Browse Articles

Superior energy storage capacity of polymer-based bilayer composites by introducing 2D ferroelectric micro-sheets

Azurin a potent anticancer and antimicrobial agent isolated from a novel Pseudomonas aeruginosa strain

Abstract Azurin, a bacterial blue-copper protein, has garnered significant attention as a potential anticancer drug in recent years. Among twenty Pseudomonas aeruginosa isolates, we identified one isolate that demonstrated potent and remarkable azurin synthesis using the VITEK 2 system and 16S rRNA sequencing. The presence of the azurin gene was confirmed in the genomic DNA using specific oligonucleotide primers, and azurin expression was also detected in the synthesized cDNA, which revealed that the azurin expression is active. Furthermore, crude azurin protein was extracted, precipitated using 70% ammonium sulfate, dialyzed, and subjected to purification using carboxymethyl-Sephadex in affinity chromatography as a cheap method for purification. The partially purified azurin protein was characterized using polyacrylamide gel electrophoresis, energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, and nuclear magnetic resonance spectroscopy. Notably, qualitative elemental analysis by EDX showed the presence of copper and sulfur, corresponding to the copper-core and disulfide-bridge, respectively, in the purified azurin fraction. Moreover, FTIR spectroscopy revealed characteristic amide I and II absorption peaks (1500–1700 cm− 1), revealing the possible secondary structure of azurin. The results of NMR revealed the presence of characteristic amino acids such as methionine and cysteine, which confirmed the EDX results for sulfur-containing amino acids. Purified azurin exhibited antimicrobial activity against Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Klebsiella pneumoniae. Additionally, its anticancer properties were determined using the MTT assay and cell cycle analysis, revealing a preference for inhibiting the MCF7 breast cancer cell line where breast cancer is most common in Egypt. Overall, the research findings suggest that the local isolate, P. aeruginosa strain 105, could be a potential source of azurin protein for incorporation into cancer treatment strategies.

Monolithic electrostatic actuators with independent stiffness modulation

Abstract Robotic artificial muscles, inspired by the adaptability of biological muscles, outperform rigid robots in dynamic environments due to their flexibility. However, the intrinsic compliance of the soft actuators restricts force transmission capacity and dynamic response. Biological muscle modulates their stiffness and damping, varying viscoelastic properties and force in interaction with the surroundings. Here we replicate this function in the electro-stiffened ribbon actuator, a monolithic strong actuator capable of high contraction and stiffness modulation. electro-stiffened ribbon actuator employs dielectric-liquid-amplified electrostatic forces for contraction, and electrorheological fluid for rapid (<10 ms) stiffness and damping adjustments. This seamless integration enables contractile force modulation, extending its capability as a lightweight variable resistance passive spring by over 2.5 times, and improves its dynamic responses, with faster contractions and rapid attenuation of oscillations by more than 50%. We demonstrate electro-stiffened ribbon actuator’s versatility in active, passive and dual connection functions, including arm-bending wearable robotics, robotic arms with variable impact resistance and muscle-like stiffness and damping modulation.

Biofabrication of zinc oxide nanoparticles using Moringa oleifera, characterization and statistical optimization for their application in crystal violet adsorption

Condensate droplet roaming on nanostructured superhydrophobic surfaces

Abstract Jumping of coalescing condensate droplets from superhydrophobic surfaces is an interesting phenomenon which yields marked heat transfer enhancement over the more explored gravity-driven droplet removal mode in surface condensation, a phase change process of central interest to applications ranging from energy to water harvesting. However, when condensate microdroplets coalesce, they can also spontaneously propel themselves omnidirectionally on the surface independent of gravity and grow by feeding from droplets they sweep along the way. Here we observe and explain the physics behind this phenomenon of roaming of coalescing condensate microdroplets on solely nanostructured superhydrophobic surfaces, where the microdroplets are orders of magnitude larger than the underlaying surface nanotexture. We quantify and show that it is the inherent asymmetries in droplet adhesion during condensation, arising from the stochastic nature of nucleation within the nanostructures, that generates the tangential momentum driving the roaming motion. Subsequent dewetting during this conversion initiates a vivid roaming and successive coalescence process, preventing condensate flooding of the surface, and enhancing surface renewal. Finally, we show that the more efficient conversion process of roaming from excess surface energy to kinetic energy results in significantly improved heat transfer efficiency over condensate droplet jumping, the mechanism currently understood as maximum.

Cross-talk-free, high extinction ratio, and ultra-compact all‑optical 4 × 2 encoder using graphene-based plasmonic waveguides

Abstract This paper presents an all-optical 4 × 2 encoder based on graphene-plasmonic waveguides for operation in the wavelength range of 8–12 μm. The basic plasmonic waveguide consists of a silicon (Si) strip and a graphene sheet supported by two dielectric ridges. Surface plasmon polaritons (SPPs) are stimulated in the spatial gap between the graphene sheet and the Si strip. The effect of geometric parameters and chemical potential of the graphene sheet changes on the suggested waveguide’s waveguiding behavior is meticulously investigated using the three-dimensional finite-difference time-domain (3D-FDTD) method. The encoder comprises a straight waveguide to detect the state of the In0 input and two Y-combiners with outputs Out0 and Out1 to detect the state of the In1, In2, and In3 inputs. The encoder exhibits a minimum extinction ratio (ER min ) of 19 dB at a wavelength of 10 μm. In addition, the cross-talk (CT) and insertion loss (IL) values are −21.3 and −1.31 dB, respectively. The encoder offers an ultra-compact structure with a total footprint of 4.25 μm2. Due to its exceptional waveguiding features, low CT and IL values, and high ER min , the proposed encoder holds promise for various communication and signal processing applications.

Phase separation of a microtubule plus-end tracking protein into a fluid fractal network

Abstract Microtubule plus-end tracking proteins (+TIPs) participate in nearly all microtubule-based cellular processes and have recently been proposed to function as liquid condensates. However, their formation and internal organization remain poorly understood. Here, we have study the phase separation of Bik1, a CLIP-170 family member and key +TIP involved in budding yeast cell division. Bik1 is a dimer with a rod-shaped conformation primarily defined by its central coiled-coil domain. Its liquid condensation likely involves the formation of higher-order oligomers that phase separate in a manner dependent on the protein’s N-terminal CAP-Gly domain and C-terminal EEY/F-like motif. This process is accompanied by conformational rearrangements in Bik1, leading to at least a two-fold increase in multivalent interactions between its folded and disordered domains. Unlike classical liquids, Bik1 condensates exhibit a heterogeneous, fractal supramolecular structure with protein- and solvent-rich regions. This structural evidence supports recent percolation-based models of biomolecular condensates. Together, our findings offer insights into the structure, dynamic rearrangement, and organization of a complex, oligomeric, and multidomain protein in both dilute and condensed states. Our experimental framework can be applied to other biomolecular condensates, including more complex +TIP networks.

An applied noise model for scintillation-based CCD detectors in transmission electron microscopy

Abstract Measurements in general are limited in accuracy by the presence of noise. This also holds true for highly sophisticated scintillation-based CCD cameras, as they are used in medical applications, astronomy or transmission electron microscopy. Further, signals measured with pixelated detectors are convolved with the inherent detector point spread function. The Poisson noise, arising from the quantized nature of the beam electrons, gets correlated by this convolution, which allows to reconstruct the detector PSF based on the Wiener–Khinchin theorem and the Pearson correlation coefficients under homogeneous illumination conditions. However, correlation also has a strong impact on the noise statistics of basic operations like the binning of signals, as it is usually done in electron energy-loss spectroscopy. Thus, this paper aims to give an insight into the different noise contributions occurring on such detectors, into their underlying statistics and their correlation. Detectors usually suffer from gain non-linearities and quantum efficiency deviations, which must be corrected for optimal results. All these operations influence the noise and are influenced by it, vice versa. In this work, we mathematically describe all these changes and show them experimentally. Methods on how to measure individual noise and correlation parameters are described allowing readers to implement routines for finding them. Sufficient knowledge on the noise of a measurement is not only crucial for classifying its quality and meaningfulness, but also allows for better post-processing operations like deconvolution, which is a common practice in spectroscopy to enhance signals.

TOP2A inhibition and its cellular effects related to cell cycle checkpoint adaptation pathway

Abstract In this study, we investigate the G2 checkpoint activated by chromosome entanglements, the so-called Decatenation Checkpoint (DC), which can be activated by TOP2A catalytic inhibition. Specifically, we focus on the spontaneous ability of cells to bypass or override this checkpoint, referred to as checkpoint adaptation. Some factors involved in adapting to this checkpoint are p53 and MCPH1. Using cellular models depleted of p53 or both p53 and MCPH1 in hTERT-RPE1 cells, we analyzed cell cycle dynamics and adaptation, segregation defects, apoptosis rate, and transcriptional changes related to prolonged exposure to TOP2A inhibitors. Our findings reveal that cell cycle dynamics are altered in MCPH1-depleted cells compared to control cells. We found that MCPH1 depletion can restore the robustness of the DC in a p53-negative background. Furthermore, this research highlights the differential effects of TOP2A poisons and catalytic inhibitors on cellular outcomes and transcriptional profiles. By examining the different mechanisms of TOP2A inhibition and their impact on cellular processes, this study contributes to a deeper understanding of the regulation and physiological implications of the DC and checkpoint adaptation in non-carcinogenic cell lines.

Machine learning models for water safety enhancement

Domperidone inhibits dengue virus infection by targeting the viral envelope protein and nonstructural protein 1

Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons

Abstract The detection of exons is an important area of research in genomic sequence analysis. Many signal-processing methods have been established successfully for detecting the exons based on their periodicity property. However, some improvement is still required to increase the identification accuracy of exons. So, an efficient computational model is needed. Therefore, for the first time, we are introducing an optimized convolutional neural network (optCNN) for classifying the exons and introns. The study aims to identify the best CNN model that provides improved accuracy for the classification of exons by utilizing the optimization algorithm. In this case, an African Vulture Optimization Algorithm (AVOA) is used for optimizing the layered architecture of the CNN model along with its hyperparameters. The CNN model generated with AVOA yielded a success rate of 97.95% for the GENSCAN training set and 95.39% for the HMR195 dataset. The proposed approach is compared with the state-of-the-art methods using AUC, F1-score, Recall, and Precision. The results reveal that the proposed model is reliable and denotes an inventive method due to the ability to automatically create the CNN model for the classification of exons and introns.

Variable power functional dilution adjustment of spot urine

3D scanner’s potential as a novel tool for lymphedema measurement in mouse hindlimb models

An evaporite sequence from ancient brine recorded in Bennu samples

Abstract Evaporation or freezing of water-rich fluids with dilute concentrations of dissolved salts can produce brines, as observed in closed basins on Earth 1 and detected by remote sensing on icy bodies in the outer Solar System 2,3 . The mineralogical evolution of these brines is well understood in regard to terrestrial environments 4 , but poorly constrained for extraterrestrial systems owing to a lack of direct sampling. Here we report the occurrence of salt minerals in samples of the asteroid (101955) Bennu returned by the OSIRIS-REx mission 5 . These include sodium-bearing phosphates and sodium-rich carbonates, sulfates, chlorides and fluorides formed during evaporation of a late-stage brine that existed early in the history of Bennu’s parent body. Discovery of diverse salts would not be possible without mission sample return and careful curation and storage, because these decompose with prolonged exposure to Earth’s atmosphere. Similar brines probably still occur in the interior of icy bodies Ceres and Enceladus, as indicated by spectra or measurement of sodium carbonate on the surface or in plumes 2,3 .

Risk factors associated with higher WHO grade in meningiomas: a multicentric study of 552 skull base meningiomas

Author Correction: A simple model for Behavioral Time Scale Synaptic Plasticity (BTSP) provides content addressable memory with binary synapses and one-shot learning

Characterization of language abilities and semantic networks in very preterm children at school-age

It has been widely assessed that very preterm children (<32 weeks gestational age) present language and memory impairments compared with full-term children. However, differences in their underlying semantic memory structure have not been studied yet. Nevertheless, the way concepts are learned and organized across development relates to children’s capacities in retrieving and using information later. Therefore, the semantic memory organization could underlie several cognitive deficits existing in very preterm children. Computational mathematical models offer the possibility to characterize semantic networks through three coefficients calculated on spoken language: average shortest path length (i.e., distance between concepts), clustering (i.e., local interconnectivity), and modularity (i.e., compartmentalization into small sub-networks). Here we assessed these coefficients in 38 very preterm schoolchildren (aged 8–10 years) compared with 38 full-term schoolchildren (aged 7–10 years) based on a verbal fluency task. Using semantic network analysis, very preterm children showed a longer distance between concepts and a lower interconnectivity at a local level than full-term children. In addition, we found a trend for a higher modularity at a global in very preterm children compared with full-term children. These findings provide preliminary evidence that very preterm children demonstrate subtle impairments in the organization of their semantic network, encouraging the adaptation of the support and education they receive.

Transforming CCTV cameras into NO2 sensors at city scale for adaptive policymaking

Abstract Air pollution in cities, especially NO2, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities. Here, we demonstrate how city CCTV cameras can act as a pseudo-NO2 sensors. Using a predictive graph deep model, we utilised traffic flow from London’s cameras in addition to environmental and spatial factors, generating NO2 predictions from over 133 million frames. Our analysis of London’s mobility patterns unveiled critical spatiotemporal connections, showing how specific traffic patterns affect NO2 levels, sometimes with temporal lags of up to 6 h. For instance, if trucks only drive at night, their effects on NO2 levels are most likely to be seen in the morning when people commute. These findings cast doubt on the efficacy of some of the urban policies currently being implemented to reduce pollution. By leveraging existing camera infrastructure and our introduced methods, city planners and policymakers could cost-effectively monitor and mitigate the impact of NO2 and other pollutants.

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