Search Results (1,509)

Heterojunction formation between BiVO₄ nanomaterials and benchmark semiconductor photocatalysts has been keenly pursued as a promising approach to improve charge transport and charge separation via interfacial electron transfer for the photoelectrocatalytic degradation of recalcitrant pharmaceutical pollutants. In this work, a heterostructured TiO₂/Mo-BiVO₄ bilayer photoanode was fabricated by the deposition of a mesoporous TiO₂ overlayer using the benchmark P25 titania catalyst on top of Mo-doped BiVO₄ inverse opal films as the supporting layer, which intrinsically absorbs visible light below 490 nm, while offering improved charge transport. A porous P25/Mo-BiVO₄ bilayer structure was produced from the densification of the inverse opal underlayer after post-thermal annealing, which was evaluated on photocurrent generation in aqueous electrolyte and the photoelectrocatalytic degradation of the refractory anti-inflammatory drug ibuprofen under back-side illumination by visible and UV–Vis light. Significantly enhanced photoelectrochemical performance on both photocurrent density and pharmaceutical degradation was achieved for the bilayer structure with respect to the additive effect of the constituent layers, which was related to the improved light harvesting arising from the backscattering by the mesoporous TiO₂ layer in combination with the favorable charge transfer at the TiO₂/Mo-BiVO₄ interface. Full article

Profiting from the surprising non-linear expressive capacity, deep convolutional neural networks have inspired lots of progress in low illumination (LI) remote sensing image enhancement. The key lies in sufficiently exploiting both the specific long-range (e.g., non-local similarity) and short-range (e.g., local continuity) structures distributed across different scales of each input LI image to build an appropriate deep mapping function from the LI images to their corresponding high-quality counterparts. However, most existing methods can only individually exploit the general long-range or short-range structures shared across most images at a single scale, thus limiting their generalization performance in challenging cases. We propose a multi-scale long–short range structure aggregation learning network for remote sensing imagery enhancement. It features flexible architecture for exploiting features at different scales of the input low illumination (LI) image, with branches including a short-range structure learning module and a long-range structure learning module. These modules extract and combine structural details from the input image at different scales and cast them into pixel-wise scale factors to enhance the image at a finer granularity. The network sufficiently leverages the specific long-range and short-range structures of the input LI image for superior enhancement performance, as demonstrated by extensive experiments on both synthetic and real datasets. Full article

This study used a direct dispersion and filtration technique to produce hybrid buckypaper (BP) composites of copper oxide nanoparticles (NPs) and entangled multiwalled carbon nanotubes (CNTs). The photocurrent generation of the BP sheets under two different (direct and gradient) illumination conditions was investigated by varying copper oxide loadings (10–50 wt%). The structure and morphology of the composites examined through X-ray diffraction and scanning electron microscopy (SEM) confirmed the presence of monoclinic cupric oxide nanoparticles in the CNT network. The difference in electrical resistivity between bulk-filled and surface-filled CuO-BP composites was assessed using the four-probe Hall measurement. The studies disclosed that the surface-loaded CuO on the CNT network demonstrated a superior ON and OFF response under the gradient illumination conditions with peak values of 17.69 μA and 350.04 μV for photocurrent and photovoltage, respectively. The significant photocurrent observed at zero applied voltage revealed the existence of a photovoltaic effect in the BP composites. An intense photoresponse was detected in the surface-filled sample CuO-BP composite in both illumination conditions. Additionally, at an illumination level of 150 W/m², wavelength-dependent photovoltaic effects on pure BP were observed using red, green, and blue filters. Full article

Safflower (Carthamus tinctorius L.), a versatile medicinal and economic crop, harbors untapped genetic resources essential for stress resilience and metabolic regulation. The TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR (TCP) transcription factors, exclusive to plants, are pivotal in orchestrating growth, development, and stress responses, yet their roles in safflower remain unexplored. Here, we report the comprehensive identification and characterization of 26 safflower TCP genes (CtTCPs), categorized into Class I (PROLIFERATING CELL FACTOR, PCF) and Class II (CINCINNATA and TEOSINTE BRANCHED1/CYCLOIDEA, CIN and CYC/TB1) subfamilies. Comparative phylogenetics, conserved motif, and gene structure analyses revealed a high degree of evolutionary conservation and functional divergence within the gene family. Promoter analyses uncovered light-, hormone-, and stress-responsive cis-elements, underscoring their regulatory potential. Functional insights from qRT-PCR analyses demonstrated dynamic CtTCP expression under abiotic stresses, including abscisic acid (ABA), Methyl Jasmonate (MeJA), Cold, and ultraviolet radiation b (UV-B) treatments. Notably, ABA stress triggered a significant increase in flavonoid accumulation, correlated with the upregulation of key flavonoid biosynthesis genes and select CtTCPs. These findings illuminate the complex regulatory networks underlying safflower’s abiotic stress responses and secondary metabolism, offering a molecular framework to enhance crop resilience and metabolic engineering for sustainable agriculture Full article

Routine least squares regression analyses may sometimes miss important aspects of data. To exemplify this point we analyse a set of 1171 observations from a questionnaire intended to illuminate the relationship between customer loyalty and perceptions of such factors as price and community outreach. Our analysis makes much use of graphics and data monitoring to provide a paradigmatic example of the use of modern robust statistical tools based on graphical interaction with data. We start with regression. We perform such an analysis and find significant regression on all factors. However, a variety of plots show that there are some unexplained features, which are not eliminated by response transformation. Accordingly, we turn to robust analyses, intended to give answers unaffected by the presence of data contamination. A robust analysis using a non-parametric model leads to the increased significance of transformations of the explanatory variables. These transformations provide improved insight into consumer behaviour. We provide suggestions for a structured approach to modern robust regression and give links to the software used for our data analyses. Full article

Carbon is predominantly used in zinc-ion hybrid capacitors (ZIHCs) as an electrode material. Nitrogen doping and strategic design can enhance its electrochemical properties. Melamine formaldehyde resin, serving as a hard carbon precursor, synthesizes nitrogen-doped porous carbon after annealing. Incorporating transition metal catalysts like Ni, Co, and Fe alters the morphology, pore structure, graphitization degree, and nitrogen doping types/proportions. Electrochemical tests reveal a superior capacitance of 159.5 F g⁻¹ at a scan rate of 1 mV s⁻¹ and rate performance in Fe-catalyzed N-doped porous carbon (Fe-NDPC). Advanced analysis shows Fe-NDPC’s high graphitic nitrogen content and graphitization degree, boosting its electric double-layer capacitance (EDLC) and pseudocapacitance. Its abundant micro- and mesopores increase the surface area fourfold compared to non-catalyzed samples, favoring EDLC and fast electrolyte transport. This study guides catalyst application in carbon materials for supercapacitors, illuminating how catalysts influence nitrogen-doped porous carbon structure and performance. Full article

Light microscopy has emerged as one of the fundamental methods to analyze biological systems; novel techniques of 3D microscopy and super-resolution microscopy (SRM) with an optical resolution down to the sub-nanometer range have recently been realized. However, most of these achievements have been made with fixed specimens, i.e., direct information about the dynamics of the biosystem studied was not possible. This stimulated the development of live cell microscopy imaging approaches, including Low Illumination Fluorescence Microscopy, Light Sheet (Fluorescence) Microscopy (LSFM), or Structured Illumination Microscopy (SIM). Here, we discuss perspectives, methods, and relevant light doses of advanced fluorescence microscopy imaging to keep the cells alive at low levels of phototoxicity. Full article

Alzheimer’s disease (AD) prevention is a critical challenge for aging societies, necessitating the exploration of food ingredients and whole foods as potential therapeutic agents. This study aimed to identify natural compounds (NCs) with therapeutic potential in AD using an innovative bioinformatics-integrated deep neural analysis approach, combining computational predictions with molecular docking and in vitro experiments for comprehensive evaluation. We employed the bioinformatics-integrated deep neural analysis of NCs for Disease Discovery (BioDeepNat) application in the data collected from chemical databases. Random forest regression models were utilized to predict the IC₅₀ (pIC₅₀) values of ligands interacting with AD-related target proteins, including acetylcholinesterase (AChE), amyloid precursor protein (APP), beta-secretase 1 (BACE1), microtubule-associated protein tau (MAPT), presenilin-1 (PSEN1), tumor necrosis factor (TNF)-α, and valosin-containing protein (VCP). Their activities were then validated through a molecular docking analysis using Autodock Vina. Predictions by the deep neural analysis identified 166 NCs with potential effects on AD across seven proteins, demonstrating outstanding recall performance. The top five food sources of these predicted compounds were black walnut, safflower, ginger, fig, corn, and pepper. Statistical clustering methodologies segregated the NCs into six well-defined groups, each characterized by convergent structural and chemical signatures. The systematic examination of structure–activity relationships uncovered differential molecular patterns among clusters, illuminating the sophisticated correlation between molecular properties and biological activity. Notably, NCs with high activity, such as astragalin, dihydromyricetin, and coumarin, and medium activity, such as luteolin, showed promising effects in improving cell survival and reducing lipid peroxidation and TNF-α expression levels in PC12 cells treated with lipopolysaccharide. In conclusion, our findings demonstrate the efficacy of combining bioinformatics with deep neural networks to expedite the discovery of previously unidentified food-derived active ingredients (NCs) for AD intervention. Full article

Vegetation indices, especially the normalized difference vegetation index (NDVI), are widely used in urban vegetation assessments. However, estimating the vegetation abundance in urban scenes using the NDVI has constraints due to the complex spectral signature related to the urban structure, materials and other factors compared to natural ground surfaces. This paper employs the 3D discrete anisotropic radiative transfer (DART) model to simulate the spectro-directional reflectance of synthetic urban scenes with various urban geometries and building materials using a flux-tracking method under shaded and sunlit conditions. The NDVI is calculated using the spectral radiance in the red (0.6545 μm) and near-infrared bands (0.865 μm). The effects of the urban material heterogeneity and 3D structure on the NDVI, and the performance of three NDVI-based fractional vegetation cover (FVC) inversion algorithms, are evaluated. The results show that the effects of the building material heterogeneity on the NDVI are negligible under sunlit conditions but not negligible under shaded conditions. The NDVI value of building components within synthetic scenes is approximately zero. The shaded road exhibits a higher NDVI value in comparison to the illuminated road because of scattering from adjacent pixels. In order to correct the effects of scattering caused by building geometry, the reflectance of the Landsat 8/OLI image is corrected using the sky view factor (SVF) and then used to calculate the FVC. Jilin-1 satellite images with high spatial resolution (0.5 m) are used to extract the vegetation cover and then aggregated to 30 m spatial resolution to calculate the FVC for validation. The results show that the RMSE is up to 0.050 after correction, while the RMSE is 0.169 before correction. This study makes a contribution to the understanding of the effects of the urban 3D structure and material reflectance on the NDVI and provides insights into the retrieval of the FVC in different urban scenes. Full article

Bow-tie diodes on the base of modulation-doped semiconductor structures are often used to detect radiation in GHz to THz frequency range. The operation of the bow-tie microwave diodes is based on carrier heating phenomena in an epitaxial semiconductor structure with broken geometrical symmetry. However, the electrical properties of bow-tie diodes are highly dependent on the purity of the grown epitaxial layer—specifically, the minimal number of defects—and the quality of the ohmic contacts. The quality of MBE-grown semiconductor structure depends on the presence of a buffer layer between a semiconductor substrate and an epitaxial layer. In this paper, we present an investigation of the electrical and optical properties of planar bow-tie microwave diodes fabricated using modulation-doped semiconductor structures grown via the MBE technique, incorporating either a GaAs buffer layer or a GaAs–AlGaAs super-lattice buffer between the semi-insulating substrate and the active epitaxial layer. These properties include voltage sensitivity, electrical resistance, I–V characteristic asymmetry, nonlinearity coefficient, and photoluminescence. The investigation revealed that the buffer layer, as well as the illumination with visible light, strongly influences the properties of the bow-tie diodes. Full article

Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering. Full article

Millions of electrostatically actuatable micromirror arrays have been arranged in between windowpanes in inert gas environments, enabling active daylighting in buildings for illumination and climatization. MEMS smart windows can reduce energy consumption significantly. However, to allow personalized light steering for arbitrary user positions with high flexibility, two main limitations must be overcome: first, limited tuning angle spans by MEMS pull-in effects; and second, the lack of a second orthogonal tuning angle, which is highly required. Firstly, design improvements of electrostatically actuatable micromirror arrays are reported by utilizing tailored bottom electrode structures for enlarging the tilt angle (Φ). Considerably larger tuning ranges are presented, significantly improving daylight steering into buildings. Secondly, 2D actuation means free movement of micromirrors via two angles—tilt (Φ) and torsion angle (θ)—while applying two corresponding voltages between the metallic micromirrors and corresponding FTO (fluorine-doped tin oxide) counters bottom electrode pads. In addition, a solution for a notorious problem in MEMS actuation is presented. Micromirror design modifications are necessary to eliminate possible crack formation on metallic structure due to stress concentration during the free movement of 2D actuatable micromirror arrays. The concept, design of micromirror arrays and bottom electrodes, as well as technological fabrication and experimental results are presented and discussed. Full article

Background: This study presents a comprehensive investigation into the correlation between Rare Diseases and Syndromes (RDS) and the dysphagic disorders manifested during childhood and adulthood in affected patients. Dysphagia is characterized by difficulty or an inability to swallow food of any consistency, as well as saliva or medications, from the oral cavity to the stomach. RDS often present with complex and heterogeneous clinical manifestations, making it challenging to develop standardized diagnostic and therapeutic approaches. Dysphagia can arise from various etiologies, including those related to the central nervous system, inflammatory and neoplastic processes, anatomical or structural disorders, and neuromuscular conditions. These diverse etiologies can result in both structural and functional deficits or neurological impairments that compromise swallowing function. While RDS frequently leads to uncommon conditions, dysphagia remains an underrecognized complication. Objectives: The primary objective of this review is to illuminate the latest knowledge concerning the management of dysphagia in both pediatric and adult populations within the context of RDS, with a particular focus on current therapeutic approaches. To achieve this, the study provides a comprehensive analysis of existing strategies for managing dysphagia in RDS, highlighting recent advancements in therapy while identifying critical gaps in clinical knowledge and practice. By synthesizing available evidence, the review aims to deepen understanding of the unique challenges associated with dysphagia in these conditions and explore innovative interventions to enhance patient care and outcomes. Results: The integration of innovative therapeutic techniques into the speech-language pathology treatment of dysphagia augments traditional strategies, offering updated knowledge that can be applied to prognosis and therapeutic interventions across various ages and racial groups. This review also provides an overview of symptomatology, assessment techniques, and the specific characteristics of dysphagia associated with various genetic and acquired RDS. Full article

The dysregulation of phosphatidylinositol 3-kinase (PI3K) signaling plays a pivotal role in driving neoplastic transformation by promoting uncontrolled cell survival and proliferation. This oncogenic activity is primarily caused by mutations that are frequently found in PI3K genes and constitutively activate the PI3K signaling pathway. However, tumorigenesis can also arise from nonmutated PI3K proteins adopting unique active conformations, further complicating the understanding of PI3K-driven cancers. Recent structural studies have illuminated the functional divergence among highly homologous PI3K proteins, revealing how subtle structural alterations significantly impact their activity and contribute to tumorigenesis. In this review, we summarize current knowledge of Class I PI3K proteins and aim to unravel the complex mechanism underlying their oncogenic traits. These insights will not only enhance our understanding of PI3K-mediated oncogenesis but also pave the way for the design of novel PI3K-based therapies to combat cancers driven by this signaling pathway. Full article

Semiconductor polymeric graphitic carbon nitride (g-C₃N₄) photocatalysts have garnered significant and rapidly increasing interest in the realm of visible light-driven hydrogen evolution reactions. This interest stems from their straightforward synthesis, ease of functionalization, appealing electronic band structure, high physicochemical and thermal stability, and robust photocatalytic activity. This review starts with the basic principle of photocatalysis and the development history, synthetic strategy, and structural properties of g-C₃N₄ materials, followed by the rational design and engineering of g-C₃N₄ from the perspectives of nano-morphological control and electronic band tailoring. Some representative results, including experimental and theoretical calculations, are listed to show the advantages of optimizing the above two characteristics for performance improvement in photocatalytic hydrogen evolution from water splitting. The existing opportunities and challenges of g-C₃N₄ photocatalysts are outlined to illuminate the developmental trajectory of this field. This paper provides guidance for the preparation of g-C₃N₄ and to better understand the current state of the art for future research directions. Full article