Faster Rate Research Articles

Thermosensitive Liposomes for Gemcitabine Delivery to Pancreatic Ductal Adenocarcinoma.

Treatment of pancreatic ductal adenocarcinoma with gemcitabine is limited by an increased desmoplasia, poor vascularization, and short plasma half-life. Heat-sensitive liposomes modified by polyethylene glycol (PEG; PEGylated liposomes) can increase plasma stability, reduce clearance, and decrease side effects. Nevertheless, translation of heat-sensitive liposomes to the clinic has been hindered by the low loading efficiency of gemcitabine and by the difficulty of inducing hyperthermia in vivo. This study was designed to investigate the effect of phospholipid content on the stability of liposomes at 37 °C and their release under hyperthermia conditions; this was accomplished by employing a two-stage heating approach. First the liposomes were heated at a fast rate, then they were transferred to a holding bath. Thermosensitive liposomes formulated with DPPC: DSPC: PEG2k (80:15:5, mole%) exhibited minimal release of carboxyfluorescein at 37 °C over 30 min, indicating stability under physiological conditions. However, upon exposure to hyperthermic conditions (43 °C and 45 °C), these liposomes demonstrated a rapid and significant release of their encapsulated content. The encapsulation efficiency for gemcitabine was calculated at 16.9%. Additionally, fluorescent analysis during the removal of unencapsulated gemcitabine revealed an increase in pH. In vitro tests with BxPC3 and KPC cell models showed that these thermosensitive liposomes induced a heat-dependent cytotoxic effect comparable to free gemcitabine at temperatures above 41 °C. This study highlights the effectiveness of the heating mechanism and cell models in understanding the current challenges in developing gemcitabine-loaded heat-sensitive liposomes.

Ultrastructural and thermal analyses reveal novel insights into low-temperature survival mechanisms of hydrated seeds of Poaceae species from alpine regions

Global warming leads to snow cover loss in the alpine ecosystem, exposing seeds to extreme diurnal temperature fluctuations during the growing season. The risk of freezing increases as seeds have increased moisture content. Studying the survival mechanisms of seeds at low temperatures can help analyze changes in alpine meadow populations and target conservation efforts. Here, we used three species of Poaceae as a model to understand freezing stress.Fully imbibed Elymus dahuricus, Festuca elata, and Lolium multiflorum seeds were subjected to programmed cooling at fast and slow rates (-1.0/0.05°C/min) and then assessed for survival. Differential Scanning Calorimetry was used to analyze thermal transitions during cooling. HE-stained paraffin sections and a Transmission Electron Microscope were employed to observe internal morphology and ultrastructural changes.E. dahuricus seeds exhibited greater tolerance to low temperatures than those of the other two species, with an LT50 of approximately -20°C for both cooling rates and maintained relatively intact ultrastructure. The observed Low-temperature exotherm (LTE) correlated with seed survival, with viability decreasing extensively below LTE.Fast cooling caused fewer changes to seed morphology and ultrastructure than slow cooling, suggesting that the primary survival mechanism during fast cooling is freezing avoidance through supercooling. Seeds exhibited greater freeze tolerance under slow than fast cooling, primarily by migrating intracellular water to extracellular spaces where it froze, causing considerable damage to cell ultrastructure and forming apparent cavities in some seeds.

In Vivo Three-Dimensional Geometric Reconstruction of the Mouse Aortic Heart Valve.

Aortic valve (AV) disease is a common valvular lesion in the United States, present in about 5% of the population at age 65 with increasing prevalence with advancing age. While current replacement heart valves have extended life for many, their long-term use remains hampered by limited durability. Non-surgical treatments for AV disease do not yet exist, in large part because our understanding of AV disease etiology remains incomplete. The direct study of human AV disease remains hampered by the fact that clinical data is only available at the time of treatment, where the disease is at or near end stage and any time progression information has been lost. Large animal models, long used to assess replacement AV devices, cannot yet reproduce AV disease processes. As an important alternative mouse animal models are attractive for their ability to perform genetic studies of the AV disease processes and test potential pharmaceutical treatments. While mouse models have been used for cellular and genetic studies of AV disease, their small size and fast heart rates have hindered their use for tissue- and organ-level studies. We have recently developed a novel ex vivo micro-CT-based methodology to 3D reconstruct murine heart valves and estimate the leaflet mechanical behaviors (Feng et al. in Sci Rep 13(1):12852, 2023). In the present study, we extended our approach to 3D reconstruction of the in vivo functional murine AV (mAV) geometry using high-frequency four-dimensional ultrasound (4DUS). From the resulting 4DUS images we digitized the mAV mid-surface coordinates in the fully closed and fully opened states. We then utilized matched high-resolution µCT images of ex vivo mouse mAV to develop mAV NURBS-based geometric model. We then fitted the mAV geometric model to the in vivo data to reconstruct the 3D in vivo mAV geometry in the closed and open states in n = 3 mAV. Results demonstrated high fidelity geometric results. To our knowledge, this is the first time such reconstruction was ever achieved. This robust assessment of in vivo mAV leaflet kinematics in 3D opens up the possibility for longitudinal characterization of murine models that develop aortic valve disease.

Rational modulation of covalent organic frameworks heterogeneous catalyst: Structural cationization effect on accelerating photocatalytic oxidation process

Photocatalytic technology has been playing a key role in energy conversion and environmental remediation. It is critical and challenging that understanding and modulating the local structure effect on accelerating each rate-dependent step during the photocatalytic process. Herein, we utilize the highly ordered and structurally tunable COFs as a favorable platform for integrating multiple modulations of photochemical properties, including light capture, exciton dissociation, charge separation, and mass transfer by structural cationization strategy. Moreover, it is found that the cationized COF has a strong enrichment ability on the sulfide substrates, exhibiting an extremely fast selective oxidation rate of sulfides even in air. The calculation results indicate that the structural cationization successfully aggregates reaction substrates and increases the molecular collision probability, generating a reaction environment to promote the photocatalytic performances. This study will be expected to facilitate the new insights for the exploration of highly active COFs heterogeneous photocatalysts.

Additive manufacturing of a new titanium alloy with tunable microstructure and isotropic properties

Conventional titanium alloys have a high propensity in developing columnar grains with strong textures during additive manufacturing (AM), which causes pronounced anisotropy in mechanical properties and hampers their practical applications. In this study, a new metastable β titanium alloy with high Fe addition (Ti-3Al-6Fe-6V-2Zr, wt.%) was designed for AM, and the strategies and underlying mechanisms to eliminating the structural heterogeneity including grain morphology, Fe element segregation and phase constituent distribution were elaborately investigated. It was demonstrated that the alloy has an outstanding intrinsic capability of forming equiaxed grains during direct energy deposition (DED) due to the positive effect of Fe on constitutional supercooling. The final microstructure of bulk sample was determined by the directly deposited microstructure and subsequent microstructure coarsening caused by cyclic heating, and homogeneously equiaxed microstructure without texture could be achieved when the heat-affected zones can fully cover the formerly deposited layer. Despite of very high level of Fe addition, both microscale and macroscale Fe segregation were completely suppressed during DED, by taking the advantages of fast solidification rate and tailoring the heating effect between the adjacent layers. Moreover, the problem related to the heterogeneity in α phase distribution along the building direction was solved by mitigating the heat accumulation during DED. On the basis of these understandings, homogeneously equiaxed Ti3662 alloy with isotropic mechanical properties of high strength (~1200MPa) and decent ductility (~10%) was finally fabricated by DED, which stands a good chance for practical application. This study demonstrates that the special metallurgical process during AM largely expands the design space of titanium alloys on the aspects of composition and microstructure, which can be utilized to fabricate titanium alloys with desirable microstructure and excellent properties.

Enhancement of Photoelectric Conversion Efficiency in Dye-Sensitized Solar Cells Using SnO2–TiO2 Composite Nanofibers

SnO2 is a wide-band gap N-type semiconductor material with a higher electron transfer rate than TiO2. Therefore, SnO2 has broad application prospects in the photoanode composite materials of dye-sensitized solar cells (DSSC). In this study, SnO2–TiO2 composite nanofibers with different molar ratios (5%, 10%, 15%, and 20%) were prepared by double-needle electrospinning technology, and the nanofibers were characterized after adjusting the solution composition. Different molar ratios of TiO2 and SnO2–TiO2 composite nanofiber photoanodes were fabricated by spin-coating, and the adsorption behavior of dyes on the photoanodes was investigated. Moreover, the electrochemical impedance of the photoanodes and DSSC and their photoelectric performance were also tested. The experimental results showed that SnO2–TiO2 composite nanofibers with a molar ratio of 10% exhibited excellent properties, including a peak specific surface area of 93.01 m2/g, strong dye adsorption capacity, small interfacial resistance, and fast electron transfer rate. These outstanding properties significantly promoted the generation of photoelectrons and improved the photoelectric conversion efficiency of DSSC. Compared with single-component TiO2, the photoelectric conversion efficiency of SnO2–TiO2 nanofiber photoanode composite material improved by 85.65%.

Facile construction of ZnWO4/g-C3N4 heterojunction for the improved photocatalytic degradation of MB, RhB and mixed dyes

This study presents the construction of binary ZnWO4/g-C3N4 nanocomposite via a facile hydrothermal approach to enhance the photocatalytic performance under the visible light irradiation. The proposed ZnWO4/g-C3N4 nanocomposite photocatalyst shows excellent photodegradation towards organic dyes, such as methylene blue (MB) and rhodamine B (RhB). The optimal loading of ZnWO4 and g-C3N4 leads to the synergistic effect which plays a predominant role in inhibiting the fast electron-hole pairs recombination rate at the interface of ZnWO4/g-C3N4 photocatalyst. The degradation rates of MB and RhB is achieved around 92.9 % and 88.8 % under the visible light irradiation for 120 min. According to our findings, the radical quenching experiments suggested that the ●OH radicals played a positive impact in the photodegradation process. Since, our proposed photocatalyst exhibit superior photocatalytic activity towards the individual MB and RhB degradation. The ZnWO4/g-C3N4 photocatalyst were further applied to demonstrate the degradation of mixed dyes (MB and RhB) at an irradiation time of 180 min. In the mixed dye solution, the ZnWO4/g-C3N4 photocatalyst achieved a highest reaction rate of 0.010 min−1 for MB and 0.012 min−1 for RhB with the degradation rate of 87.8 % and 83.3 % for RhB and MB, respectively. For the mixed dyes, the radical quenching experiments confirmed that the ●OH radicals are responsible for the fast photodegradation. Additionally, the practicality of the proposed ZnWO4/g-C3N4 photocatalyst towards the degradation of MB, RhB and the mixed dyes were examined in the tap water samples. Furthermore, the plausible photodegradation mechanism of ZnWO4/g-C3N4 photocatalyst was proposed in detail. This study provides a new insight for the simultaneous degradation of mixed chemical pollutants using our proposed ZnWO4/g-C3N4 photocatalyst.

Uncovering fantastic synergistic lithium adsorption with manganese-titanium based composite nanospheres: Mild synthesis and molecular dynamics simulation insights

In light of the burgeoning energy technology sector and the ever-growing demand for lithium across diverse industrial domains, conventional lithium extraction methods have been proven inadequate due to their limited production capacity and high operational costs. This work introduces a novel approach to the manganese-titanium based composite HMTO (Mn:Ti=1:4) lithium ion-sieve (LIS) nanospheres, employing lithium acetate dihydrate, manganese carbonate and titanium dioxide P25 as the primary materials. These nanospheres exhibit relatively uniform spherical morphology, narrow size distribution, small average particle size (ca. 55 nm), large specific surface area (43.58 m2 g−1) and high surface O2− content (59.01%). When utilized as the adsorbents for Li+ ions, the HMTO (Mn:Ti=1:4) LIS demonstrates a fast adsorption rate, approaching equilibrium within 6.0 h with an equilibrium adsorption capacity (qe) of 79.5 mg g−1 and a maximum adsorption capacity (qm) of 87.26 mg g−1 (initial concentration C0: 1.8 g L−1). In addition, the HMTO (Mn:Ti=1:4) also delivers a high lithium extraction from the simulated high magnesium-lithium molar ratio salt lake brine (Mg:Li=103), achieving a qe of 33.85 mg g−1 along with a remarkable selectivity (αMgLi=2192.76). Particularly, the HMTO (Mn:Ti=1:4) LIS showcases a satisfactory recycling adsorption performance. The adsorption capacity remains at a high level, even that determined after the 5th cycle (55.45 mg g−1) surpasses that of the most recently reported adsorbents. Ultimately, the fantastic synergistic lithium adsorption mechanism is deliberately uncovered by leveraging the ion exchange principles and molecular dynamics (MD) simulations.

DNA Nanoparticle Based 2D Biointerface to Study the Effect of Dynamic RGD Presentation on Stem Cell Adhesion and Migration.

The native extracellular matrix (ECM) undergoes constant remodeling, where adhesive ligand presentation changes over time and in space to control stem cell function. As such, it is of interest to develop 2D biointerfaces able to study these complex ligand stem-cell interactions. In this study, a novel dynamic bio interface based on DNA hybridization is developed, which can be employed to control ligand display kinetics and used to study dynamic cell-ligand interaction. In this approach, mesoporous silica nanoparticles (MSN) are functionalized with single-strand DNA (MSN-ssDNA) and spin-coated on a glass substrate to create the 2D bio interface. Cell adhesive tripeptide RGD is conjugated to complementary DNA strands (csDNA) of 9, 11, or 20 nucleotides in length, to form csDNA-RGD. The resulting 3 csDNA-RGD conjugates can hybridize with the ssDNA on the MSN surface, presenting RGD with increased ligand dissociation rates as DNA length is shortened. Slow RGD dissociation rates led to enhanced stem cell adhesion and spreading, resulting in elongated cell morphology. Cells on surfaces with slow RGD dissociation rates also exhibited higher motility, migrating in multiple directions compared to cells on surfaces with fast RGD dissociation rates. This study contributes to the existing body of knowledge on dynamic ligand-stem cell interactions.

Exploring the DNA-binding and anticancer potential of polypyridyl ruthenium(II) complexes

Organometallic medicines based on ruthenium (Ru) have attracted interest as chemotherapeutic and bioimaging agents due to their low toxicity and superior physical optical characteristics. However, there are still no ideal candidates in clinical trials due to the lack of understanding of the structure-activity relationships (SARs) of polypyrodyl Ru(II) complexes. Though increasing the electron-donating ability of the ligands has been proven to benefit bioactivity due to the fast hydroxylation rate between chlorine and DNA, the total electronic effects are much more important, especially for the binding modes between Ru(II)-polypyridyl complexes and DNA. By evaluating the electron-donating ability of the ligand complexes 1 - 4 through density functional theory (DFT) calculations, bioactivities tests, and docking studies, we investigated the relationship between the structures of Ru(II) complexes and cytotoxicity. Our findings showed that it was insufficient to merely consider the impact of electron-donating effects on biological activities in place of interaction modes. Furthermore, the cellular imaging study investigated the complex with phenyl substituents (1) and confirmed that the main target was DNA. Besides, the “off-on” emission phenomena of complex 1 indicated that there is also covalent interaction with DNA. These findings offer valuable insight into the development of SARs of polypyrodyl Ru(II) complexes.

Titanium dioxide/graphene oxide synergetic reinforced composite phase change materials with excellent thermal energy storage and photo-thermal performances

The development of advanced composite solid-solid phase change materials (SSPCMs) is urgent to explore for improving solar energy harvesting and storage. Herein, novel composite SSPCMs with synergetic cross-linking structure were fabricated through polymerization using GO and TiO2 constructed on the polyurethane framework skeleton. GO and TiO2 synergetic enhanced polymer framework played a role as skeletal structure to encapsulate PEG in the molecular chains, and provided as highly thermal conductive pathways for the composite SSPCMs. TiO2 nanoparticles performed as extended surface on the skeletal structure for further improvement in thermal conductivity. The composite SSPCMs exhibited a remarkably improved thermal conductivity as high as 0.7 W/(m‧K) and fast thermal response rate. The good light adsorption property of TiO2 enhanced the light absorbance efficiency of the composite SSPCMs by 94.4%. And the photo-thermal conversion efficiency of the composite SSPCMs highly reached 93.5%. Meanwhile, the composite SSPCMs exhibited excellent anti-leakage performance and shape stability under high temperature. Consequently, the as-prepared composite SSPCMs possessed a potential for applications in thermal energy storage and solar energy utilization systems.

Widespread introduced species dominate the urban tree assemblage on the endemic-rich tropical island of São Tomé.

The Afrotropics are experiencing some of the fastest urbanisation rates on the planet but the impact of city growth on their rich and unique biodiversity remains understudied, especially compared to natural baselines. Little is also known about how introduced species influence β-diversity in these contexts, and how patterns coincide with native ranges of species. Here we investigated how tree assemblages of the endemic-rich Afrotropical island of São Tomé differed between urban, rural and natural zones. These were primarily characterised by urban greenspaces, shade plantations, and old-growth forests, respectively. Based on 81 transects, we assessed biodiversity metrics of endemic, native and introduced species. Tree abundance and species richness were highest in the natural zone, where the composition was most different from the urban zone. The tree community of the rural zone was the most uneven and had the least variation among transects, representing the lowest β-diversity. The urban zone was dominated by introduced species (57.7%), while the natural zone hosted almost exclusively native species (93.3%), including many endemics (26.1%). The biogeographic realms that species originated from were particularly diverse in the urban zone, with few species from the Afrotropics. In contrast to native and endemic trees, introduced trees were clearly associated with urban and rural expansion, as they were much more abundant and species-rich in these zones than in the natural zone, facilitating biotic homogenisation. These findings highlight how urban and rural environments are affecting the native tree flora of São Tomé, and the need for conservation measures geared towards globally threatened and endemic tree species. Importantly, these require the protection of natural forests, despite the rising land demands for settlements and agriculture. Ultimately, such action to conserve endemic trees will contribute to global efforts to prevent further biodiversity declines.

An FPGA-Implemented Antinoise Fuzzy Recurrent Neural Network for Motion Planning of Redundant Robot Manipulators.

When a robot completes end-effector tasks, internal error noises always exist. To resist internal error noises of robots, a novel fuzzy recurrent neural network (FRNN) is proposed, designed, and implemented on field-programmable gated array (FPGA). The implementation is pipeline-based, which guarantees the order of overall operations. The data processing is based on across-clock domain, which is beneficial for computing units' acceleration. Compared with traditional gradient-based neural networks (NNs) and zeroing neural networks (ZNNs), the proposed FRNN has faster convergence rate and higher correctness. Practical experiments on a 3 degree-of-freedom (DOs) planar robot manipulator show that the proposed fuzzy RNN coprocessor needs 496 lookup table random access memories (LUTRAMs), 205.5 block random access memories (BRAMs), 41384 lookup tables (LUTs), and 16743 flip-flops (FFs) of the Xilinx XCZU9EG chip.

Bridging shores: Leveraging green finance, financial globalization, and human capital for a cleaner environment in G-20 marine ecosystems using the fishing ground capability curve theory

The G-20 nations, that are industrializing at the fastest rate in the world, are dealing with serious environmental issues, particularly those pertaining to marine habitats. Because economic activity is affecting these environmental and marine ecosystems drastically, it is necessary to probe the factors that can promote sustainable development in developed and developing nations. Thus, understanding the possible effects of financial globalization (FG), green finance (GFN), and economic expansion on marine health is paramount. The study uses advanced methodologies to examine potential intersections between economic growth (GDP) and FG on the marine ecosystem of the G-20 countries between 2000 and 2022 in the context of fishing grounds load capability Curve (FGLCC) framework by showing a nonlinear relationship between marine conditions and income levels. Human capital is exerting positive impacts of (5.640–6.743 %) but FG is impacting (-4.182 to −5.490) on FGLCC. Furthermore, the work emphasizes the necessity of policies that strike a balance between the G-20's marine environmental sustainability and economic development, stressing the significance of deliberate legislative and strategic interventions. It is necessary to enact laws that favor sustainable fishing methods rather than focusing solely on boosting GDP through intensive fishing. Possible actions may involve allocating resources towards research and development in efficient fishing techniques, advocating for the adoption of environmentally friendly fishing equipment, and helping for the adoption of technologies that minimize unintended catch and damage to habitats. These discoveries have important ramifications for marine policymakers as they tackle marine environmental issues in the face of economic development. They are essential for improving marine requirements and safeguarding the health and well-being of future generations.

A Study On The Impact Of Digital Marketing On Consumer Buying Behaviour

In customer-oriented marketing environment, the buying pattern of consumer changes at a fast rate. The consumer behaviour is influenced by various factors like price, features, quality, packaging, status, generation, age of the customer, etc. the technological factor influences consumer buying behaviour to a great extent. New innovations in technology have brought challenges and opened up opportunities for existing businesses. Businesses all over the world have been adapting technology related strategies to enable them to maximise customer networks. Technology has revolutionised marketing, moving it from more conventional forms to digital marketing, which uses digital media to communicate marketing material. A significant portion of customer purchasing behaviour is influenced by digital marketing. Digital platforms like blogs, websites and social networks are crucial for boosting a business’s sales and strengthening its clientele. Therefore, its crucial to comprehend how and where these channels affect consumer behaviour. This paper focuses on how consumer purchasing behaviour is impacted by digital ads, social media, blogs and reviews. A survey is made to perform the research and each respondent is given a well-crafted questionnaire. Analysis and interpretation are limited to the data obtained from the questionnaire.

Study on the hydration characteristics, mechanical properties, and microstructure of thermally activated low-carbon recycled cement

Based on the thermal activation method, utilizing fine particles of waste concrete to prepare low-carbon recycled cement (RC) is currently one of the research hotspots. Based on existing literature research results, this paper identifies four calcination temperature conditions (120, 450, 750, 1000 ℃) and uses replacement rates of 10 %, 30 %, 50 %, 70 %, and 100 % of RC to replace ordinary Portland cement (OPC).The main physical properties, hydration characteristics, mechanical properties, and microstructure of RC are studied. The study elucidated the influence of RC thermal activation temperature, replacement rate, fineness, and age on its hydration characteristics, mechanical properties, and microstructure, establishing a macro-micro correlation for RC and revealing the mechanism of improvement in mechanical properties of thermal-activated RC. The research indicates that in the initial stage of hydration, RC exhibits a relatively fast hydration rate, with higher initial total heat release than OPC. Hydration products include internal hydration products (formation of C-(A)-S-H gel) and external hydration products (formation of plate-shaped AFm). Furthermore, RC exhibits a faster hydration rate at a hot activation temperature of 750°C, with CH and C-S-H gel transforming into the highly hydrated α´L-C2S, forming a denser microstructure at an early stage. Moreover, at a replacement rate of 30 %, the compressive strength of cement mortar reaches its optimum at 28 days, achieving 13.81 MPa, equivalent to 60.7 % of OPC cement mortar, with a 66.67 % reduction in carbon emissions. Microstructure analysis reveals that when the temperature reaches 1000°C, excessive combustion of RC leads to the formation of low-reactivity C2AS and β-C2S, resulting in a decrease in macroscopic mechanical properties. Additionally, increasing the fineness of RC significantly enhances its rehydration capability, especially as the age increases. This study is of great significance for the preparation of green cement-based composite cementitious materials.

Mapping high-resolution XCO2 concentrations in China from 2015 to 2020 based on spatiotemporal ensemble learning model

High-resolution column-averaged dry air mole fraction of CO2 (XCO2) data is crucial for understanding the spatiotemporal patterns of XCO2 and for mitigating carbon emissions. Due to the limited scanning range of sensors and strict inversion conditions, satellite-retrieved XCO2 data are often significantly incomplete. Machine learning models are widely used to fill these gaps in satellite XCO2 data. However, the limitations of individual machine learning models and the complexity of the spatial distribution of XCO₂ mean that the accuracy of XCO2 predictions still needs improvement. In this study, a new spatiotemporal stacked ensemble learning model (STEL) was developed by combining random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), optical gradient boosting (LightGBM), and categorical boosting (CatBoost) using the stacking ensemble learning methodology. Considering the spatiotemporal heterogeneity of XCO2, a novel spatiotemporal weighting feature was constructed as part of the model's input parameters. Finally, the XCO2 observed by Orbiting Carbon Observatory 2 (OCO-2) was reconstructed using STEL, and a monthly mean XCO2 dataset covering China from 2015 to 2020 was generated at a spatial resolution of 0.1°. The results show that STEL exhibits superior performance and generalization capabilities compared to individual machine-learning models. R2 RMSE and MAPE were 0.9624, 1.0023 ppm, and 0.1583 % on the test set, and 0.8970, 1.4213 ppm, and 0.2475 % for R2, RMSE, and MAPE in ground validation, respectively. In 10-fold cross-validation, STEL's RMSE was reduced by 9.52 % compared to the best-performing single model (RF). The spatiotemporal trend of CO2 in China from 2015 to 2020 was analyzed using STEL XCO2 data. The results indicate that this dataset accurately reflects the spatiotemporal heterogeneity of XCO2 distribution at a fine scale. Overall, XCO2 exhibited a spatiotemporal pattern of “high in the east and low in the west” and “high in spring and low in summer.” Except in summer, high XCO₂ values were mainly distributed in the North China Plain. XCO2 trends and hotspots showed considerable spatial variation. The Pearl River Delta and Yangtze River Delta urban agglomerations have the fastest XCO2 growth rates, and the distribution of XCO2 hotspots is consistent with the distribution of population and economic centers. In the sparsely populated northwest of China, XCO2 is growing rapidly due to increased thermal power generation and coal mining. XCO2 hotspots in Northwest China are mainly located in Xinjiang, Ningxia, and Inner Mongolia. The methodology and data presented are useful for further research on carbon emissions, carbon sinks, and climate change.

Living in the Age of Deepfakes: A Bibliometric Exploration of Trends, Challenges, and Detection Approaches

In an era where all information can be reached with one click and by using the internet, the risk has increased in a significant manner. Deepfakes are one of the main threats on the internet, and affect society by influencing and altering information, decisions, and actions. The rise of artificial intelligence (AI) has simplified the creation of deepfakes, allowing even novice users to generate false information in order to create propaganda. One of the most prevalent methods of falsification involves images, as they constitute the most impactful element with which a reader engages. The second most common method pertains to videos, which viewers often interact with. Two major events led to an increase in the number of deepfake images on the internet, namely the COVID-19 pandemic and the Russia–Ukraine conflict. Together with the ongoing “revolution” in AI, deepfake information has expanded at the fastest rate, impacting each of us. In order to reduce the risk of misinformation, users must be aware of the deepfake phenomenon they are exposed to. This also means encouraging users to more thoroughly consider the sources from which they obtain information, leading to a culture of caution regarding any new information they receive. The purpose of the analysis is to extract the most relevant articles related to the deepfake domain. Using specific keywords, a database was extracted from Clarivate Analytics’ Web of Science Core Collection. Given the significant annual growth rate of 161.38% and the relatively brief period between 2018 and 2023, the research community demonstrated keen interest in the issue of deepfakes, positioning it as one of the most forward-looking subjects in technology. This analysis aims to identify key authors, examine collaborative efforts among them, explore the primary topics under scrutiny, and highlight major keywords, bigrams, or trigrams utilized. Additionally, this document outlines potential strategies to combat the proliferation of deepfakes in order to preserve information trust.

Wrinkled hierarchical porous carbon spheres with interconnected multi-cavity for ultrahigh capacitive deionization

As one of the most promising electrode materials for capacitive deionization (CDI), the development of carbon materials with controllable pore structure and continuous mass production is essential for their practical application. Herein, a facile ultrasonic spray pyrolysis method was developed to synthesize surface-functionalized wrinkled hierarchical porous carbon spheres (HCS) with unique interconnected multi-cavity structures. The wrinkled and interconnected multi-cavity hierarchical pores of the HCS play a crucial role in providing accessible ion adsorption sites and promoting ion diffusion and storage in the “multi-cavity warehouse”. The carboxyl groups on the surface of HCS generate a negative charge that promotes the adsorption of cations. The optimized HCS possesses outstanding desalination capacity (114.25 mg g−1), fast adsorption rate (6.57 mg g−1 min−1), and superior cycling stability (95%). Meanwhile, the HCS exhibited impressive desalination capacities in brackish water. Furthermore, the density functional theory calculation results confirmed that the synergistic effect of carboxyl groups and defects significantly enhanced the Na+ adsorption capacity and facilitated ion diffusion. This study extends the synthesis method of surface-functionalized hierarchical porous carbon, which is expected to facilitate the development of CDI electrode materials.

Physiological properties of auditory neurons responding to omission deviants in the anesthetized rat

SummaryThe detection of novel, low probability events in the environment is critical for survival. To perform this vital task, our brain is continuously building and updating a model of the outside world; an extensively studied phenomenon commonly referred to as predictive coding. Predictive coding posits that the brain is continuously extracting regularities from the environment to generate predictions. These predictions are then used to supress neuronal responses to redundant information, filtering those inputs, which then automatically enhances the remaining, unexpected inputs.We have recently described the ability of auditory neurons to generate predictions about expected sensory inputs by detecting their absence in an oddball paradigm using omitted tones as deviants. Here, we studied the responses of individual neurons to omitted tones by presenting individual sequences of repetitive pure tones, using both random and periodic omissions, presented at both fast and slow rates in the inferior colliculus and auditory cortex neurons of anesthetized rats. Our goal was to determine whether feature-specific dependence of these predictions exists. Results showed that omitted tones could be detected at both high (8 Hz) and slow repetition rates (2 Hz), with detection being more robust at the non-lemniscal auditory pathway.