Miracle-dz/UniFAR-FTUs huggingface dataset:
Surveillance of Human Rotaviruses in Wuhan, China (2019–2022): Whole-Genome Analysis of Emerging DS-1-like G8P[8] Rotavirus

Group A rotaviruses (RVAs) are major etiologic agents of gastroenteritis in infants and young children worldwide. To study the prevalence and genetic characteristics of RVAs, a hospital-based surveillance study was conducted in Wuhan, China from June 2019 through May 2022. The detection rates of RVAs were 19.40% (142/732) and 3.51% (8/228) in children and adults, respectively. G9P[8] was the predominant genotype, followed by G8P[8] and G3P[8]. G8P[8] emerged and was dominant in the 2021–2022 epidemic season. The genome constellation of six G8P[8] strains was assigned to G8-P[8]-I2-R2-C2-M2-A2-N2-T2-E2-H2. Phylogenetic analysis revealed that the VP7, VP4, VP2, VP3, NSP1, NSP2, NSP3, and NSP5 genes of these G8P[8] strains clustered closely with those of the G8P[8] strains in Asia and were distant from those of the P[8] and G2P[4] strains simultaneously detected in Wuhan. In contrast, the VP1, VP6, and NSP4 genes were closely related to the typical G2P[4] rotavirus, including those of G2P[4] strains simultaneously detected in Wuhan. The detection rate of RVAs decreased in the COVID-19 pandemic era. It was deduced that the G8P[8] rotaviruses that emerged in China may be reassortants, carrying the VP6, VP1, and NSP4 genes derived from the G2P[4] rotavirus in the backbone of the neighboring DS-1-like G8P[8] strains represented by CAU17L-103.
Influence of irrigation and supplementation on performance and ingestive behavior of beef cattle on mixed grass pastures

- The purpose of this study was to evaluate the performance of beef cattle with two levels of energy-protein supplementation of low-consumption on African Bermudagrass pasture, overseeded in winter with oat and ryegrass, with or without irrigation. Twenty-four castrated Angus steers (11 months old and had an initial average of 220 kg body weight (BW)) were used in experimental area of 3.6 ha. The experiment was completely randomized in a 2×2 factorial design, with three replicates. The evaluation period was 249 days (July/2019 to March/2020). The treatments were: irrigated pasture with supplementation of 1 g/kg BW or 2.7 g/kg BW and non-irrigated pasture with 1 g/kg BW of supplement or 2.7 g/kg BW. The grazing method was continuous with a variable stocking rate. Irrigation provided pastures with better chemical composition in winter and spring. Irrigation increased the daily accumulation rate in winter (84.6 vs. 45.9 kg DM/ha/day), providing a greater stocking rate (1,702 vs. 1,385 kg/ha) and, consequently, body weight gain per hectare. Supplementation of 2.7 g/kg BW provided a greater stocking rate in winter (1,652 vs. 1,435 kg/ha) and spring (3,096 vs. 2,811 kg/ha), not changing in summer. The association of irrigation and supplementation of 2.7 vs. 1.0 g/kg BW improves the intake pattern by the animal in summer without changing productivity parameters. Irrigation increases productivity and the nutritional value of pasture with higher livestock production per area in periods of water deficit. The supply of 2.7 vs. 1.0 g/kg BW provides a greater stocking rate and body
Assessing the Sustainability of Photodegradation and Photocatalysis for Wastewater Reuse in an Agricultural Resilience Context

The growths in worldwide population—of up to 8.5 billion people by 2030—and agriculture have put great pressure on water resources, above all in arid and drought-prone areas. Nowadays, water scarcity, drought and the pollution of wastewater are considered major issues of concern. For this reason, the authors provided an overview of two methods of wastewater water purification and removing pollutants for use in crop irrigation in a sustainable manner. The novelty lies in the reuse of recovered wastewater, purified through photodegradation and photocatalysis technologies using solar energy. The knowledge of the environmental impacts associated with the use of recycled water with these photo-processes to irrigate crops under field conditions is still scarce. In the future, this issue will be important. In particular, photodegradation and photocatalysis achieve a sustainable reduction in contaminants contained in wastewater of between 35% and 100%. The use of bismuth vanadate supports the complete removal of pollutants, and the implementation of catalytic membranes makes these processes more circular. This research was performed under the “Progetto GRINS “Growing Resilient, Inclusive and Sustainable” with the aim of “Building a dataset for the circular economy of the main Italian production systems”.
A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments

In unknown environments, mobile robots can use visual-based Simultaneous Localization and Mapping (vSLAM) to complete positioning tasks while building sparse feature maps and dense maps. However, the traditional vSLAM works in the hypothetical environment of static scenes and rarely considers the dynamic objects existing in the actual scenes. In addition, it is difficult for the robot to perform high-level semantic tasks due to its inability to obtain semantic information from sparse feature maps and dense maps. In order to improve the ability of environment perception and accuracy of mapping for mobile robots in dynamic indoor environments, we propose a semantic information-based optimized vSLAM algorithm. The optimized vSLAM algorithm adds the modules of dynamic region detection and semantic segmentation to ORB-SLAM2. First, a dynamic region detection module is added to the vision odometry. The dynamic region of the image is detected by combining single response matrix and dense optical flow method to improve the accuracy of pose estimation in dynamic environment. Secondly, the semantic segmentation of images is implemented based on BiSeNet V2 network. For the over-segmentation problem in semantic segmentation, a region growth algorithm combining depth information is proposed to optimize the 3D segmentation. In the process of map building, semantic information and dynamic regions are used to remove dynamic objects and build an indoor map containing semantic information. The system not only can effectively remove the effect of dynamic objects on the pose estimation, but also use the semantic information of images to build indoor maps containing semantic information. The proposed algorithm is evaluated and analyzed in TUM RGB-D dataset and real dynamic scenes. The results show that the accuracy of our algorithm outperforms that of ORB-SLAM2 and DS-SLAM in dynamic scenarios.
Anti-Cancer Prodrug Cyclophosphamide Exerts Thrombogenic Effects on Human Venous Endothelial Cells Independent of CYP450 Activation—Relevance to Thrombosis

Cancer patients are at a very high risk of serious thrombotic events, often fatal. The causes discussed include the detachment of thrombogenic particles from tumor cells or the adverse effects of chemotherapeutic agents. Cytostatic agents can either act directly on their targets or, in the case of a prodrug approach, require metabolization for their action. Cyclophosphamide (CPA) is a widely used cytostatic drug that requires prodrug activation by cytochrome P450 enzymes (CYP) in the liver. We hypothesize that CPA could induce thrombosis in one of the following ways: (1) damage to endothelial cells (EC) after intra-endothelial metabolization; or (2) direct damage to EC without prior metabolization. In order to investigate this hypothesis, endothelial cells (HUVEC) were treated with CPA in clinically relevant concentrations for up to 8 days. HUVECs were chosen as a model representing the first place of action after intravenous CPA administration. No expression of CYP2B6, CYP3A4, CYP2C9 and CYP2C19 was found in HUVEC, but a weak expression of CYP2C18 was observed. CPA treatment of HUVEC induced DNA damage and a reduced formation of an EC monolayer and caused an increased release of prostacyclin (PGI2) and thromboxane (TXA) associated with a shift of the PGI2/TXA balance to a prothrombotic state. In an in vivo scenario, such processes would promote the risk of thrombus formation.
Structure and evolution of flux transfer events near dayside magnetic reconnection dissipation region: MMS observations

We investigate a series of three small-scale flux transfer events (FTEs) associated with reconnected flux ropes, recently generated by a nearby, dayside magnetic reconnection line. The data are observed by the Magnetospheric Multiscale spacecraft near noon local time. We find that the associated FTEs are created by secondary magnetic reconnection and have different magnetic field topologies, which is a similar condition to that expected in the multiple X-line magnetic reconnection (MR) model. The calculated results show that the sizes of the FTEs become larger with the time elapsed and the MR reconnection jets at the FTEs are all located on the trailing and outer edges. The above features indicate that these FTEs are still in the evolutionary stage after they are ejected from the reconnection region. Our observations suggest that mesoscale or even typical size FTEs can be created from secondary MR, initially, and subsequently can evolve to a typical size in the process of spreading.
Influenza A Virus in Pigs in Senegal and Risk Assessment of Avian Influenza Virus (AIV) Emergence and Transmission to Human

We conducted an active influenza surveillance in the single pig slaughterhouse in Dakar to investigate the epidemiology and genetic characteristics of influenza A viruses (IAVs) and to provide serologic evidence of avian influenza virus (AIV) infection in pigs at interfaces with human populations in Senegal. Nasal swab and blood samples were collected on a weekly basis from the same animal immediately after slaughter. Influenza A viruses were diagnosed using RT-qPCR and a subset of positive samples for H3 and H1 subtypes were selected for full genome amplification and NGS sequencing. Serum samples were tested by HI assay for the detection of antibodies recognizing four AIVs, including H9N2, H5N1, H7N7 and H5N2. Between September 2018 and December 2019, 1691 swine nasal swabs were collected and tested. Influenza A virus was detected in 30.7% (520/1691), and A/H1N1pdm09 virus was the most commonly identified subtype with 38.07% (198/520), followed by A/H1N2 (16.3%) and A/H3N2 (5.2%). Year-round influenza activity was noted in pigs, with the highest incidence between June and September. Phylogenetic analyses revealed that the IAVs were closely related to human IAV strains belonging to A/H1N1pdm09 and seasonal H3N2 lineages. Genetic analysis revealed that Senegalese strains possessed several key amino acid changes, including D204 and N241D in the receptor binding site, S31N in the M2 gene and P560S in the PA protein. Serological analyses revealed that 83.5% (95%CI = 81.6–85.3) of the 1636 sera tested were positive for the presence of antibodies against either H9N2, H5N1, H7N7 or H5N2. Influenza H7N7 (54.3%) and H9N2 (53.6%) were the dominant avian subtypes detected in Senegalese pigs. Given the co-circulation of multiple subtypes of influenza viruses among Senegalese pigs, the potential exists for the emergence of new hybrid viruses of unpredictable zoonotic and pandemic potential in the future.
Single UAV-Based Wave Source Localization in NLOS Environments

SUMMARY To localize an unknown wave source in non-line-of-sight environments, awavesourcelocalizationschemeusingmultipleunmanned-aerial-vehicles (UAVs) is proposed. In this scheme, each UAV estimates the direction-of-arrivals (DoAs) of received signals and the wave source is localized from the estimated DoAs by means of maximum likelihood estimation. In this study, by extending the concept of this scheme, we propose a novel wave source localization scheme using a single UAV. In the proposed scheme, the UAV moves on the path comprising multiple measurementpointsandthewavesourceissequentiallylocalizedfromDoAdistributionsestimatedatthesemeasurementpoints.Ateachmeasurement point,witha moving path planning algorithm , the UAV determines the next measurement point from the estimated DoA distributions and measurement points that the UAV has already visited. We consider two moving path planning algorithms, and validate the proposed scheme through simulation experiments.
Cell Cycle Status Influences Resistance to Apoptosis Induced by Oxidative Stress in Human Breast Cancer Cells, Which Is Accompanied by Modulation of Autophagy

Cancer cells are characterised by uncontrolled cell proliferation; however, some of them can temporarily arrest their cell cycle at the G0 or G1 phase, which could contribute to tumour heterogeneity and drug resistance. The cell cycle status plays a critical role in chemosensitivity; however, the influence of G0- and G1-arrest has not been elucidated. To study the cell cycle arrest-mediated resistance, we used MCF-7 cells and generated three populations of cells: (1) cells arrested in the G0-like phase, (2) cells that resumed the cell cycle after the G0-like phase and (3) cells arrested in early G1 with a history of G0-like arrest. We observed that both the G0-like- and the G1-arrested cells acquired resistance to apoptosis induced by oxidative stress, accompanied by a decreased intracellular reactive oxygen species and DNA damage. This effect was associated with increased autophagy, likely facilitating their survival at DNA damage insult. The cell cycle reinitiation restored a sensitivity to oxidative stress typical for cells with a non-modulated cell cycle, with a concomitant decrease in autophagy. Our results support the need for further research on the resistance of G0- and G1-arrested cancer cells to DNA-damaging agents and present autophagy as a candidate for targeting in anticancer treatment.
Glucose Rate-of-Change and Insulin-on-Board Jointly Weighted Zone Model Predictive Control

We present the design and evaluation of closed-loop insulin delivery using zone model predictive control (MPC) featuring an adaptive weighting scheme to address prolonged hyperglycemia due to changes in insulin sensitivity, underdelivery from profile mismatch, and meal composition. In the MPC cost function, the penalty on predicted glucose deviation from the upper zone boundary is weighted by a joint function of predicted glucose rate-of-change (ROC) and insulin-on-board (IOB). The asymmetric weighting gradually increases when glucose ROC and IOB are jointly low, independent of glucose magnitude, to limit hyperglycemia, while aggressively reducing for negative glucose ROC to avoid hypoglycemia. The proposed controller was evaluated using two simulation scenarios: an induced resistance scenario and a nominal scenario to highlight the performance over a reference zone MPC with glucose ROC weighting only. The continuous adaption scheme resulted in consistent improvement for the entire glucose range without incurring an additional risk of hypoglycemia. For the induced resistance and no feedforward bolus scenario, the percent time in 70–180 mg/dL was higher (53.5% versus 48.9%, $p < 0.001$ ) with a larger improvement in the overnight percent time in the tighter glucose range 70–140 mg/dL (70.9% versus 52.9%, $p < 0.001$ ). The results from extensive simulations, as well as clinical validation in three different outpatient studies, demonstrate the utility and safety of the proposed zone MPC.
Review on Type of Sensors and Detection Method of Anti-Collision System of Unmanned Aerial Vehicle

Unmanned aerial vehicle (UAV) usage is increasing drastically worldwide as UAVs are used in various industries for many applications, such as inspection, logistics, agriculture, and many more. This is because performing a task using UAV makes the job more efficient and reduces the workload needed. However, for a UAV to be operated manually or autonomously, the UAV must be equipped with proper safety features. An anti-collision system is one of the most crucial and fundamental safety features that UAVs must be equipped with. The anti-collision system allows the UAV to maintain a safe distance from any obstacles. The anti-collision technologies are of crucial relevance to assure the survival and safety of UAVs. Anti-collision of UAVs can be varied in the aspect of sensor usage and the system’s working principle. This article provides a comprehensive overview of anti-collision technologies for UAVs. It also presents drone safety laws and regulations that prevent a collision at the policy level. The process of anti-collision technologies is studied from three aspects: Obstacle detection, collision prediction, and collision avoidance. A detailed overview and comparison of the methods of each element and an analysis of their advantages and disadvantages have been provided. In addition, the future trends of UAV anti-collision technologies from the viewpoint of fast obstacle detection and wireless networking are presented.
Interfacial dynamics of drop coalescence and impinging liquid jets: Effect of viscous, Marangoni and shear-thinning stresses

Jiakai Lu Ph.D., Purdue University, December 2014. Interfacial dynamics of drop coalescence and impinging liquid jets: e↵ect of viscous, Marangoni and shear-thinning stresses. Major Professors: Carlos M. Corvalan Associate Professor, Department of Food Science and Paul E. Sojka Professor, School of Mechanical Engineering. The free-surface dynamics of drop coalescence and that of impinging liquid jets have both fundamental interest and practical importance in processes ranging from crop spraying and the processing of food emulsions to the atomization of fuel and propellants in combustion and propulsion engines. These processes frequently involve the use of complex fluids — shear-thinning or viscoelastic non-Newtonian liquids, which often contain surfactants either as additives or as contaminants. This thesis reports high-fidelity simulations of unsteady free-surface flows of complex fluids. The objective is to advance the understanding of the free-surface dynamics of drop coalescence and liquid jet impingement when viscous, surfactant and shear-thinning e↵ects are important. Simulations in this thesis enabled, for the first time, a comprehensive numerical analysis of the coalescence of surfactant-laden drops after the merging drops make contact. The analysis reveals how interfacial (Marangoni) stresses induced by uneven accumulation of surfactant control the rate at which the drops coalesce by modulating the pull of surface tension on the tiny meniscus bridge joining the drops. Simulations also enabled the analysis of the unsteady free-surface dynamics of impinging viscous and shear-thinning liquid jets. Results demonstrate that viscous and shear-thinning stresses profoundly a↵ects the impingement dynamics — in particular the velocity and thickness of the resulting radially expanding liquid sheet — by modifying the pressure developed in the impact region.
The Effect of Acute Exercise on State Anxiety: A Systematic Review

Acute exercise has been shown to induce a small reduction in state anxiety, yet the most beneficial exercise stimulus is not clear. This review provides an update on the papers published since the last comprehensive review in 2015, with specific emphasis on whether study quality has improved. Randomised control trials, conducted in samples of healthy adults with non-clinical anxiety, were sourced from PubMed, PsycInfo, and Scopus. Study characteristics and study quality were assessed in nine studies comprising thirteen exercise conditions. Acute exercise significantly reduced anxiety in 53% (N = 7/13) of the exercise conditions. In comparison to a control condition, four showed exercising to be more effective, and one was as effective as the control. Two of the effective studies did not contain a control group. Six conditions were ineffective in reducing anxiety. There was no clear pattern of what combination of exercise mode, duration, and intensity was most effective, suggesting a variety may be effective in reducing anxiety. Methodological limitations still exist within the research, e.g., participant recruitment not considering baseline anxiety; variations in the control condition content. Future research should include participant samples exhibiting moderate-to-high levels of anxiety and examine self-selected exercise intensities.
Blood flow in mildly stenosed aortoiliac bifurcation: A numerical study

Cardiovascular diseases (CVD) are the leading cause of mortality and morbidity. Atherosclerosis is a major CVD resulting in stenosis development in the arterial lumen. Computational Fluid Dynamics (CFD) techniques provide a better understanding of the causes of the disease and its early diagnosis. The present study aims at analysing the blood flow in a mildly stenosed aortoiliac bifurcation using the finite volume-based software ANSYS. It is inferred that even a mild stenosis in the arterial bifurcation also affects the flow of blood and hemodynamic parameters like pressure and velocity
Implementation of Machine Learning and Deep Learning Techniques for the Detection of Epileptic Seizures Using Intracranial Electroencephalography

The diagnosis of epilepsy primarily relies on the visual and subjective assessment of the patient’s electroencephalographic (EEG) or intracranial electroencephalographic (iEEG) signals. Neurophysiologists, based on their experience, look for characteristic discharges such as spikes and multi-spikes. One of the main challenges in epilepsy research is developing an automated system capable of detecting epileptic seizures with high sensitivity and precision. Moreover, there is an ongoing search for universal features in iEEG signals that can be easily interpreted by neurophysiologists. This article explores the possibilities, issues, and challenges associated with utilizing artificial intelligence for seizure detection using the publicly available iEEG database. The study presents standard approaches for analyzing iEEG signals, including chaos theory, energy in different frequency bands (alpha, beta, gamma, theta, and delta), wavelet transform, empirical mode decomposition, and machine learning techniques such as support vector machines. It also discusses modern deep learning algorithms such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our goal was to gather and comprehensively compare various artificial intelligence techniques, including both traditional machine learning methods and deep learning techniques, which are most commonly used in the field of seizure detection. Detection results were tested on a separate dataset, demonstrating classification accuracy, sensitivity, precision, and specificity of seizure detection. The best results for seizure detection were obtained with features related to iEEG signal energy (accuracy of 0.97, precision of 0.96, sensitivity of 0.99, and specificity of 0.96), as well as features related to chaos, Lyapunov exponents, and fractal dimension (accuracy, precision, sensitivity, and specificity all equal to 0.95). The application of CNN and LSTM networks yielded significantly better results (CNN: Accuracy of 0.99, precision of 0.98, sensitivity of 1, and specificity of 0.99; LSTM: Accuracy of 0.98, precision of 0.96, sensitivity of 1, and specificity of 0.99). Additionally, the use of the gradient-weighted class activation mapping algorithm identified iEEG signal fragments that played a significant role in seizure detection.
Using Recurrence Relation to Count a Number of Perfect Matching in Linear Chain and Snake Chain Graphs

This paper presents the recurrence relation used to count a number of perfect matchings in linear chain and snake chain graphs. These graphs are offen found in the chemical structure. A matching graph M is a subgraph of a graph G where there are no edges adjacent to each other. If V(M)=V(G), we call M a “perfect matching”. phi(G) is a number of perfect matching of G which leads to important chemical properties. The results show that a number of perfect matching of a linear chain graph depends on parity of faces and number of edges in each face. A number of perfect matching of a snake chain graph depends on parity of the chain.