Browse Articles

Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease

Abstract The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge. This paper emphasis on pandemic simulation models by introduced respiratory-specific transmission to extend and complement the classical Susceptible-Exposed-(Asymptomatic)-Infected-Recovered SE(A)IR model to assess the significance of the COVID-19 transmission control features to provide an explanation of the rationale for the government policy. A novel epidemiological model is developed using mean-field theory. Utilizing the SE(A)IR extended framework, which is a suitable method for describing the progression of epidemics over actual or genuine landscapes, we have developed a novel model named SEIAPUFR. This model effectively detects the connections between various stages of infection. Subsequently, we formulated eight ordinary differential equations that precisely depict the population’s temporal development inside each segment. Furthermore, we calibrated the transmission and clearance rates by considering the impact of various control strategies on the epidemiological dynamics, which we used to project the future course of COVID-19. Based on these parameter values, our emphasis was on determining the criteria for stabilizing the disease-free equilibrium (DEF). We also developed model parameters that are appropriate for COVID-19 outbreaks, taking into account varied population sizes. Ultimately, we conducted simulations and predictions for other prominent cities in China, such as Wuhan, Shanghai, Guangzhou, and Shenzhen, that have recently been affected by the COVID-19 outbreak. By integrating different control measures, respiratory-specific modeling, and disease supervision sampling into an expanded SEI (A) R epidemic model, we found that supervision sampling can improve early warning of viral activity levels and superspreading events, and explained the significance of containments in controlling COVID-19 transmission and the rationality of policy by the influence of different containment measures on the transmission rate. These results indicate that the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission, and the proportion of supervision sampling should be proportional to the transmission rate, especially only aimed at preventing a resurgence of SARS-CoV-2 transmission in low-prevalence areas. Furthermore, The incidence hazard of Males and Females was 1.39(1.23–1.58), and 1.43(1.26–1.63), respectively. Our investigation found that the ratio of peak sampling is directly related to the transmission rate, and both decrease when control measures are implemented. Consequently, the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission. Reasonable and effective interventions during the early stage can flatten the transmission curve, which will slow the momentum of the outbreak to reduce medical pressure.

Stress landscape of folding brain serves as a map for axonal pathfinding

Abstract Understanding the mechanics linking cortical folding and brain connectivity is crucial for both healthy and abnormal brain development. Despite the importance of this relationship, existing models fail to explain how growing axon bundles navigate the stress field within a folding brain or how this bidirectional and dynamic interaction shapes the resulting surface morphologies and connectivity patterns. Here, we propose the concept of “axon reorientation” and formulate a mechanical model to uncover the dynamic multiscale mechanics of the linkages between cortical folding and connectivity development. Simulations incorporating axon bundle reorientation and stress-induced growth reveal potential mechanical mechanisms that lead to higher axon bundle density in gyri (ridges) compared to sulci (valleys). In particular, the connectivity patterning resulting from cortical folding exhibits a strong dependence on the growth rate and mechanical properties of the navigating axon bundles. Model predictions are supported by in vivo diffusion tensor imaging of the human brain.

Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images

Abstract The most common carcinoma-related cause of death among women is breast cancer. Early detection is crucial, and the manual screening method may lead to a delayed diagnosis, which would delay treatment and put lives at risk. Mammography imaging is advised for routine screening to diagnose breast cancer at an early stage. To improve generalizability, this study examines the implementation of Federated Learning (FedL) to detect breast cancer. Its performance is compared to a centralized training technique that diagnoses breast cancer. Although FedL has been famous as a safeguarding privacy algorithm, its similarities to ensemble learning methods, such as federated averaging (FEDAvrg), still need to be thoroughly investigated. This study examines explicitly how a YOLOv6 model trained with FedL performs across several clients. A new homomorphic encryption and decryption algorithm is also proposed to retain data privacy. A novel pruned YOLOv6 model with FedL is introduced in this study to differentiate benign and malignant tissues. The model is trained on the breast cancer pathological dataset BreakHis and BUSI. The proposed model achieved a validation accuracy of 98% on BreakHis dataset and 97% on BUSI dataset. The results are compared with the VGG-19, ResNet-50, and InceptionV3 algorithms, showing that the proposed model achieved better results. The tests reveal that federated learning is feasible, as FedAvrg trains models of outstanding quality with only a few communication rounds, as shown by the results on a range of model topologies such as ResNet50, VGG-19, InceptionV3, and the proposed Ensembled FedL YOLOv6.

Asteroid Bennu contains salts from ancient brine

Stroma-derived Dickkopf-1 contributes to the suppression of NK cell cytotoxicity in breast cancer

Abstract Mechanisms related to tumor evasion from NK cell-mediated immune surveillance remain enigmatic. Dickkopf-1 (DKK1) is a Wnt/β-catenin inhibitor, whose levels correlate with breast cancer progression. We find DKK1 to be expressed by tumor cells and cancer-associated fibroblasts (CAFs) in patient samples and orthotopic breast tumors, and in bone. By using genetic approaches, we find that bone-derived DKK1 contributes to the systemic DKK1 elevation in tumor-bearing female mice, while CAFs contribute to DKK1 at primary tumor site. Systemic and bone-specific DKK1 targeting reduce tumor growth. Intriguingly, deletion of CAF-derived DKK1 also limits breast cancer progression, without affecting its levels in circulation, and regardless of DKK1 expression in the tumor cells. While not directly supporting tumor proliferation, stromal-DKK1 suppresses NK cell activation and cytotoxicity by downregulating AKT/ERK/S6 phosphorylation. Importantly, increased DKK1 levels and reduced cytotoxic NK cells are detected in women with progressive breast cancer. Our findings indicate that DKK1 represents a barrier to anti-tumor immunity through suppression of NK cells.

Investigation and evaluation of palygorskite microstructure following acid pretreatment and its potential use as an adsorbent for copper

Rising infrastructure inequalities accompany urbanization and economic development

Abstract Impending global urban population growth is expected to occur with considerable infrastructure expansion. However, our understanding of attendant infrastructure inequalities is limited, highlighting a critical knowledge gap in the sustainable development implications of urbanization. Using satellite data from 2000 to 2019, we examine country-level population-adjusted biases in infrastructure distribution within and between regions of varying urbanization levels and derive four key findings. First, we find long-run positive associations between infrastructure inequalities and both urbanization and economic development. Second, our estimates highlight increasing infrastructure inequalities across most of the countries examined. Third, we find greater future infrastructure inequality increases in the global south, where inequalities will rise more in countries with substantial urban primacy. Fourth, we find that infrastructure inequality may evolve differently than economic inequalities. Overall, advancing sustainable development vis-à-vis urbanization and economic development will require intentional infrastructure planning for spatial equity.

A comparative analysis of collagen based dressings and polysaccharide microspheres for hemostasis management in hepatic stab wounds

Highly sensitive, responsive, and selective iodine gas sensor fabricated using AgI-functionalized graphene

Flaky sputtered silicon MWCNTs core-shell structure as a freestanding binder-free electrode for lithium-ion battery

Room temperature ferroelectricity in monolayer graphene sandwiched between hexagonal boron nitride

RETRACTED ARTICLE: Gastrointestinal helminth of Nile crocodiles (Crocodylus niloticus), in Arba Minch Crocodile Ranch, Ethiopia

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.