Excessive intracellular glucose in insulin independent tissues including nerve, nephron, lens and retina invites mishandling of metabolism of glucose resulting in a background of increased oxidative stress, advanced glycation end products (AGE) formation, lipid peroxidation and failure of antioxidant defense systems in type 2 diabetes mellitus (T2DM). All these detrimental biochemical anomalies ultimately attack biological membranes and especially capillary beds of retina and glomerulus of kidney, resulting in break-down of inner blood-retinal i.e. initiation of diabetic retinopathy (DR). If these disarrays are corrected to a large extent, development of DR can be avoided or delayed. In this prospective clinical trial, 185 patients with T2DM who received B-vitamins, vitamin-C, and E along with anti-diabetic medication for five years, demonstrated a slower rate of the development of DR and reduced abnormal biochemical mediators like reactive oxygen species (ROS), malondialdehyde (MDA), AGE, and vascular endothelial growth factor (VEGF) compared to 175 T2DM individuals who were treated with only anti-hyperglycemic drugs.

Myr47 lipopeptide consisting of hepatitis B virus (HBV) pre-S1 domain (myristoylated 2-48 peptide) is a commercialized effective anti-HBV drug, preventing the interaction of HBV with sodium taurocholate cotransporting polypeptide (NTCP) on human hepatocytes, of which the activity requires both N-myristoylation residue and specific amino acid sequence. Meanwhile, we recently reported that Myr47 reduces the cellular uptake of HBV surface antigen (HBsAg, subviral particle of HBV) in the absence of NTCP expression (Somiya; et al. Virology 2016, 497, 23–32). In this study, we analyzed how Myr47 reduces the cellular uptake of lipid nanoparticles (including liposomes (LPs) and HBsAg) without NTCP expression. By using Myr47 mutants lacking the HBV infection inhibitory activity, they could reduce the cellular uptake of LPs in an N-myristoylation-dependent manner whereas in an amino acid sequence-independent manner. Moreover, Myr47 and its mutants could reduce the interaction of LPs with apolipoprotein E3 (ApoE3) in an N-myristoylation-dependent manner regardless of their amino acid sequences. From these results, N-myristoyl residue of lipopeptides generally could interfere the LPs/HBsAg-ApoE3 complex formation, thereby reducing the cellular uptake of LPs/HBsAg. When lipid nanoparticles are used as a DDS (drug delivery system) nanocarrier, the surface modification with lipopeptides may be a new method to inhibit unwanted cellular uptake of DDS nanocarriers by non-target cells.

Historically, viral hepatitis is a considerable public health problem in Central Asian countries, which may have worsened after dissolution of the Soviet Union. However, up-to-date seroepidemiological studies are lacking. The aim of the present study was, therefore, to provide current estimates of seroprevalence of viral hepatitis in Kyrgyzstan, one of the economically least developed countries in the region. We conducted a population-based cross-sectional study in 2018 in the capital of Kyrgyzstan, Bishkek (n=1075). Participants, children and adults, were recruited from an outpatient clinic. Data were collected during face-to-face interviews. A blood sample (6 ml) was collected from each participant and tested with ELISA for the presence of serological markers for five viral hepatitides (A, B, C, D and E). Poststratification weighting was performed to obtain nationally representative findings. The overwhelming majority of the study participants were positive for anti-HAV (estimated seroprevalence, 75.3%; 95% confidence interval: 72.5–77.9%). The weighted seroprevalence estimates of HBsAg, anti-HCV and anti-HDV were 2.2% (1.5–3.3%), 3.8% (2.8–5.1%), and 0.40% (0.15–1.01%), respectively. Anti-HEV seropositivity was 3.3% (2.4–4.5%). Of the 33 HBsAg positive participants, five (15%) were anti-HDV positive. Our study confirms that Kyrgyzstan remains a high endemic country for hepatitis virus A and C infections. However, seroprevalences of HBV and HDV were lower than previously reported, and based on these data, the country could potentially be reclassified from high to (lower) intermediate endemicity. The observed anti-HEV seroprevalence resembles the low endemicity pattern characteristic of high-income countries.

ORF6 is responsible for suppressing the immune response of cells infected by the SARS-CoV-2 virus. It is also the most toxic protein of SARS-CoV-2, and its actions are associated with the viral pathogenicity. Here we study in silico and in vitro the structure of the protein, its interaction with RAE1, and the mechanism of action behind its high toxicity. We show both computationally and experimentally that SARS-CoV-2 ORF6, embedded in the membranes of the endoplasmic reticulum, binds to RAE1 and sequesters it in the cytoplasm, thus depleting its availability in the nucleus and impairing nucleocytoplasmic mRNA transport. This negatively affects the cellular genome stability by compromising the cell cycle progression into the S-phase and by promoting the accumulation of RNA-DNA hybrids. Understanding the multiple ways in which ORF6 affects DNA replication may also have important implications for elucidating the pathogenesis of SARS-CoV-2 and developing therapeutic strategies to mitigate its deleterious effects on host cells.

Synthetic fuels play an important role in the defossilization of future aviation transport. To reduce the ecological impact of remote airports due to long range transportation of kerosene, a decentralized on-site-production of synthetic paraffinic kerosene is applicable, preferably as near-drop-in fuel or alternatively as blend. One possible solution for such a production of synthetic kerosene is the Power-to-Liquid process. The basic development of a simplified plant layout addressing the specific challenges of a decentralized kerosene production which differ from most current approaches for infrastructural well-connected regions is described. The decisive influence of the Fischer-Tropsch synthesis on the PtL process is shown by means of a steady-state reactor model which was developed in Python and serves as basis for further development of a modular environment able to represent entire process chains. The reactor model is based on reaction kinetics according current literature. The effects of adjustments of the main operation parameters on the reactor behavior are evaluated and the impacts on up- and downstream processes are described. The results prove the governing influence of the Fischer-Tropsch reactor on the PtL process and show its flexibility regarding the desired product fraction output, which makes it an appropriate solution for a decentralized kerosene production.

E-commerce businesses employ recommender models to assist in identifying a personalized set of products for each visitor. To accurately assess the recommendations’ influence on customer clicks and buys, three target areas—customer behavior, data collection, user-interface —will be explored for possible sources of erroneous data. Varied customer behavior misrepresents the recommendations’ true influence on a customer due to the presence of B2B interactions and outlier customers. Non-parametric statistical procedures for outlier removal are delineated and other strategies are investigated to account for the effect of a large percentage of new customers or high bounce rates. Subsequently, in data collection we identify probable misleading interactions in the raw data, propose a robust method of tracking unique visitors, and accurately attributing the buy influence for combo products. Lastly, user-interface issues discuss the possible problems caused due to the recommendation widget’s positioning on the e-commerce website and the stringent conditions that should be imposed when utilizing data from the product listing page. This collective methodology results in an exact and valid estimation of the customer’s interactions influenced by the recommendation model in the context of standard industry metrics such as Click-through rates, Buy-through rates, and Conversion revenue.

The pH-related metabolic paradigm has rapidly grown in cancer research and treatment. In this contribution, this recent oncological perspective has been laterally for the first time in order to integrate neurodegeneration within the energetics of the cancer acid-base conceptual frame. At all levels of study, molecular, biochemical, metabolic and clinical, the intimate nature of both processes appears to be opposite mechanisms occurring at the far ends of a physiopathological intracellular pH/extracellular pH (pHi/pHe) spectrum. This wide-ranged original approach now permits an increase in our understanding of these opposite processes, cancer and neurodegeneration, and, as a consequence, allows to propose new avenues of treatment based upon the intracellular and microenvironmental hydrogen ion dynamics regulating and deregulating the biochemistry and metabolism of both cancer and neural cells. Under the same perspective, the etiopathogenesis and special characteristics of multiple sclerosis (MS) becomes an excellent model for the study of neurodegenerative diseases and, utilizing this pioneering approach, we find that MS appears to be a metabolic disease even before an autoimmune one. Also within this paradigm, several important aspects of MS, from mitochondrial failure to microbiota functional abnormalities, are analyzed in depth.

To enable the deployment of battery-electric vehicles (BEV) as passenger cars in the private transport sector, a suitable charging infrastructure is crucial. In this paper a methodology for efficient spatial distribution of charging infrastructure is evaluated by investigating a scenario with a market penetration of BEVs of 100 percent (around 1.3 million vehicles). It aims towards the development of various charging infrastructure scenarios - including public and private charging - which are suitable to cover the charging demand. Therefore, these scenarios are investigated in detail with focus on number of public charging points, their spatial distribution, the available charging power and the necessary capital costs. For the creation of those charging infrastructure scenarios a placement model is developed. It uses the data of a MATSim (Multi-Agent Transport Simulation) traffic simulation of the metropolitan area of Berlin to evaluate and optimize different distributions of charging infrastructure. The model uses a genetic algorithm and the principle of multi-objective optimization. The capital cost of the charging points and the mean detour car drivers must cover additionally are used as optimization criteria. Using these criteria should lead to cost efficient infrastructure solutions which provide high usability at the same time. The main advantage of the method selected is that multiple optimal solutions with different characteristics can be found and suitable solutions can be selected by using other criteria subsequently. The optimized charging infrastructure solutions show capital costs between 624 and 2950 million euro. Users must cover an additionally mean detour of 254m to 590m per charging process to reach an available charging point. According to the results a suitable ratio between charging points and vehicles is between 11:1 and 5:1. A share of fast charging infrastructure (>50kW) of less than ten percent seems to be sufficient, if it is situated at main traffic routes and highly frequented places.

: E-commerce sales surge represents a huge challenge for urban freight transport. Parcel lockers constitute a valid solution for addressing the challenges home deliveries imply. In fact, eliminating courier-consumer contact (also relevant for health-related issues, as made evident by COVID19 pandemic) and delivering in few predefined places might help coping with missed deliveries substantially. Furthermore, this option enables consolidated shipping and reducing delivery trip costs. This paper analyses and compares consumers’ preferences for alternative collection strategies. It investigates home delivery vs parcel locker use and forecasts their future market shares. This is performed based on both customers’ socio-economic variables and attributes characterising these alternative logistic fulfilment strategies. The case study considered rests upon a stated preference survey deployed in the city of Rome. The investigation specifically targets young people (i.e., population under 30 years) since they represent early adopters. Discrete choice models allow both quantifying the monetary value of parcel lockers attributes (i.e., willingness to pay measures) and estimating the potential demand for this innovative delivery scheme. Results show that distance and accessibility are the main choice determinants. Furthermore, there is an overall high propensity to adopt parcel lockers. This research can support policymakers when implementing such solutions.

In this work, several random Boolean networks (RBN) are generated and analyzed from two characteristics: their time evolution diagram and their transition diagram. To do this, its randomness is estimated using three metrics, of which algorithmic complexity is the only one capable of revealing both transitions towards the chaotic regime and the algorithmic contribution of certain states to the transition diagram and their relationship with the order they occupy in the temporal evolution of the respective RBN.

The core motivation for this study emanates from Voronin S.'s seminal work on the universality of the zeta function. This universality positions the zeta function as a pivotal element in the conceptual framework of global artificial intelligence, suggesting its potential as a foundational component. A critical barrier to harnessing this promising universality has been the absence of efficient algorithms capable of actualizing this concept.

Conclusions: Our findings suggest that the NMS-E represents a valuable preoperative diagnostic tool for endometriosis, effectively correlating with the disease's severity and surgical outcomes. Incorporating the NMS-E into clinical practice could significantly enhance the management of endometriosis by addressing current diagnostic limitations and guiding surgical planning.

The term neurodegeneration with brain iron accumulation (NBIA) brings together a broad set of progressive and disabling neurological genetic disorders in which iron is deposited preferentially in certain areas of the brain. Among NBIA disorders, the most frequent subtype is pantothenate kinase-associated neurodegeneration (PKAN) caused by pathologic variants in the PANK2 gene codifying the enzyme pantothenate kinase 2 (PANK2). Nowadays, there are no effective treatments to stop the progression of these diseases. This review discusses the utility of patient-derived cell models as a valuable tool for the identification of commercial pharmacological or natural compounds for implementing polytarget precision medicine in PKAN. In the last years, several studies have described that PKAN patient-derived fibroblasts manifest the main pathological changes associated with the disease including intracellular iron overload. Interestingly, treatment of mutant cell cultures with various supplements such as pantothenate, pantethine, thiamine, L-carnitine, vitamin E, omega 3, and -lipoic acid improved all pathophysiological alterations in PKAN fibroblasts with residual expression of the PANK2 enzyme. The information provided by pharmacological screenings in patient-derived cellular models can help to optimize therapeutic strategies in individual PKAN patients.

Abstract:The digital landscape is rapidly reshaping under the influence of AI-powered cloud-based e-commerce platforms (PCEPs). These platforms leverage the transformative power of AI to personalize customer experiences, optimize pricing strategies, enhance security, and streamline operations, offering businesses a significant competitive edge. However, challenges surrounding data privacy, algorithmic bias, and potential job displacement raise crucial ethical questions regarding widespread AI adoption in e-commerce.This study delves into the untapped potential and ethical considerations of PCEPs, distinguishing itself by proposing practical strategies for responsible development and ethical implementation.Objectives: Investigate the transformative potential of PCEPs: Personalized product recommendations:Analyze effectiveness and potential bias. Dynamic pricing strategies:Explore impact on consumer fairness and market stability. Enhanced fraud detection:Evaluate accuracy and privacy implications. Optimized supply chain management:Assess efficiency gains and job displacement risks. Critically analyze the ethical considerations of AI in e-commerce: Data privacy:Assess current practices and develop mitigation strategies. Algorithmic bias:Identify potential sources and propose solutions for fairer decision-making. Job displacement:Evaluate impacts and propose reskilling initiatives. Propose comprehensive and practical strategies for responsible PCEP development and implementation: Transparency:Enhance user awareness and control over data usage. Fairness:Design AI systems to mitigate bias and uphold consumer rights. Accountability:Establish clear guidelines and oversight mechanisms for ethical AI development.Conclusion: While PCEPs offer a plethora of transformative benefits, their ethical implementation is paramount. By adhering to principles of transparency, fairness, and accountability, businesses can unlock the full potential of PCEPs while minimizing potential risks and shaping the future of online commerce in a sustainable and equitable manner

first_pagesettingsOrder Article ReprintsOpen AccessArticleThe Carbon Footprint of Spanish University Websitesby Sonia Sanchez-Cuadrado ¹ andJorge Morato ^2,*1Department of Library and Information Science, Complutense University of Madrid, 28010 Madrid, Spain²Department of Computer Science, Carlos III University of Madrid, Leganes, 28911 Madrid, Spain^*Author to whom correspondence should be addressed.Sustainability 2024, 16(13), 5670; https://doi.org/10.3390/su16135670 (registering DOI)Submission received: 31 May 2024 / Revised: 22 June 2024 / Accepted: 23 June 2024 / Published: 3 July 2024(This article belongs to the Special Issue Sustainable Education in the Digital Society: Applying Communication Technologies and Digital Tools to Education) Downloadkeyboard_arrow_down Browse Figures Versions NotesAbstractThe consumption of digital content is essential for economic and scientific development, leading to a high dependence on information and communication technologies, which significantly impacts the environment. Quantifying this ecological impact is challenging due to its intangible nature and the diversity of factors. This research focuses on assessing the energy consumption and CO₂ production of Spanish university websites to evaluate their levels of digital pollution and environmental impact. The calculation of the carbon footprint considers factors related to energy consumption and the use of green or renewable energies. The findings indicate that only 17% of university websites can be considered environmentally friendly. There are significant differences in CO₂ consumption and emissions depending on whether the websites implement environmentally sound practices. This study highlights the lack of initiatives aimed at reducing energy consumption and greenhouse gas emissions associated with web information management. The study shows that Spanish universities have the potential to contribute to the reduction in CO₂ emissions by implementing sustainable development practices on their websites. Furthermore, it fosters awareness about technology usage and its environmental impact, promoting the adoption of green and sustainable approaches.

This qualitative research explores the impact of Supplier Relationship Management (SRM) on e-commerce brand reputation, focusing on the strategic imperatives, operational challenges, and economic implications within digital supply chains. Trust emerges as a foundational element in effective SRM practices, facilitating consistent product quality, timely delivery, and consumer trust, thereby enhancing brand credibility and loyalty. Strategic alignment of SRM with organizational goals enables e-commerce brands to leverage supplier capabilities for mutual growth and innovation. Operational challenges such as technological constraints and logistical complexities underscore the importance of digital integration and proactive risk management strategies to optimize SRM effectiveness. Digital technologies, including AI and blockchain, play a transformative role in enhancing transparency, efficiency, and resilience in SRM strategies, enabling e-commerce firms to mitigate risks and capitalize on market opportunities. Emotional intelligence (EI) competencies among supply chain professionals facilitate effective communication, conflict resolution, and sustainable supplier relationships that bolster brand reputation resilience. Economically, effective SRM practices yield cost efficiencies, improved financial performance, and resilience against supply chain disruptions, supporting sustainable growth in competitive digital markets. This study contributes valuable insights for practitioners and scholars aiming to enhance SRM strategies, optimize supplier relationships, and strengthen brand reputation management in e-commerce. By prioritizing trust, strategic alignment, digital innovation, EI competencies, and proactive risk management, e-commerce brands can navigate complexities, capitalize on opportunities, and foster positive brand reputations in the evolving landscape of digital commerce.

This qualitative research explores Supplier Relationship Management (SRM) within the context of subscription-based e-commerce models. Through in-depth interviews and thematic analysis, the study investigates key elements such as trust, collaboration, technology integration, sustainability, risk management, cultural alignment, innovation, cost management, globalization, supplier selection, and performance measurement. Findings reveal that trust is foundational in fostering reliable interactions and open communication between e-commerce businesses and suppliers. Collaboration emerges as crucial for innovation and operational efficiency, facilitating co-development and shared risk management. Technology integration, including AI and blockchain, enhances supply chain visibility and decision-making capabilities. Sustainability considerations drive businesses to engage with suppliers adhering to environmental standards, enhancing brand reputation and consumer loyalty. Effective risk management strategies mitigate disruptions and ensure supply chain resilience. Cultural alignment fosters harmonious partnerships and mutual understanding across diverse global markets. Innovation through supplier collaboration drives product development and market responsiveness. Cost management practices optimize operational efficiency and strategic supplier relationships. Globalization necessitates adaptive strategies to manage diverse cultural and regulatory landscapes. Strategic supplier selection and continuous performance measurement drive ongoing improvement and alignment with business objectives. This study contributes empirical insights and practical implications for enhancing SRM in subscription-based e-commerce, informing strategies to navigate challenges and leverage opportunities in a competitive marketplace.

Abstract:Shown is the derivation of Lorentz-Einstein k-factor in SRT as an amplitude-term of oscillation-differential equations of second order.This case is shown for classical Lorentz-factor as solution of an equation for undamped oscillation as well as the developed theorem as a second solution for advanced SRT of fourth order with an equation for damped oscillation-states.This advanced term allows a calculation for any velocities by real rest mass