDs) against Candida spp. and Cryptococcus spp. were recently discovered. In the present study, the in vitro interactions between three NSAIDs (aspirin, ibuprofen and diclofenac sodium) and commonly used antifungal agents (fluconazole, itraconazole, voriconazole, caspofungin and amphotericin B) against planktonic and biofilm cells of T. asahii were evaluated using the checkerboard microdilution method. The spectrophotometric method and the XTT reduction assay were used to generate data on biofilm cells. The fractional inhibitory concentration index (FICI) and the ΔE model were compared to interpret drug interactions. Using the FICI, the highest percentages of synergistic effects against planktonic cells (86.67%) and biofilm cells (73.33%) were found for amphotericin B/ibuprofen, and caspofungin/ibuprofen showed appreciable percentages (73.33% for planktonic form and 60.00% for biofilm) as well. We did not observe antagonism. The ΔE model gave consistent results with FICI (86.67%). Our findings suggest that amphotericin B/ibuprofen and caspofungin/ibuprofen combinations have potential effects against T. asahii. Further in vivo and animal studies to investigate associated mechanisms need to be conducted.

AIGC is currently a hot field and a future trend in AI applications, and addressing the challenge of digitally reconstructing cultural heritage under the influence of AI technology is a pressing issue that requires immediate resolution. The article proposes an application framework for AIGC technology that is based on refining its meaning and designing a specific process for applying it to the digital reconstruction of cultural heritage. A high-definition camera is used to acquire relevant images of cultural heritage. The image features are extracted by the SIFT algorithm optimized by the PROSAC algorithm. The color features are acquired by combining the color histogram, color moment, and color correlation diagram. The 3D laser scanning technology is used to obtain the 3D point cloud data of the cultural heritage; the KD-tree improved ICP algorithm is introduced to improve the efficiency of point cloud alignment; the dense reconstruction of the 3D point cloud data of the cultural heritage is realized based on CMVS/PMVS; and the immersive 3D experience system of the cultural heritage is constructed by combining with platforms such as Unity3D. The average matching rate of the optimized SITF algorithm to the image features of cultural heritage is about 74.91%, and the maximum alignment time of the ICP algorithm to the cultural heritage point cloud data based on KD-tree is 9.241 s. The cultural heritage immersive 3D experience system has a satisfaction rate of 56.75%, and the density reconstructed model’s surface has an average deviation of only 0.34 mm from the real surface. The user satisfaction rating for the immersive 3D experience system for cultural heritage is 56.75%. Based on AIGC technology, it can revitalize cultural heritage and achieve digital reconstruction and inheritance innovation of cultural heritage.

In the domain of addressing cancer resistance, challenges such as limited effectiveness and treatment resistance remain persistent. Hypoxia is a key feature of solid tumors and is strongly associated with poor prognosis in cancer patients. Another significant portion of the development of acquired drug resistance is attributed to tumor stemness. Cancer stem cells (CSCs), a small tumor cell subset with self-renewal and proliferative abilities, are crucial for tumor initiation, metastasis, and intra-tumoral heterogeneity. Studies have shown a significant association between hypoxia and CSCs in the context of tumor resistance. Recent studies reveal a strong link between hypoxia and tumor stemness, which together promote tumor survival and progression during treatment. This review elucidates the interplay between hypoxia and CSCs, as well as their correlation with resistance to therapeutic drugs. Targeting pivotal genes associated with hypoxia and stemness holds promise for the development of novel therapeutics to combat tumor resistance.