Miracle-dz/UniFAR-FTUs huggingface dataset:
Lyz2-Cre-Mediated Genetic Deletion of Septin7 Reveals a Role of Septins in Macrophage Cytokinesis and Kras-Driven Tumorigenesis

By crossing septin7-floxed mice with Lyz2-Cre mice carrying the Cre recombinase inserted in the Lysozyme-M (Lyz2) gene locus we aimed the specific deletion of septin7 in myeloid cells, such as monocytes, macrophages and granulocytes. Septin7 flox/flox :Lyz2-Cre mice show no alterations in the myeloid compartment. Septin7-deleted macrophages (BMDMs) were isolated and analyzed. The lack of Septin7 expression was confirmed and a constitutive double-nucleation was detected in Septin7-deficient BMDMs indicating a defect in macrophage cytokinesis. However, phagocytic function of macrophages as judged by uptake of labelled E. coli particles and LPS-stimulated macrophage activation as judged by induction of TNF mRNA expression and TNF secretion were not compromised. In addition to myeloid cells, Lyz2-Cre is also active in type II pneumocytes (AT2 cells). We monitored lung adenocarcinoma formation in these mice by crossing them with the conditional knock-in Kras-LSL-G12D allele. Interestingly, we found that control mice without septin7 depletion die after 3–5 weeks, while the Septin7-deficient animals survived 11 weeks or even longer. Control mice sacrificed in the age of 4 weeks display a bronchiolo-alveolar hyperplasia with multiple adenomas, whereas the Septin7-deficient animals of the same age are normal or show only a weak multifocal brochiolo-alveolar hyperplasia. Our findings indicate an essential role of Septin7 in macrophage cytokinesis but not in macrophage function. Furthermore, septin7 seems absolutely essential for oncogenic Kras-driven lung tumorigenesis making it a potential target for anti-tumor interventions.
Long-Reads-Based Metagenomics in Clinical Diagnosis With a Special Focus on Fungal Infections

Identification of the causative infectious agent is essential in the management of infectious diseases, with the ideal diagnostic method being rapid, accurate, and informative, while remaining cost-effective. Traditional diagnostic techniques rely on culturing and cell propagation to isolate and identify the causative pathogen. These techniques are limited by the ability and the time required to grow or propagate an agent in vitro and the facts that identification based on morphological traits are non-specific, insensitive, and reliant on technical expertise. The evolution of next-generation sequencing has revolutionized genomic studies to generate more data at a cheaper cost. These are divided into short- and long-read sequencing technologies, depending on the length of reads generated during sequencing runs. Long-read sequencing also called third-generation sequencing emerged commercially through the instruments released by Pacific Biosciences and Oxford Nanopore Technologies, although relying on different sequencing chemistries, with the first one being more accurate both platforms can generate ultra-long sequence reads. Long-read sequencing is capable of entirely spanning previously established genomic identification regions or potentially small whole genomes, drastically improving the accuracy of the identification of pathogens directly from clinical samples. Long-read sequencing may also provide additional important clinical information, such as antimicrobial resistance profiles and epidemiological data from a single sequencing run. While initial applications of long-read sequencing in clinical diagnosis showed that it could be a promising diagnostic technique, it also has highlighted the need for further optimization. In this review, we show the potential long-read sequencing has in clinical diagnosis of fungal infections and discuss the pros and cons of its implementation.
Effect of Cascade Surface Roughness on Boundary Layer Flow Under Variable Conditions

Under the influence of many factors, the surface roughness of the cascade will change during turbomachinery operation, which will affect the boundary layer flow of the cascade. In this article, the effects of cascade surface roughness on boundary layer flow under variable conditions are analyzed by experiments and numerical simulation. The results show that with the increase of roughness, the total pressure loss coefficient of the cascade decreases first and then increases. The larger the Reynolds number is, the greater the total pressure loss coefficient is, and the sensitive area of loss change is changed. In the sensitive area, the roughness has a greater influence on cascade loss. There are separation bubbles at the suction front edge of smooth cascades. With the increase of roughness, the degree of turbulence increases, and the transition process is accelerated. When the roughness is between 74 and 150 μm, the separation bubble disappears and the separation loss decreases. In conclusion, the aerodynamic loss of the cascade increases with the increase of roughness, and the cascade efficiency decreases. However, roughness can restrain the flow separation and reduce the separation loss. The two have gone through a process of one and the other. When the roughness is 74 μm, the displacement thickness, momentum thickness, and shape factor at the back of the cascade are the minimum.
Bacterial Metabolic Potential and Micro-Eukaryotes Enriched in Stony Coral Tissue Loss Disease Lesions

The epizootic disease outbreak known as stony coral tissue loss disease (SCTLD) is arguably the most devastating coral disease in recorded history. SCTLD emerged off the coast of South Florida in 2014 and has since moved into the Caribbean, resulting in coral mortality rates that have changed reef structure and function. Currently, the cause of SCTLD is unknown, but there is evidence from 16S rRNA gene sequencing and bacterial culture studies that the microbial community plays a role in the progression of SCTLD lesions. In this study, we applied shotgun metagenomics to characterize the potential function of bacteria, as well as the composition of the micro-eukaryotic community, associated with SCTLD lesions. We re-examined samples that were previously analyzed using 16S rRNA gene high-throughput sequencing from four coral species: Stephanocoenia intersepta, Diploria labyrinthiformis, Dichocoenia stokesii, and Meandrina meandrites. For each species, tissue from apparently healthy (AH) corals, and unaffected tissue (DU) and lesion tissue (DL) on diseased corals, were collected from sites within the epidemic zone of SCTLD in the Florida Keys. Within the micro-eukaryotic community, the taxa most prominently enriched in DL compared to AH and DU tissue were members of Ciliophora. We also found that DL samples were relatively more abundant in less energy-efficient pathways like the pentose phosphate pathways. While less energy-efficient processes were identified, there were also relatively higher abundances of nucleotide biosynthesis and peptidoglycan maturation pathways in diseased corals compared to AH, which suggests there was more bacteria growth in diseased colonies. In addition, we generated 16 metagenome-assembled genomes (MAGs) belonging to the orders Pseudomonadales, Beggiatoales, Rhodobacterales, Rhizobiales, Rs-D84, Flavobacteriales, and Campylobacterales, and all MAGs were enriched in DL samples compared to AH samples. Across all MAGs there were antibiotic resistance genes that may have implications for the treatment of SCTLD with antibiotics. We also identified genes and pathways linked to virulence, such as nucleotide biosynthesis, succinate dehydrogenase, ureases, nickel/iron transporters, Type-1 secretion system, and metalloproteases. Some of these enzymes/pathways have been previously targeted in the treatment of other bacterial diseases and they may be of interest to mitigate SCTLD lesion progression.
Implementasi Kontrol Model Prediksi Berbasis ANFIS Pada Mesin Penghasil Uap Air

Steam generator has problem such as dependent variable on the output parameters, therefore it is needed a way to get system behavior of steam generator which is represented by model of the plant. Implementation of the model predictive control in this research was represented by using simulation, so the object that was controlled was virtual steam generator. In this research was used MATLAB software as a virtual steam generator and also for computes ANFIS, whereas the LabVIEW software was used as a representation of control room . From the research, it was found the best parameters for each ANFIS that was used as a model unit in the model predictive control, that was by using historical data 4 th as much as 800 datas, the percentage ratio of learning for training data and checking data on each ANFIS for each model structures sequentially by 90% and 10%, except the percentage ratio for ANFIS on water flow parameter sequentially by 80% and 20%. The results of validation RMSE (Root Mean Square Error) by testing for 100 datas, it was obtained values as follows: water flow=1.9941, water pressure=48.0236, air flow=604.0621, fuel gas pressure=0.7087, fuel gas temperature=18.6594, O 2 content=0.9591, steam pressure=76.1557, steam quality=3.9734 and steam flow=264.9173.
Building a Drone Operator Digital Twin using a Brain-Computer Interface for Emotion Recognition

Drones are often associated with data collection operations and monitoring services. Thus, they are particularly relevant for performing high-risk tasks remotely, allowing the operator to execute them safely. These operations require a high level of attention and focus from the operator; however, humans are prone to stress and distractions, which negatively impacts the control he delivers. Brain-computer interfaces can be a way to enable the analysis of the emotional states of the drone operator. In this paper, we propose a decision-making system that evaluates the emotional state of the drone operator to allow commands to be sent to the drone. By developing a digital twin of the operator capable of classifying the emotional states at both cognitive and visual levels, the system can decide whether the operator is in a proper emotional state to control the drone. Necessary information is computed and forwarded to the drone through a ROS2 client node. Initial evaluation results indicate that the digital twin can detect emotions and mood swings in a real-time setting and that the system may provide a reliable and secure way of validating drone commands through the mind.
Detecting the Surface Defects of the Magnetic- Tile Based on Improved YOLACT ++

In the process of manufacture of magnetic tiles, especially in the production and transportation, the defects such as crack, stains, uneven and break will appear inevitably. To defect different defects of the magnetic tiles effectively, this paper proposes an improved YOLACT++ based on attention, and because of the YOLACT++ is a real-time instance segmentation model, which is constructed by deep convolution neural network. In our methods, we add attention mechanism SE-attention module and lightweight networks to the YOLACT++ structure, then use Soft-NMS (Non-Maximum Suppression) at the forecaster and add pre-trained weights. The experiments demonstrate that our method can achieve FPS gain of 3.9 points while mAP gain of 1.8 points over YOLACT++. The experimental results show that our method has fine robustness as well as stability for the detection of the magnetic tiles. The speed and accuracy of magnetic tile detection exceed the previous detection algorithm.
A hardware-in-the-loop simulator for physical human-aerial manipulator cooperation

A hardware-in-the-loop simulator for human cooperation with an aerial manipulator is presented in this paper. The simulator provides the user with realistic haptic feedback proper of a human-aerial manipulator interaction activity. The forces exchanged between the hardware interface and the hu-man/environment are measured and supplied to a dynamically simulated aerial manipulator. In turn, the simulated aerial platform feeds back its position to the hardware allowing the human to feel and evaluate the interaction effects. Besides human-aerial manipulator cooperation, the simulator lends itself to developing and testing autonomous control strategies in aerial manipulation. Therefore, the effectiveness of the proposed system is evaluated along with two case studies: a collaborative task where the human operator attaches a tool to the robot end-effector and an autonomous bird diverter installation task.
Task Allocation for Heterogeneous Multi-agent System via Game-Based Learning

In this paper, heterogeneous multi-agent system is employed to perform tasks. We propose a novel task allocation method based on potential game under the limited communication range of each agent. Firstly, the task allocation problem is formulated as a potential game with the goal of maximizing task utility. Then we confirm the existence of Nash Equilibrium (NE). Furthermore, a concurrent best response algorithm with rapid convergence is proposed to achieve the desirable solution. Finally, the simulation results show the effectiveness of the proposed algorithm.
Geometric Calibration of a New Miniaturized Endoscopic Ultrasound Probe

The present paper proposes a method for the geometric calibration of a new endoscopic ultrasound (US) probe designed for the imaging of the inner ear. Such US probe has 24 elements and a distal diameter equal to 4 mm. The atypical probe miniaturized geometry may raise doubts about the applicability of existing state-of-the-art calibration methods. This work answers such question. The results presented in this paper indicate that the proposed straightforward calibration procedure leads to satisfying accuracy. The phantom used for the geometric calibration have wires as reference geometry and its dimensions are taken based on a microtomography acquisition. The obtained results may enable the US image-guided navigation of the human inner ear.
A Receiving SPICE Model of the Dipole Antenna Illuminated by an Electromagnetic Wave

Electromagnetic wave can be coupled into electronic system through the received antenna, which cause serious electromagnetic interference with the electronic system. This paper presents a receiving SPICE model to analyze the response on terminal loads of dipole antenna illuminated by an electromagnetic wave. Based on the frequency-domain expression of the antenna receiving model and Vector-Fitting method, the receiving SPICE model of the dipole antenna is obtained. The SPICE model is composed of lumped elements, and only related to the structure of the dipole antenna and the incident electromagnetic wave, but independent of the loads. So, the SPICE model can be easily used in analyzing the dipole antenna with any loads. The simulation results by using this SPICE model are in good agreement with that of full wave analysis simulator (CST).
Towards a Generic Grasp Planning Pipeline using End-Effector Specific Primitive Grasping Actions

In the past few years, several robotic end-effectors based on diverse kinematics and actuation principles have been developed to provide grasping and manipulation functionalities. To ease the control and application of these wide-ranging end-effectors, the development of effective reusable tools that can facilitate the end-effector motion planning and control is necessary. In this work, we introduce a generic grasp planner that leverages on the concept of the primitive grasping actions. Given the specific characteristics of an end-effector, including its kinematic and actuation arrangements, a number of primitive grasping actions are extracted and employed by the proposed grasp planner to autonomously plan and synthesize more complex grasping behaviours. The grasp planner is validated through experimental trials involving the HERI II robotic hand, a four-fingers tendon-driven under-actuated hand. The results of these experiments demonstrate the efficacy of the proposed method to generate appropriate planning actions enabling to grasp objects of different shapes.
Improving Clustering-Based Forecasting of Aggregated Distribution Transformer Loadings With Gradient Boosting and Feature Selection

Load forecasting is more important than ever to enable new monitor and control functionalities of distribution networks aiming to mitigate the impact of the energy transition. Load forecasting at medium voltage (MV) level is becoming more challenging, because these load profiles become more stochastic due to the increasing penetration of photovoltaic (PV) generation in distribution networks. This work combines medium to low voltage (MV/LV) transformer loadings measured with advanced metering infrastructure (AMI) and machine learning (ML) algorithms to propose a new clustering based day-ahead aggregated load forecasting approach. This four-step approach improves the day-ahead load forecast of a city. First, MV/LV transformer loadings are clustered based on the shape of their load pattern. Second, a gradient boosting algorithm is used to forecast the load of each cluster and calculate the related feature importance. Third, feature selection is applied to improve the forecast accuracy of each cluster. Finally, the day-ahead load forecast of all clusters are aggregated. The case study presented uses 519 measured MV/LV transformer loadings in a city to perform 30 day-ahead load forecasts. Compared against the day-ahead aggregated load forecast without clustering, the average normalized root mean squared error (NRMSE) reduced 12.7 %, the average mean absolute percentage error (MAPE) reduced 18.2 % and the average Pearson Correlation Coefficient (PCC) increased 0.37 %. The 95 % confidence interval of the difference between the average NRMSE, MAPE and PCC without clustering and with the proposed method indicates a statistically significant improvement in accuracy.
A Hybrid Resource Allocation Method for URLLC Based on NOMA

Aiming at the ultra high reliability and low latency requirements for the emergent URLLC business, this paper proposes a hybrid resource allocation method based on nonorthogonal multiple access technology. In order to improve the utilization of frequency resources, non-orthogonal multiple access technology is used to share URLLC user resources in the system. At the same time, in order to ensure the ultra-reliability and low latency requirements of URLLC users, the resources are divided into shared resources and private resources. When the shared resources cannot support the transmission requirements of URLLC users, the private resources are used for transmission. The simulation results show that the proposed method can effectively improve the transmission rate of URLLC users in the system compared with the only NOMA method scheme.
Improved Seagull Optimization Algorithm to Optimize Neural Networks with Gated Recurrent Units for Network Intrusion Detection

In the field of network intrusion detection based on a neural network with Gated Recurrent Units (GRU), the accuracy of classification decreases due to the improper setting of the parameters. Therefore an Improved Seagull Optimization Algorithm (ISOA) to optimize GRU parameters has been proposed. The ISOA is proposed to address the shortcomings of the Seagull Optimization Algorithm (SOA) of low convergence accuracy and slow convergence speed in dealing with global optimization problems. The algorithm uses an opposition-based learning method instead of a random method to initialize the population so that the population has a better diversity. To balance the overall and local search capability of the algorithm, in this paper, we design a special factor update formula that converges nonlinearly with the number of iterations, allowing the algorithm to better jump out of locally optimal solutions and speed up convergence. The improved algorithm is used to obtain better parameters and uses them to set the parameters of the GRU to build a better-performing classification model. Multiple UCI datasets are used and compared with other methods and finally validated using the NSL-KDD datasets for network intrusion detection. The experimental results show that compared to the performance of long short-term memory(LSTM) units, multilayer perceptron(MLP), and the traditional neural network with GRU, the classification accuracy of the method with ISOA for optimizing GRU is improved on each dataset, which helps to improve the classification performance in network intrusion detection.
Deep COVID-19 Recognition Using Chest X-ray Images: A Comparative Analysis

The novel coronavirus variant, which is also widely known as COVID-19, is currently a common threat to all humans across the world. Effective recognition of COVID-19 using advanced machine learning methods is a timely need. Although many sophisticated approaches have been proposed in the recent past, they still struggle to achieve expected performances in recognizing COVID-19 using chest X-ray images. In addition, the majority of them are involved with the complex pre-processing task, which is often challenging and time-consuming. Meanwhile, deep networks are end-to-end and have shown promising results in image-based recognition tasks during the last decade. Hence, in this work, some widely used state-of-the-art deep networks are evaluated for COVID-19 recognition with chest X-ray images. All the deep networks are evaluated on a publicly available chest X-ray image datasets. The evaluation results show that the deep networks can effectively recognize COVID-19 from chest X-ray images. Further, the comparison results reveal that the EfficientNetB7 network outperformed other existing state-of-the-art techniques.
Finding Optimal BLE Configuration for Indoor Positioning with Consumption Restrictions

Bluetooth Low Energy (BLE) fingerprinting has gained a lot of research effort in recent years due to flexibility in both beacons placement and configuration. Different works have addressed the effect of the configuration parameters, mainly the transmission power (Tx) and period (Ts), over positioning accuracy but not on the system lifespan and the trade-off between these two. In this work, different configurations of one, three and six slots have been tested over the same experimental setup. Positioning accuracy was obtained using different variations of the Weighted k-Nearest Neighbours (Wk-NN) algorithm, and the system lifespan was estimated using the actual current consumption and transmission mechanism for each configuration. Experimental results have shown that Tx and the number of slots can be adjusted to optimize this trade-off; meanwhile, changes in Ts worsen Wk-NN results more than in the other parameters, showing that the minimum Ts is always the best option.