Lunar impact crater identification and age estimation with Chang'E data by deep and transfer learning

Pseudo-Spectral Spatial Feature Extraction and Enhanced Fusion Image for Efficient Meter-Sized Lunar Impact Crater Automatic Detection in Digital Orthophoto Map

Impact craters are crucial for our understanding of planetary resources, geological ages, and the history of evolution. We designed a novel pseudo-spectral spatial feature extraction and enhanced fusion (PSEF) method with the YOLO network to address the problems encountered during the detection of the numerous and densely distributed meter-sized impact craters on the lunar surface. The illumination incidence edge features, isotropic edge features, and eigen frequency features are extracted by Sobel filtering, LoG filtering, and frequency domain bandpass filtering, respectively. Then, the PSEF images are created by pseudo-spectral spatial techniques to preserve additional details from the original DOM data. Moreover, we conducted experiments using the DES method to optimize the post-processing parameters of the models, thereby determining the parameter ranges for practical deployment. Compared with the Basal model, the PSEF model exhibited superior performance, as indicated by multiple measurement metrics, including the precision, recall, F1-score, mAP, and robustness, etc. Additionally, a statistical analysis of the error metrics of the predicted bounding boxes shows that the PSEF model performance is excellent in predicting the size, shape, and location of impact craters. These advancements offer a more accurate and consistent method to detect the meter-sized craters on planetary surfaces, providing crucial support for the exploration and study of celestial bodies in our solar system.

Automated Lunar Crater Identification with Chandrayaan-2 TMC-2 Images using Deep Convolutional Neural Networks

Terrestrial planets and their moons have impact craters, contributing significantly to the complex geomorphology of planetary bodies in our Solar System. Traditional crater identification methods struggle with accuracy because of the diverse forms, locations, and sizes of the craters. Our main aim is to locate lunar craters using images from Terrain Mapping Camera-2 (TMC-2) onboard the Chandrayaan-II satellite. The crater-based U-Net model, a convolutional neural network frequently used in image segmentation tasks, is a deep learning method presented in this study. The task of crater detection was accomplished with the proposed model in two steps: initially, it was trained using Resnet18 as the backbone and U-Net based on Image Net as weights. Secondly, TMC-2 images from Chandrayaan-2 were used to detect craters based on the trained model. The model proposed in this study comprises a neural network, feature extractor, and optimization technique for lunar crater detection. The model achieves 80.95% accuracy using unannotated data and precision and recall are much better with annotated data with an accuracy of 86.91% in object detection with TMC-2 ortho images. 2000 images have been considered for the present work as manual annotation is a time-consuming process and the inclusion of more images can enhance the performance score of the model proposed.

A Review of Lunar Communications and Antennas: Assessing Performance in the Context of Propagation and Radiation

Over the previous two decades, a notable array of space exploration missions have been initiated with the primary aim of facilitating the return of both humans and robots from Earth to the moon. The significance of these endeavors cannot be emphasized enough as numerous entities, both public and private, from across the globe have invested substantial resources into this pursuit. Researchers have committed their efforts to addressing the challenges linked to lunar communication. Even with all of these efforts, only a few of the many suggested designs for communication and antennas on the moon have been evaluated and compared. These designs have also not been shared with the scientific community. To bridge this gap in the existing body of knowledge, this paper conducts a thorough review of lunar surface communication and the diverse antenna designs employed in lunar communication systems. This paper provides a summary of the findings presented in lunar surface communication research while also outlining the assorted challenges that impact lunar communication. Apart from various antenna designs reported in this field, based on their intended usage, two additional classifications are introduced: (a) mission-based antennas-utilized in actual lunar missions-and (b) research-based antennas-employed solely for research purposes. Given the critical need to comprehend and predict lunar conditions and antenna behaviors within those conditions, this review holds immense significance. Its relevance is particularly pronounced in light of the numerous upcoming lunar missions that have been announced.

Intermittent fasting supports the balance of the gut microbiota composition

There is a growing body of detailed research demonstrating that intermittent fasting is essentially a cleansing activity in terms of health. Especially since its applications that exceed 16 h trigger autophagy, it continues its effect on all tissue and organ systems after the regeneration movement that starts at the cellular level. Similarly, it continues to be better understood with each passing day that the gut microbiota (GM) has many positive effects on all tissue and organ systems. Although the GM is affected by many different parameters, dietary habits are reported to be the most effective factor. Therefore, it is important to investigate the effects of different preferred fasting practices on the GM, which has numerous health benefits. Pointing out this situation, this study aims to determine the effects of 18-h intermittent fasting for 5 weeks on the shaping of GM. A 12-month-old male Wistar rat was chosen as the model organism in the study. At the end of the application, the metagenome was applied to the cecum content of the intestinal tissue collected from the sacrificed animals. Intermittent fasting practice led to an increase in alpha diversity, which expresses a significant bacterial diversity, the stabilization of Firmicutes and Bacteroidetes ratios (F/B), and the reshaping of the values with the highest prevalence in all stages of the classification, especially in the family, genus, and species care. Analysis results showed that the preferred intermittent fasting program helps balance the GM composition. This study is an important example showing the strong positive link between intermittent fasting and GM.

Cyclophilin A-mediated mitigation of coronavirus SARS-CoV-2

Human cyclophilin A (hCypA) is important for the replication of multiple coronaviruses (CoVs), and cyclosporine A inhibitors can suppress CoVs. The emergence of rapidly spreading severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has sparked concerns that mutations affect the binding ability of the spike (S) protein to the angiotensin-converting enzyme 2 (ACE2) cell receptor, affecting the severity of coronavirus disease (COVID-19). Far-western blotting and surface plasmon resonance (SPR) results revealed that hCypA interacts strongly with the viral SARS-CoV-2 receptor-binding domain (RBD), with a binding affinity of 6.85 × 10-8 M. The molecular interaction between hCypA and the viral protein interface was shown using three-dimensional structural analysis, which revealed the blocking of key residues on the RBD interface by hCypA. The RBD facilitates binding to the ACE2 receptor. The hCypA-S protein complex suppressed the binding of RBD to the ACE2 receptor, which a required event for CoV entry into the host cell. The reliability of this postulated blocking mechanism of the hCypA-SARS-CoV2 RBD complex with ACE was confirmed by SPR and molecular interaction lateral flow (MILF) strip assay, which offers the immunochromatographic signal read-outs. The emergence of new SARS-CoV-2 variants with key mutations in RBD had a negligible effect on the binding of the RBD variants to hCypA, indicating an effective mitigation strategy for SARS-CoV-2 variants. The MILF strip assay results also highlight the neutralizing effect of hCypA by effectively blocking RBD (wild type and its variants) from binding ACE2. Given the importance of hCypA in viral entry regulation, it has the potential to be used as a target for antiviral therapy.

A Knowledge Transfer Framework for General Alloy Materials Properties Prediction

Biomedical metal implants have many applications in clinical treatment. Due to a variety of application requirements, alloy materials with specific properties are being designed continuously. The traditional alloy properties testing experiment is faced with high-cost and time-consuming challenges. Machine learning can accurately predict the properties of materials at a lower cost. However, the predicted performance is limited by the material dataset. We propose a calculation framework of alloy properties based on knowledge transfer. The purpose of the framework is to improve the prediction performance of machine learning models on material datasets. In addition to assembling the experiment dataset, the simulation dataset is also generated manually in the proposed framework. Domain knowledge is extracted from the simulation data and transferred to help train experiment data by the framework. The high accuracy of the simulation data (above 0.9) shows that the framework can effectively extract domain knowledge. With domain knowledge, the prediction performance of experimental data can reach more than 0.8. And it is 10% higher than the traditional machine learning method. The explanatory ability of the model is enhanced with the help of domain knowledge. In addition, five tasks are applied to show the framework is a general method.

Population of Degrading Small Impact Craters in the Chang’E-4 Landing Area Using Descent and Ground Images

The landing camera (LCAM) of Chang’e-4 lander provides a series of low (46 cm/pixel) to high (2.3 cm/pixel) resolution images, which are suitable for centimeter-scale craters. In this paper, we analyze the degradation of those small-sized craters to provide detailed information on the local geological evolution of the lunar surface. From the mosaicked descent image, 6316 craters were extracted and classified into four degradation levels based on their morphology on the image: fresh, slightly degraded, moderately degraded, and severely degraded. The ground terrain camera (TCAM) image and the DEM of the Yutu-2 panoramic camera (PCAM) validate the crater degradation levels from a qualitative and quantitative perspective, respectively. The results show that the smaller the size of the craters, the more easily they are degraded. The crater populations in equilibrium in the four study areas indicate that the cumulative size–frequency distribution (SFD) slope is different from previous research results, and the smaller the craters, the more difficult to reach an equilibrium state (for craters smaller than a given size, the production rate is exactly balanced by the removal rate), which may be due to secondary cratering and surface resurfacing caused by the burial of ejecta from neighboring craters.

Insulin signaling shapes fractal scaling of C. elegans behavior

Fractal scaling in animal behavioral activity, where similar temporal patterns appear repeatedly over a series of magnifications among time scales, governs the complex behavior of various animal species and, in humans, can be altered by neurodegenerative diseases and aging. However, the mechanism underlying fractal scaling remains unknown. Here, we cultured C. elegans in a microfluidic device for 3 days and analyzed temporal patterns of C. elegans activity by fractal analyses. The residence-time distribution of C. elegans behaviors shared a common feature with those of human and mice. Specifically, the residence-time power-law distribution of the active state changed to an exponential-like decline at a longer time scale, whereas the inactive state followed a power-law distribution. An exponential-like decline appeared with nutrient supply in wild-type animals, whereas this decline disappeared in insulin-signaling-defective daf-2 and daf-16 mutants. The absolute value of the power-law exponent of the inactive state distribution increased with nutrient supply in wild-type animals, whereas the value decreased in daf-2 and daf-16 mutants. We conclude that insulin signaling differentially affects mechanisms that determine the residence time in active and inactive states in C. elegans behavior. In humans, diabetes mellitus, which is caused by defects in insulin signaling, is associated with mood disorders that affect daily behavioral activities. We hypothesize that comorbid behavioral defects in patients with diabetes may be attributed to altered fractal scaling of human behavior.

The Identification of Impact Craters from GRAIL-Acquired Gravity Data by U-Net Architecture

The identification of impact craters on the Moon and other planetary bodies is of great significance to studying and constraining the dynamical process and evolution of the Solar System. Traditionally, this has been performed through the visual examination of images. Due to the effect of overburden, some structural features cannot be effectively identified from optical images, resulting in limitations in the scope, efficiency and accuracy of identification. In this paper, we investigate the viability of convolutional neural networks (CNNs) to perform the detection of impact craters from GRAIL-acquired gravity data. The ideal values of each hyperparameter in U-net architecture are determined after dozens of iterations of model training, testing and evaluation. The final model was evaluated by the Loss function with the low value of 0.04, indicating that the predicted output of the model reached a relatively high fitting degree with the prior labelled output. The comparative results with different methods show that the proposed method has a clear detection of the target features, with an accuracy of more than 80%. In addition, the detection results of the whole image account for 83% of the number of manually delineated gravity anomalies. The proposed method can still achieve the same quality for the identification of the gravity anomalies caused by impact craters under the condition that the resolution of GRAIL gravity data are not superior. Our results demonstrate that the U-net architecture can be a very effective tool for the rapid and automatic identification of impact craters from gravity map on the Moon, as well as other Solar System bodies.

Review for Examining the Oxidation Process of the Moon Using Generative Adversarial Networks: Focusing on Landscape of Moon

Japan Aerospace Exploration Agency (JAXA) has collected and studied the data observed by the lunar probe, SELenological and ENgineering Explorer (SELENE), from 2007 to 2017. JAXA discovered that the oxygen of the upper atmosphere of the Earth is transported to the moon by the tail of the magnetic field. However, this research is still in progress, and more data are needed to clarify the oxidation process. Therefore, this paper supplements the insufficient observation data by using Generative Adversarial Networks (GAN) and proposes a review paper focusing on the methodology, enhancing the level of completion of the preceding research, and the trend of examining the oxidation process and landscape of the moon. We propose using Anokhin’s Conditionally-Independent Pixel Synthesis (CIPS) as a model to be used in future experiments as a result of the review. CIPS can generate pixels independently for each color value, and since it uses a Multi-Layer Perceptron (MLP) network rather than spatial convolutions, there is a significant advantage in scalability. It is concluded that the proposed methodology will save time and costs of the existing research in progress and will help reveal the causal relationship more clearly.

An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery

Lunar craters and rilles are significant topographic features on the lunar surface that will play an essential role in future research on space energy resources and geological evolution. However, previous studies have shown low efficiency in detecting lunar impact craters and poor accuracy in detecting lunar rilles. There is no complete automated identification method for lunar features to explore space energy resources further. In this paper, we propose a new specific deep-learning method called high-resolution global–local networks (HR-GLNet) to explore craters and rilles and to discover space energy simultaneously. Based on the GLNet network, the ResNet structure in the global branch is replaced by HRNet, and the residual network and FPN are the local branches. Principal loss function and auxiliary loss function are used to aggregate global and local branches. In experiments, the model, combined with transfer learning methods, can accurately detect lunar craters, Mars craters, and lunar rilles. Compared with other networks, such as UNet, ERU-Net, HRNet, and GLNet, GL-HRNet has a higher accuracy (88.7 ± 8.9) and recall rate (80.1 ± 2.7) in lunar impact crater detection. In addition, the mean absolute error (MAE) of the GL-HRNet on global and local branches is 0.0612 and 0.0429, which are better than the GLNet in terms of segmentation accuracy and MAE. Finally, by analyzing the density distribution of lunar impact craters with a diameter of less than 5 km, it was found that: (i) small impact craters in a local area of the lunar north pole and highland (5°–85°E, 25°–50°S) show apparent high density, and (ii) the density of impact craters in the Orientale Basin is not significantly different from that in the surrounding areas, which is the direction for future geological research.

Awakening sleeper cells: a narrative review on bacterial magic spot synthetases as potential drug targets to overcome persistence

Magic spot synthetases are emerging targets to overcome persistence caused by stringent response. The 'stringent response' is a bacterial stress survival mechanism, which results in the accumulation of alarmones (also called Magic spots) leading to the formation of dormant persister cells. These 'sleeper cells' evade antibiotic treatment and could result in relapse of infection. This review broadly investigates the phenomenon of stringent response and persistence, and specifically discusses the distribution, classification, and nomenclature of proteins such as Rel/SpoT homologs (RSH), responsible for alarmone synthesis. The authors further explain the relevance of RSH as potential drug targets to break the dormancy of persister cells commonly seen in biofilms. One of the significant factors that initiate alarmone synthesis is nutrient deficiency. In a starved condition, ribosome-associated RSH detects deacylated tRNA and initiates alarmone synthesis. Accumulation of alarmones has a considerable effect on bacterial physiology, virulence, biofilm formation, and persister cell formation. Preventing alarmone synthesis by inhibiting RSH responsible for alarmone synthesis will prevent or reduce persister cells' formation. Magic spot synthetases are thus potential targets that could be explored to overcome persistence seen in biofilms.

The Quaking RNA-binding proteins as regulators of cell differentiation

The RNA-binding protein Quaking (QKI) has emerged as a potent regulator of cellular differentiation in developmental and pathological processes. The QKI gene is itself alternatively spliced to produce three major isoforms, QKI-5, QKI-6, and QKI-7, that possess very distinct functions. Here, we highlight roles of the different QKI isoforms in neuronal, vascular, muscle, and monocyte cell differentiation, and during epithelial-mesenchymal transition in cancer progression. QKI isoforms control cell differentiation through regulating alternative splicing, mRNA stability and translation, with activities in gene transcription now also becoming evident. These diverse functions of the QKI isoforms contribute to their broad influences on RNA metabolism and cellular differentiation. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Processing > Splicing Regulation/Alternative Splicing RNA in Disease and Development > RNA in Development.

Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy

Multi-threshold image segmentation (MIS) is now a well known image segmentation technique, and many researchers have applied intelligent algorithms to it, but these methods suffer from local optimal drawbacks. This paper presented a novel approach to improve the Salp Swarm Algorithm (SSA), namely EHSSA, and applied it to MIS. Knowing the inaccuracies and discussions on implementation of this method, a new efficient mechanism is proposed to improve global search capability of the algorithm and avoid falling into a local optimum. Moreover, the excellence of the proposed algorithm was proved by comparative experiments at IEEE CEC2014. Afterward, the performance of EHSSA was demonstrated by testing a set of images selected from the Berkeley segmentation data set 500 (BSDS500), and the experimental results were analyzed by evaluating the parameters, which proved the efficiency of the proposed algorithm in MIS. Furthermore, EHSSA was applied to the microscopic image segmentation of breast cancer. Medical image segmentation is the study of how to quickly extract objects of interest (human organs) from various images to perform qualitative and quantitative analysis of diseased tissues and improve the accuracy of their diagnosis, which assists the physician in making more informed decisions and patient rehabilitation. The results of this set of experiments also proved its superior performance. For any info about this paper, readers can refer to https://aliasgharheidari.com.

A protocol for single-source dual-pulse stimulated emission depletion setup with Bessel modulation

STimulated Emission Depletion (STED) microscopy attains super-resolution in biological imaging beyond the diffraction limit. Here, we give a concise protocol to construct a dual-pulse STED setup with one super-continuum laser. Moreover, a flexible and dismountable Bessel modulation module is introduced for potential 2D-stack STED imaging. Experiments and notices are introduced in detail, with discussion on some important check-points for STED, such as detector saturation. Finally, the results validate the system working.

Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine

Coronavirus Disease 2019 (COVID-19) was distributed globally at the end of December 2019 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early diagnosis and successful COVID-19 assessment are missing, clinical care is ineffective, and deaths are high. In this study, we investigate whether the level of biochemical indicators helps to discriminate and classify the severity of the COVID-19 using the machine learning method. This research creates an efficient intelligence method for the diagnosis of COVID-19 from the perspective of biochemical indexes. The framework is proposed by integrating an enhanced new stochastic called the colony predation algorithm (CPA) with a kernel extreme learning machine (KELM), abbreviated as ECPA-KELM. The core feature of the approach is the ECPA algorithm which incorporates the two main operators that have been abstained from the grey wolf optimizer and moth-flame optimizer to improve and restore the CPA research functions and are simultaneously used to optimize the parameters and to select features for KELM. The ECPA output is checked thoroughly using IEEE CEC2017 benchmark to verify the capacity of the proposed methodology. Finally, in the diagnosis of COVID-19 using biochemical indexes, the designed ECPA-KELM model and other competing KELM models based on other optimization are used. Checking statistical results will display improved predictive properties for all metrics and higher stability. ECPA-KELM can also be used to discriminate and classify the severity of the COVID-19 as a possible computer-aided method and provide effective early warning for the treatment and diagnosis of COVID-19.

The Fermi-Dirac distribution provides a calibrated probabilistic output for binary classifiers

While it would be desirable that the output of binary classification algorithms be the probability that the classification is correct, most algorithms do not provide a method to calculate such a probability. We propose a probabilistic output for binary classifiers based on an unexpected mapping of the probability of correct classification to the probability of occupation of a fermion in a quantum system, known as the Fermi–Dirac distribution. This mapping allows us to compute the optimal threshold to separate predicted classes and to calculate statistical parameters necessary to estimate confidence intervals of performance metrics. Using this mapping we propose an ensemble learning algorithm. In short, the Fermi–Dirac distribution provides a calibrated probabilistic output for binary classification.

Binary classification is one of the central problems in machine-learning research and, as such, investigations of its general statistical properties are of interest. We studied the ranking statistics of items in binary classification problems and observed that there is a formal and surprising relationship between the probability of a sample belonging to one of the two classes and the Fermi–Dirac distribution determining the probability that a fermion occupies a given single-particle quantum state in a physical system of noninteracting fermions. Using this equivalence, it is possible to compute a calibrated probabilistic output for binary classifiers. We show that the area under the receiver operating characteristics curve (AUC) in a classification problem is related to the temperature of an equivalent physical system. In a similar manner, the optimal decision threshold between the two classes is associated with the chemical potential of an equivalent physical system. Using our framework, we also derive a closed-form expression to calculate the variance for the AUC of a classifier. Finally, we introduce FiDEL (Fermi–Dirac-based ensemble learning), an ensemble learning algorithm that uses the calibrated nature of the classifier’s output probability to combine possibly very different classifiers.

Split-Attention Networks with Self-Calibrated Convolution for Moon Impact Crater Detection from Multi-Source Data

Impact craters are the most prominent features on the surface of the Moon, Mars, and Mercury. They play an essential role in constructing lunar bases, the dating of Mars and Mercury, and the surface exploration of other celestial bodies. The traditional crater detection algorithms (CDA) are mainly based on manual interpretation which is combined with classical image processing techniques. The traditional CDAs are, however, inefficient for detecting smaller or overlapped impact craters. In this paper, we propose a Split-Attention Networks with Self-Calibrated Convolution (SCNeSt) architecture, in which the channel-wise attention with multi-path representation and self-calibrated convolutions can generate more prosperous and more discriminative feature representations. The algorithm first extracts the crater feature model under the well-known target detection R-FCN network framework. The trained models are then applied to detecting the impact craters on Mercury and Mars using the transfer learning method. In the lunar impact crater detection experiment, we managed to extract a total of 157,389 impact craters with diameters between 0.6 and 860 km. Our proposed model outperforms the ResNet, ResNeXt, ScNet, and ResNeSt models in terms of recall rate and accuracy is more efficient than that other residual network models. Without training for Mars and Mercury remote sensing data, our model can also identify craters of different scales and demonstrates outstanding robustness and transferability.

Absence of a long-lived lunar paleomagnetosphere

Determining the presence or absence of a past long-lived lunar magnetic field is crucial for understanding how the Moon's interior and surface evolved. Here, we show that Apollo impact glass associated with a young 2 million-year-old crater records a strong Earth-like magnetization, providing evidence that impacts can impart intense signals to samples recovered from the Moon and other planetary bodies. Moreover, we show that silicate crystals bearing magnetic inclusions from Apollo samples formed at ∼3.9, 3.6, 3.3, and 3.2 billion years ago are capable of recording strong core dynamo-like fields but do not. Together, these data indicate that the Moon did not have a long-lived core dynamo. As a result, the Moon was not sheltered by a sustained paleomagnetosphere, and the lunar regolith should hold buried ³He, water, and other volatile resources acquired from solar winds and Earth's magnetosphere over some 4 billion years.

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a new ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was successfully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com.

Combined strategies for effective cancer immunotherapy with a novel anti-CD47 monoclonal antibody

CD47 is a widely expressed cell-surface protein that regulates phagocytosis mediated by cells of the innate immune system, such as macrophages and dendritic cells. CD47 serves as the ligand for a receptor on these innate immune cells, signal regulatory protein (SIRP)-α, which in turn inhibits phagocytosis. Several targeted CD47 therapeutic antibodies have been investigated clinically; however, how to improve its therapeutic efficacy remains unclear. Herein, we developed a CD47 blocking antibody, named IBI188, that could specifically block the CD47-SIRP-α axis, which transduces the "don't eat me" signal to macrophages. In vitro phagocytosis assays demonstrated the pro-phagocytosis ability of IBI188. Furthermore, several in vivo models were chosen to evaluate the anti-tumor efficacy of IBI188. IBI188 treatment upregulated cell movement- and inflammation-related genes in macrophages. Synergism was observed when combined with an anti-CD20 therapeutic antibody, whose function depends on antibody-dependent cellular cytotoxicity/phagocytosis (ADCC/ADCP). CD47 expression was evaluated following azacytidine (AZA) treatment, a standard-of-care for patients with multiple myeloma; enhanced anti-tumor efficacy was observed in the combination group in AML xenograft models. Notably, IBI188 treatment increased vascular endothelial growth factor-A (VEGF-A) levels in a solid tumor model, and combined treatment with an anti-VEGF-A antibody and IBI188 resulted in an enhanced anti-tumor effect. These data indicate that IBI188 is a therapeutic anti-CD47 antibody with anti-tumor potency, which can be enhanced when used in combination with standard-of-care drugs for cancer treatment.

Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis

Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (FKNN) is widely used in literature. The parameters have an essential impact on the performance of FKNN. Hence, the parameters need to be attuned to suit different problems. Also, choosing more representative features can enhance the performance of FKNN. This research proposes an improved optimization technique based on the sine cosine algorithm (LSCA), which introduces a linear population size reduction mechanism for enhancing the original algorithm's performance. Moreover, we developed an FKNN model based on the LSCA, it simultaneously performs feature selection and parameter optimization. Firstly, the search performance of LSCA is verified on the IEEE CEC2017 benchmark test function compared to the classical and improved algorithms. Secondly, the validity of the LSCA-FKNN model is verified on three medical datasets. Finally, we used the proposed LSCA-FKNN to predict lupus nephritis classes, and the model showed competitive results. The paper will be supported by an online web service for any question at https://aliasgharheidari.com.

Enzyme/Nanocopper Hybrid Nanozymes: Modulating Enzyme-like Activity by the Protein Structure for Biosensing and Tumor Catalytic Therapy

Artificial enzymes with modulated enzyme-mimicking activities of natural systems represent a challenge in catalytic applications. Here, we show the creation of artificial Cu metalloenzymes based on the generation of Cu nanoparticles in an enzyme matrix. Different enzymes were used, and the structural differences between the enzymes especially influenced the controlled the size of the nanoparticles and the environment that surrounds them. Herein, we demonstrated that the oxidase-like catalytic activity of these copper nanozymes was rationally modulated by enzyme used as a scaffold, with a special role in the nanoparticle size and their environment. In this sense, these nanocopper hybrids have confirmed the ability to mimic a unique enzymatic activity completely different from the natural activity of the enzyme used as a scaffold, such as tyrosinase-like activity or as Fenton catalyst, which has extremely higher stability than natural mushroom tyrosinase. More interestingly, the oxidoreductase-like activity of nanocopper hybrids was cooperatively modulated with the synergistic effect between the enzyme and the nanoparticles improving the catalase activity (no peroxidase activity). Additionally, a novel dual (metallic and enzymatic activity) of the nanozyme made the highly improved catechol-like activity interesting for the design of 3,4-dihydroxy-l-phenylalanine (l-DOPA) biosensor for detection of tyrosinase. These hybrids also showed cytotoxic activity against different tumor cells, interesting in biocatalytic tumor therapy.