The microstructure characteristics of TiAl alloy prepared by laser-directed energy deposition (L-DED) are coarse columnar grains parallel to the building direction, which results in serious mechanical properties and anisotropy and limits its application. In the present study, TiB₂ can be used as an effective grain refiner due to the extremely high Q value (growth inhibition factor; the larger the Q value of an alloying element, the stronger its grain refinement effect.) of B. With TiB₂ addition, TiAl alloys prepared by laser-directed energy deposition with the microstructure of full equiaxed grains were obtained, and the grain size was significantly reduced by about 30% with 0.45 wt.% TiB₂. This value has been further increased to 45% when adding 0.9 wt.% TiB₂. Moreover, the γ_m phase was nearly eliminated and the width of (α2 + γ) lamellar was significantly decreased, which has positive effects on mechanical properties. Meanwhile, TiB₂ precipitates uniformly distribute in the matrix, as a reinforced particle to increase the hardness and compressive strength of the alloys. The microhardness of the TiAl alloy increased with the increasing content of TiB₂. The addition of TiB₂ improved the room and high-temperature compressive properties of TiAl alloy while slightly increasing its ductility. These findings have important guiding significance for expanding the application of TiAl alloys. Full article

Intensive economic and human activities present challenges to the carbon storage capacity of terrestrial ecosystems, particularly in arid regions that are sensitive to climate change and ecologically fragile. Therefore, accurately estimating and simulating future changes in carbon stocks on the northern slope economic belt of Tianshan Mountains (NSEBTM) holds great significance for maintaining ecosystem stability, achieving high-quality development of the economic belt, and realizing the goal of “carbon neutrality” by 2050. This study examines the spatiotemporal evolution characteristics of the NSEBTM carbon stocks in arid regions from 1990 to 2050, utilizing a combination of multi-source data and integrating the Patch-generating Land use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models. Additionally, an attribution analysis of carbon stock changes is conducted by leveraging land use data. The findings demonstrate that (1) the NSEBTM predominantly consists of underutilized land, accounting for more than 60% of the total land area in the NSEBTM. Unused land, grassland, and water bodies exhibit a declining trend over time, while other forms of land use demonstrate an increasing trend. (2) Grassland serves as the primary reservoir for carbon storage in the NSEBTM, with grassland degradation being the leading cause of carbon loss amounting to 102.35 t over the past three decades. (3) Under the ecological conservation scenario for 2050 compared to the natural development scenario, there was a net increase in carbon storage by 12.34 t; however, under the economic development scenario compared to the natural development scenario, there was a decrease in carbon storage by 25.88 t. By quantitatively evaluating the land use change in the NSEBTM and its impact on carbon storage in the past and projected for the next 30 years, this paper provides scientific references and precise data support for the territorial and spatial decision making of the NSEBTM, thereby facilitating the achievement of “carbon neutrality” goals. Full article

The calculation of satellite orbit involves some very complex formula derivations and expansions, which are very difficult to manually derive and prone to errors. And the efficiency of manual derivation is not high. We can use computer algebra systems to derive complex formulas related to satellite orbits. This can avoid some of the drawbacks of manual derivation and significantly improve computational efficiency and accuracy. In the past, the relationship among three anomalies was generally represented in the form of a trigonometric series with the first eccentricity $e$ as the parameter. In this paper, the trigonometric series with the parameter $m=\frac{1-\sqrt{1-e^2}}{e}$ is used, as determined by the Lagrange conjugate series. We can use the formula of the Lagrange conjugate series to derive the relationship between the true anomaly and elliptic anomaly. And the relationship between the elliptic anomaly and the mean anomaly is derived by using the symbolic iteration method. In this research paper, we calculated the accuracy of the trigonometric series expansion among three types of anomalies at the first eccentricity $e$ equal to values of 0.01, 0.1, and 0.2. The calculation results indicate that the accuracy of the trigonometric series expansion with $m$ as the parameter is better than 10⁻⁵. Moreover, in some cases, the trigonometric series expansion among the three anomalies with $m$ as a parameter is simpler in form than the expansion expressed with parameter $e$. This paper also derived and calculated the symbolic expressions and extreme values of the difference among three anomalies and expressed the extreme values of the difference in the form of a power series of $e$. It can be seen that the extreme value increases with the increase in eccentricity $e$. And the absolute values of the extreme value of the difference between the elliptic anomaly and the mean anomaly, the true anomaly and the elliptic anomaly, and the true anomaly and the mean anomaly increase in this order. When the eccentricity is small, the absolute value of the extreme value of the difference between the true anomaly and the mean anomaly is about twice as large as the elliptic anomaly and the mean anomaly and the true anomaly and the mean anomaly. Full article

Due to the increasing demand for transport of electrical energy, measures for power flow control, congestion management, and higher utilisation of the existing grid play a decisive role in the transformation of the power system. Hence, enormous efforts must be undertaken using measures of congestion management. Modelling and integration of corresponding measures in optimisation tools to support grid and system operation and therewith reduce the resulting efforts become more important. This is especially true because of the high intermittency and decentralisation of renewable generation leading to increased complexity of the power system, higher loading of assets, and a growing need for control over flexible alternating current transmission systems (FACTS) and high-voltage direct current (HVDC) converters. This work therefore describes the implementation of optimised congestion management in an A Mathematical Programming Language (AMPL)-based nonlinear optimisation problem. AMPL is an effective tool to deal with highly complex problems of optimisation and scheduling. Therefore, the modelling of assets and flexibilities for power flow control in AC/DC systems in combination with an innovative grid operation strategy using predefined curative measures for the optimised use of the existing grid is introduced. The nonlinear mathematical optimisation aims at the optimal cost selection of flexibility measures. The application of the optimisation technique in a combined AC/DC system shows the optimal preventive and curative use of measures in operational congestion management. Simulation results prove that, by using predefined curative measures, the volume of cost-intensive preventive measures can significantly be reduced, especially in association with power flow control. Full article

Protein–ligand docking plays a significant role in structure-based drug discovery. This methodology aims to estimate the binding mode and binding free energy between the drug-targeted protein and candidate chemical compounds, utilizing protein tertiary structure information. Reformulation of this docking as a quadratic unconstrained binary optimization (QUBO) problem to obtain solutions via quantum annealing has been attempted. However, previous studies did not consider the internal degrees of freedom of the compound that is mandatory and essential. In this study, we formulated fragment-based protein–ligand flexible docking, considering the internal degrees of freedom of the compound by focusing on fragments (rigid chemical substructures of compounds) as a QUBO problem. We introduced four factors essential for fragment–based docking in the Hamiltonian: (1) interaction energy between the target protein and each fragment, (2) clashes between fragments, (3) covalent bonds between fragments, and (4) the constraint that each fragment of the compound is selected for a single placement. We also implemented a proof-of-concept system and conducted redocking for the protein–compound complex structure of Aldose reductase (a drug target protein) using SQBM+, which is a simulated quantum annealer. The predicted binding pose reconstructed from the best solution was near-native ($\mathrm{RMSD}=1.26$ Å), which can be further improved ($\mathrm{RMSD}=0.27$ Å) using conventional energy minimization. The results indicate the validity of our QUBO problem formulation. Full article

Speech is a crucial source of personal information, and the risk of attackers using such information increases day by day. Speaker privacy protection is crucial, and various approaches have been proposed to hide the speaker’s identity. One approach is voice anonymization, which aims to safeguard speaker identity while maintaining speech content through techniques such as voice conversion or spectral feature alteration. The significance of voice anonymization has grown due to the necessity to protect personal information in applications such as voice assistants, authentication, and customer support. Building upon the S3PRL-VC toolkit and on pre-trained speech and speaker representation models, this paper introduces a feature disentanglement approach to improve the de-identification performance of the state-of-the-art anonymization approaches based on voice conversion. The proposed approach achieves state-of-the-art speaker de-identification and causes minimal impact on the intelligibility of the signal after conversion. Full article

During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids and integrate hybrid AC/DC grids. This paper compares SST to conventional copper-based power transformers (CPT) with and without an on-load tap changer (OLTC) and with additional downstream converters. For this purpose, a corresponding electricity distribution grid is set up in the power system analysis tool DIgSILENT PowerFactory 2022. A DC generator like a photovoltaic system, a DC load like an electric vehicle fast charging station, and an AC load are connected. Based on load flow simulations, the four power transformers are compared concerning voltage stability during a generator-based and a load-based scenario. The results of load flow simulations show that SSTs are most valuable when additional generators and loads are to be connected to the infrastructure, which would overload the existing grid equipment. The efficiency of using SSTs also depends on the parameters of the electrical grid, especially the lengths of the low-voltage (LV) lines. In addition, a flowchart-based decision process is proposed to support the decision-making process for the appropriate power transformer from an electrical perspective. Beyond these electrical properties, an evaluation matrix lists other relevant criteria like characteristics of the installation site, noise level, expected lifetime, and economic criteria that must be considered. Full article

Knowledge of a person’s level of skin pigmentation, or so-called “skin tone”, has proven to be an important building block in improving the performance and fairness of various applications that rely on computer vision. These include medical diagnosis of skin conditions, cosmetic and skincare support, and face recognition, especially for darker skin tones. However, the perception of skin tone, whether by the human eye or by an optoelectronic sensor, uses the reflection of light from the skin. The source of this light, or illumination, affects the skin tone that is perceived. This study aims to refine and assess a convolutional neural network-based skin tone estimation model that provides consistent accuracy across different skin tones under various lighting conditions. The 10-point Monk Skin Tone Scale was used to represent the skin tone spectrum. A dataset of 21,375 images was captured from volunteers across the pigmentation spectrum. Experimental results show that a regression model outperforms other models, with an estimated-to-target distance of 0.5. Using a threshold estimated-to-target skin tone distance of 2 for all lights results in average accuracy values of 85.45% and 97.16%. With the Monk Skin Tone Scale segmented into three groups, the lighter exhibits strong accuracy, the middle displays lower accuracy, and the dark falls between the two. The overall skin tone estimation achieves average error distances in the LAB space of $16.40\pm 20.62$. Full article

The addition of soil amendments and plants in heavy metal-contaminated soil can result in a significant impact on physicochemical properties, microbial communities and heavy metal distribution, but the specific mechanisms remain to be explored. In this study, Koelreuteria paniculata was used as a test plant, spent mushroom compost (SMC) and attapulgite (ATP) were used as amendments, and manganese slag was used as a substrate. CK (100% slag), M0 (90% slag + 5% SMC + 5% ATP) and M1 (90% slag + 5% SMC + 5% ATP, planting K. paniculata) groups were assessed in a pilot-scale experiment to explore their different impacts on phytoremediation. The results indicated that adding the amendments significantly improved the pH of the manganese slag, enhancing and maintaining its fertility and water retention. Adding the amendments and planting K. paniculata (M1) significantly reduced the bioavailability and migration of heavy metals (HMs). The loss of Mn, Pb and Zn via runoff decreased by 15.7%, 8.4% and 10.2%, respectively, compared to CK. K. paniculata recruited and enriched beneficial fungi, inhibited pathogenic fungi, and a more stable fungal community was built. This significantly improved the soil quality, promoted plant growth and mitigated heavy metal toxicity. In conclusion, this study demonstrated that the addition of SMC-ATP and planting K. paniculata showed a good phytostabilization effect in the manganese slag and further revealed the response process of the fungal community in phytoremediation. Full article

CACNA1C gene encodes the alpha 1 subunit of the CaV1.2 L-type Ca2+ channel. Pathogenic variants in this gene have been associated with cardiac rhythm disorders such as long QT syndrome, Brugada syndrome and Timothy syndrome. Recent evidence has suggested the possible association between CACNA1C mutations and neurologically-isolated (in absence of cardiac involvement) phenotypes in children, giving birth to a wider spectrum of CACNA1C-related clinical presentations. However, to date, little is known about the variety of both neurological and non-neurological signs/symptoms in the neurologically-predominant phenotypes. Methods and results: We conducted a systematic review of neurologically-predominant presentations without cardiac conduction defects, associated with CACNA1C mutations. We also reported a novel de novo missense pathogenic variant in the CACNA1C gene of a children patient presenting with constructional, dressing and oro-buccal apraxia associated with behavioral abnormalities, mild intellectual disability, dental anomalies, gingival hyperplasia and mild musculoskeletal defects, without cardiac conduction defects. Conclusions: The present study highlights the importance of considering the investigation of the CACNA1C gene in children’s neurological isolated syndromes, and expands the phenotype of the CACNA1C related conditions. In addition, the present study highlights that, even in absence of cardiac conduction defects, nuanced clinical manifestations of the Timothy syndrome (e.g., dental and gingival defects) could be found. These findings suggest the high variable expressivity of the CACNA1C gene and remark that the absence of cardiac involvement should not mislead the diagnosis of a CACNA1C related disorder. Full article

To predict the maneuverability of a dual full rotary propulsion ship quickly and accurately, the integrated computational fluid dynamics (CFD) and mathematical model approach is performed to simulate the ship turning and zigzag tests, which are then compared and validated against a full-scale trial carried out under actual sea conditions. Initially, the RANS equations are solved, employing the Volume of Fluid (VOF) method to capture the free water surface, while a numerical simulation of the captive model test is conducted using the rigid body motion module. Secondly, hydrodynamic derivatives for the MMG model are obtained from the CFD simulations and empirical formula. Lastly, a four-degree-of-freedom mathematical model group (MMG) maneuvering model is proposed for the dual full rotary propulsion ship, incorporating full-scale simulations of turning and zigzag tests followed by a full-scale trial for comparative validation. The results indicate that the proposed method has a high accuracy in predicting the maneuverability of dual full-rotary propulsion ships, with an average error of less than 10% from the full-scale trial data (and within 5% for the tactical diameters in particular) in spite of the influence of environmental factors such as wind and waves. It provides experience in predicting the maneuverability of a full-scale ship during the ship design stage. Full article

Machine learning methods have been recognized as rapid methods for satellite-based aerosol retrievals but have not been widely applied in geostationary satellites. In this study, we developed a wide and deep learning model to retrieve the aerosol optical depth (AOD) using Himawari-8. Compared to traditional deep learning methods, we embedded a “wide” modeling component and tested the proposed model across China using independent training (2016–2018) and test (2019) datasets. The results showed that the “wide” model improves the accuracy and enhances model interpretability. The estimates exhibited better accuracy (R² = 0.81, root-mean-square errors (RMSEs) = 0.19, and within the estimated error (EE) = 63%) than those of the deep-only models (R² = 0.78, RMSE = 0.21, within the EE = 58%). In comparison with extreme gradient boosting (XGBoost) and Himawari-8 V2.1 AOD products, there were also significant improvements. In addition to higher accuracy, the interpretability of the proposed model was superior to that of the deep-only model. Compared with other seasons, higher contributions of spring to the AOD concentrations were interpreted. Based on the application of the wide and deep learning model, the near-real-time variation of the AOD over China could be captured with an ultrafine temporal resolution. Full article

Recognizing 3D objects from point clouds is a crucial technology for autonomous vehicles. Nevertheless, LiDAR (Light Detection and Ranging) point clouds are generally sparse, and they provide limited contextual information, resulting in unsatisfactory recognition performance for distant or small objects. Consequently, this article proposes an object recognition algorithm named Adaptive Scale and Correlative Attention PointPillars (ASCA-PointPillars) to address this problem. Firstly, an innovative adaptive scale pillars (ASP) encoding method is proposed, which encodes point clouds using pillars of varying sizes. Secondly, ASCA-PointPillars introduces a feature enhancement mechanism called correlative point attention (CPA) to enhance the feature associations within each pillar. Additionally, a data augmentation algorithm called random sampling data augmentation (RS-Aug) is proposed to solve the class imbalance problem. The experimental results on the KITTI 3D object dataset demonstrate that the proposed ASCA-PointPillars algorithm significantly boosts the recognition performance and RS-Aug effectively enhances the training effects on an imbalanced dataset. Full article

Peptide antigens derived from tumors have been observed to elicit protective immune responses, categorized as either tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs). Subunit cancer vaccines incorporating these antigens have shown promise in inducing protective immune responses, leading to cancer prevention or eradication. Over recent years, peptide-based cancer vaccines have gained popularity as a treatment modality and are often combined with other forms of cancer therapy. Several clinical trials have explored the safety and efficacy of peptide-based cancer vaccines, with promising outcomes. Advancements in techniques such as whole-exome sequencing, next-generation sequencing, and in silico methods have facilitated the identification of antigens, making it increasingly feasible. Furthermore, the development of novel delivery methods and a deeper understanding of tumor immune evasion mechanisms have heightened the interest in these vaccines among researchers. This article provides an overview of novel insights regarding advancements in the field of peptide-based vaccines as a promising therapeutic avenue for cancer treatment. It summarizes existing computational methods for tumor neoantigen prediction, ongoing clinical trials involving peptide-based cancer vaccines, and recent studies on human vaccination experiments. Full article

The constant and dynamic interaction between ventricular function and arterial afterload, known as ventricular-arterial coupling, is key to understanding cardiovascular pathophysiology. Ventricular–arterial coupling has traditionally been assessed invasively as the ratio of effective arterial elastance over end-systolic elastance (E_a/E_es), calculated from information derived from pressure–volume loops. Over the past few decades, numerous invasive and non-invasive simplified methods to estimate the elastance ratio have been developed and applied in clinical investigation and practice. The echocardiographic assessment of left ventricular E_a/E_es, as proposed by Chen and colleagues, is the most widely used method, but novel echocardiographic approaches for ventricular–arterial evaluation such as left ventricle outflow acceleration, pulse-wave velocity, and the global longitudinal strain or global work index have arisen since the former was first published. Moreover, multimodal imaging or artificial intelligence also seems to be useful in this matter. This review depicts the progressive development of these methods along with their academic and clinical application. The left ventricular–arterial coupling assessment may help both identify patients at risk and tailor specific pharmacological or interventional treatments. Full article

In this paper, we present a novel coordinated method tailored to address the dynamic multi-target hunting control problem in multi-agent systems, offering significant practical value. Our approach encompasses several key components: initially, we introduce a task allocation model that integrates a fuzzy inference system with a particle swarm optimization algorithm. This hybrid model efficiently allocates hunting tasks for scattered evading targets, effectively transforming the dynamic multi-target hunting problem into multiple dynamic single-target-hunting problems. This transformation enhances the speed and efficacy of task allocation. Subsequently, we propose an attraction/repulsive model grounded in potential field theory. This model facilitates the coordinated hunting of each target by organizing agents into subgroups. Relying solely on relative position and velocity information between agents and targets, our model simplifies computation, while maintaining effectiveness. Furthermore, the coordination of hunting activities for each target is achieved through a series of agent subgroups, guided by our proposed motion model. This systematic approach ensures a cohesive and efficient hunting strategy. Finally, we validate the effectiveness and feasibility of our proposed method through simulation results. These results provide empirical evidence of the method’s efficacy and potential applicability in real-world scenarios. Full article

Microenvironment and transcriptional plasticity generate subpopulations within the tumor, and the use of BRAF inhibitors (BRAFis) contributes to the rise and selection of resistant clones. We stochastically isolated subpopulations (C1, C2, and C3) from naïve melanoma and found that the clones demonstrated distinct morphology, phenotypic, and functional profiles: C1 was less proliferative, more migratory and invasive, less sensitive to BRAFis, less dependent on OXPHOS, more sensitive to oxidative stress, and less pigmented; C2 was more proliferative, less migratory and invasive, more sensitive to BRAFis, less sensitive to oxidative stress, and more pigmented; and C3 was less proliferative, more migratory and invasive, less sensitive to BRAFis, more dependent on OXPHOS, more sensitive to oxidative stress, and more pigmented. Hydrogen peroxide plays a central role in oxidative stress and cell signaling, and PRDXs are one of its main consumers. The intrinsically resistant C1 and C3 clones had lower MITF, PGC-1α, and PRDX1 expression, while C1 had higher AXL and decreased pigmentation markers, linking PRDX1 to clonal heterogeneity and resistance. PRDX2 is depleted in acquired BRAFi-resistant cells and acts as a redox sensor. Our results illustrate that decreased pigmentation markers are related to therapy resistance and decreased antioxidant defense. Full article

SET-domain group histone methyltransferases (SDGs) are known to play crucial roles in plant responses to abiotic stress. However, their specific function in cotton’s response to drought stress has not been well understood. This study conducted a comprehensive analysis of the SDG gene family in Gossypium hirsutum, identifying a total of 82 SDG genes. An evolutionary analysis revealed that the SDG gene family can be divided into eight subgroups. The expression analysis shows that some GhSDG genes are preferentially expressed in specific tissues, indicating their involvement in cotton growth and development. The transcription level of some GhSDG genes is induced by PEG, with GhSDG59 showing significant upregulation upon polyethylene glycol (PEG) treatment. Quantitative polymerase chain reaction (qPCR) analysis showed that the accumulation of transcripts of the GhSDG59 gene was significantly upregulated under drought stress. Further functional studies using virus-induced gene silencing (VIGS) revealed that silencing GhSDG59 reduced cotton tolerance to drought stress. Under drought conditions, the proline content, superoxide dismutase (SOD) and peroxidase (POD) enzyme activities in the GhSDG59-silenced plants were significantly lower than in the control plants, while the malondialdehyde (MDA) content was significantly higher. Transcriptome sequencing showed that silencing the GhSDG59 gene led to significant changes in the expression levels of 1156 genes. The KEGG enrichment analysis revealed that these differentially expressed genes (DEGs) were mainly enriched in the carbon metabolism and the starch and sucrose metabolism pathways. The functional annotation analysis identified known drought-responsive genes, such as ERF, CIPK, and WRKY, among these DEGs. This indicates that GhSDG59 is involved in the drought-stress response in cotton by affecting the expression of genes related to the carbon metabolism and the starch and sucrose metabolism pathways, as well as known drought-responsive genes. This analysis provides valuable information for the functional genomic study of SDGs and highlights potential beneficial genes for genetic improvement and breeding in cotton. Full article

The sandstone-type uranium deposit of the Kelulun Depression is the first industrially valuable uranium deposit discovered in the Hailar Basin. This study performed a systematic examination of 17 sandstone samples from the Yimin Formation in the Kelulun Depression based on various analytical techniques. The findings of the current study were synthesized with previous research to investigate the impact of the redox conditions and the tectonic background of the source area, as well as the paleoclimatic evolution of the Yimin Formation on uranium mineralization. The elemental Mo, U/Th, V/Cr, Ni/Co, and V/(V+Ni) ratios indicate that the paleowater was in an oxygen-rich environment during the deposition of the Yimin Formation. Additionally, the C-value, Sr/Cu, Al₂O₃/MgO, and Rb/Sr ratios indicate that the Yimin Formation was formed in a paleoclimate characterized by arid-to-semi-arid conditions. The geochemical characteristics of the observed elements indicated that the sediment source of the Yimin Formation was mainly felsic rocks from the upper continental crust, the weathering of the rock was weak, and the tectonic background was a passive continental margin. Coffinite is distributed in the form of cementation and stellates within or around pyrite crystals, and uranium-titanium oxide is mostly distributed in an irregular granular distribution in the biotite cleavage fractures of the study area. In summary, the findings of this study reveal that the tectonic settings, provenance, uranium source, paleoclimate, and oxygen-rich paleowater of the Yimin Formation have important geological significance for the large-scale uranium mineralization of the Kelulun Depression. Full article

High-nickel ternary materials are currently the most promising lithium battery cathode materials due to their development and application potential. Nevertheless, these materials encounter challenges like cation mixing, lattice oxygen loss, interfacial reactions, and microcracks. These issues are exacerbated at high voltages, compromising their cyclic stability and safety. In this study, we successfully prepared Nb⁵⁺-doped high-nickel ternary cathode materials via a high-temperature solid-phase method. We investigated the impact of Nb⁵⁺ doping on the microstructure and electrochemical properties of LiNi_0.88Co_0.05Mn_0.07O₂ ternary cathode materials by varying the amount of Nb₂O₅ added. The experimental results suggest that Nb⁵⁺ doping does not alter the crystal structure but modifies the particle morphology, yielding radially distributed, elongated, rod-like structures. This morphology effectively mitigates the anisotropic volume changes during cycling, thereby bolstering the material’s cyclic stability. The material exhibits a discharge capacity of 224.4 mAh g⁻¹ at 0.1C and 200.3 mAh g⁻¹ at 1C, within a voltage range of 2.7 V–4.5 V. Following 100 cycles at 1C, the capacity retention rate maintains a high level of 92.9%, highlighting the material’s remarkable capacity retention and cyclic stability under high-voltage conditions. The enhancement of cyclic stability is primarily due to the synergistic effects caused by Nb⁵⁺ doping. Nb⁵⁺ modifies the particle morphology, thereby mitigating the formation of microcracks. The formation of high-energy Nb-O bonds prevents oxygen precipitation at high voltages, minimizes the irreversibility of the H2–H3 phase transition, and thereby enhances the stability of the composite material at high voltages. Full article

Manganese (Mn), a cofactor for various enzyme classes, is an essential trace metal for all organisms. However, overexposure to Mn causes neurotoxicity. Here, we evaluated the effects of exposure to Mn chloride (MnCl₂) on viability, morphology, synapse function (based on neurogranin expression) and behavior of zebrafish larvae. MnCl₂ exposure from 2.5 h post fertilization led to reduced survival (60%) at 5 days post fertilization. Phenotypical changes affected body length, eye and olfactory organ size, and visual background adaptation. This was accompanied by a decrease in both the fluorescence intensity of neurogranin immunostaining and expression levels of the neurogranin-encoding genes nrgna and nrgnb, suggesting the presence of synaptic alterations. Furthermore, overexposure to MnCl₂ resulted in larvae exhibiting postural defects, reduction in motor activity and impaired preference for light environments. Following the removal of MnCl₂ from the fish water, zebrafish larvae recovered their pigmentation pattern and normalized their locomotor behavior, indicating that some aspects of Mn neurotoxicity are reversible. In summary, our results demonstrate that Mn overexposure leads to pronounced morphological alterations, changes in neurogranin expression and behavioral impairments in zebrafish larvae. Full article

West Nile virus (WNV) is an arbovirus spread primarily by Culex mosquitoes, with humans being a dead-end host. WNV was introduced to Florida in 2001, with 467 confirmed cases since. It is estimated that 80 percent of cases are asymptomatic, with mild cases presenting as a non-specific flu-like illness. Currently, detection of WNV in humans occurs primarily in healthcare settings via RT-PCR or CSF IgM when patients present with severe manifestations of disease including fever, meningitis, encephalitis, or acute flaccid paralysis. Given the short window of detectable viremia and requirement for CSF sampling, most WNV infections never receive an official diagnosis. This study utilized enzyme-linked immunosorbent assay (ELISA) to detect WNV IgG antibodies in 250 patient serum and plasma samples collected at Tampa General Hospital during 2020 and 2021. Plaque reduction neutralization tests were used to confirm ELISA results. Out of the 250 patients included in this study, 18.8% of them were IgG positive, consistent with previous WNV exposure. There was no relationship between WNV exposure and age or sex. Full article