Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbial Phagocytic Receptors and Their Potential Involvement in Cytokine Induction in Macrophages

Phagocytosis is an essential process for the uptake of large (>0.5 µm) particulate matter including microbes and dying cells. Specialized cells in the body perform phagocytosis which is enabled by cell surface receptors that recognize and bind target cells. Professional phagocytes play a prominent role in innate immunity and include macrophages, neutrophils and dendritic cells. These cells display a repertoire of phagocytic receptors that engage the target cells directly, or indirectly via opsonins, to mediate binding and internalization of the target into a phagosome. Phagosome maturation then proceeds to cause destruction and recycling of the phagosome contents. Key subsequent events include antigen presentation and cytokine production to alert and recruit cells involved in the adaptive immune response. Bridging the innate and adaptive immunity, macrophages secrete a broad selection of inflammatory mediators to orchestrate the type and magnitude of an inflammatory response. This review will focus on cytokines produced by NF-κB signaling which is activated by extracellular ligands and serves a master regulator of the inflammatory response to microbes. Macrophages secrete pro-inflammatory cytokines including TNFα, IL1β, IL6, IL8 and IL12 which together increases vascular permeability and promotes recruitment of other immune cells. The major anti-inflammatory cytokines produced by macrophages include IL10 and TGFβ which act to suppress inflammatory gene expression in macrophages and other immune cells. Typically, macrophage cytokines are synthesized, trafficked intracellularly and released in response to activation of pattern recognition receptors (PRRs) or inflammasomes. Direct evidence linking the event of phagocytosis to cytokine production in macrophages is lacking. This review will focus on cytokine output after engagement of macrophage phagocytic receptors by particulate microbial targets. Microbial receptors include the PRRs: Toll-like receptors (TLRs), scavenger receptors (SRs), C-type lectin and the opsonic receptors. Our current understanding of how macrophage receptor stimulation impacts cytokine production is largely based on work utilizing soluble ligands that are destined for endocytosis. We will instead focus this review on research examining receptor ligation during uptake of particulate microbes and how this complex internalization process may influence inflammatory cytokine production in macrophages.
Deep Learning and its Application for Healthcare Delivery in Low and Middle Income Countries

As anyone who has witnessed firsthand knows, healthcare delivery in low-resource settings is fundamentally different from more affluent settings. Artificial Intelligence, including Machine Learning and more specifically Deep Learning, has made amazing advances over the past decade. Significant resources are now dedicated to problems in the field of medicine, but with the potential to further the digital divide by neglecting underserved areas and their specific context. In the general case, Deep Learning remains a complex technology requiring deep technical expertise. This paper explores advances within the narrower field of deep learning image analysis that reduces barriers to adoption and allows individuals with less specialized software skills to effectively employ these techniques. This enables a next wave of innovation, driven largely by problem domain expertise and the creative application of this technology to unaddressed concerns in LMIC settings. The paper also explores the central role of NGOs in problem identification, data acquisition and curation, and integration of new technologies into healthcare systems.
The Impact of Social Behavior and Peers’ Attitudes Toward Students With Special Educational Needs on Self-Reported Peer Interactions

According to the literature, social participation (e.g., peer interactions) of students diagnosed with special educational needs (SEN) has to be focused upon as they are at a higher risk of being socially excluded compared to students without SEN. Research has pointed out that social participation of students with SEN is influenced by their own social behavior as well as the attitudes of peers with no SEN toward them. The present study assessed the impact of the social behavior of students diagnosed with SEN (n = 88; 48 boys and 40 girls) as well as that of the attitudes of their peers without SEN (n = 227; 139 boys and 153 girls) toward them on the social participation. Results indicated that students without SEN were less likely to interact with their classmates with SEN. Peer interactions of students with SEN were not significantly influenced by their own social behavior.
Veliparib Is an Effective Radiosensitizing Agent in a Preclinical Model of Medulloblastoma

Medulloblastoma is the most common malignant childhood brain tumor, and 5-year overall survival rates are as low as 40% depending on molecular subtype, with new therapies critically important. As radiotherapy and chemotherapy act through the induction of DNA damage, the sensitization of cancer cells through the inhibition of DNA damage repair pathways is a potential therapeutic strategy. The poly-(ADP-ribose) polymerase (PARP) inhibitor veliparib was assessed for its ability to augment the cellular response to radiation-induced DNA damage in human medulloblastoma cells. DNA repair following irradiation was assessed using the alkaline comet assay, with veliparib inhibiting the rate of DNA repair. Veliparib treatment also increased the number of γH2AX foci in cells treated with radiation, and analysis of downstream pathways indicated persistent activation of the DNA damage response pathway. Clonogenicity assays demonstrated that veliparib effectively inhibited the colony-forming capacity of medulloblastoma cells, both as a single agent and in combination with irradiation. These data were then validated in vivo using an orthotopic implant model of medulloblastoma. Mice harboring intracranial D425 medulloblastoma xenografts were treated with vehicle, veliparib, 18 Gy multifractionated craniospinal irradiation (CSI), or veliparib combined with 18 Gy CSI. Animals treated with combination therapy exhibited reduced tumor growth rates concomitant with increased intra-tumoral apoptosis observed by immunohistochemistry. Kaplan–Meier analyses revealed a statistically significant increase in survival with combination therapy compared to CSI alone. In summary, PARP inhibition enhanced radiation-induced cytotoxicity of medulloblastoma cells; thus, veliparib or other brain-penetrant PARP inhibitors are potential radiosensitizing agents for the treatment of medulloblastoma.
Late Cenozoic Denudation and Topographic Evolution History of the Lhasa River Drainage in Southern Tibetan Plateau: Insights From Inverse Thermal History Modeling

The interaction of surface erosion (e.g., fluvial incision) and tectonic uplift shapes the landform in the Tibetan Plateau. The Lhasa River flows toward the southwest across the central Gangdese Mountains in the southern Tibetan Plateau, characterized by a low-relief and high-elevation landscape. However, the evolution of low-relief topography and the establishment of the Lhasa River remain highly under debate. Here, we collected thermochronological ages reported in the Lhasa River drainage, using a 3D thermokinematic model to invert both late Cenozoic denudation and relief history of the Lhasa River drainage. Our results show that the Lhasa River drainage underwent four-phase denudation history, including two-stage rapid denudation at ∼25–16 Ma (with a rate of ∼0.42 km/Ma) and ∼16–12 Ma (with a rate of ∼0.72 km/Ma). In the latest Oligocene–early Miocene, uplift of the Gangdese Mountains triggered the rapid denudation and the formation of the current main drainage of the Lhasa River. In the middle Miocene, the second stage of the rapid denudation and the high relief were associated with intense incision of the Lhasa River, which is probably due to the enhanced Asian summer monsoon precipitation. This later rapid episode was consistent with the records of regional main drainage systems. After ∼12 Ma, the denudation rate decreases rapidly, and the relief of topography in the central Gangdese region was gradually subdued. This indicates that the fluvial erosion resulting from Asian monsoon precipitation increase significantly impacts on the topographic evolution in the central Gangdese region.
The Detection of Cancer Epigenetic Traces in Cell-Free DNA

Nucleic acid fragments found in blood circulation originate mostly from dying cells and carry signs pointing to specific features of the parental cell types. Deciphering these clues may be transformative for numerous research and clinical applications but strongly depends on the development and implementation of robust analytical methods. Remarkable progress has been achieved in the reliable detection of sequence alterations in cell-free DNA while decoding epigenetic information from methylation and fragmentation patterns requires more sophisticated approaches. This review discusses the currently available strategies for detecting and analyzing the epigenetic marks in the liquid biopsies.
State-of-the-art Geoinformation Technologies Use in the Road Traffic Management

New opportunities in the various processes’ management in the frame of the last decades have formed on the base of information technologies development, their widespread use in various spheres of human activity. Road traffic management is one of the fields where information technology using is necessary and demanding. IT technologies are widely used to solve the transport problem, but for the traffic organization, which includes not only the transport network, but also all traffic participants, their use is not so widespread, but necessary. In Kyiv, the capital of Ukraine, a project has been being implemented an automated road traffic management system creation during the past years. The modern approaches for data working (including technologies for their "production") and geoinformation technologies are lain on the base of the system. The solutions complex that this system provides allows analyzing the existing road traffic management with digital cartography and spatial analysis methods using. However, the successful implementation of any information project requires not only the problem formulation itself but also its support at various levels, i.e. various stakeholders. In the article, the authors analyze the project stakeholders, classify them based on the impact on the project and the interaction organization. They also describe the main approaches to the geographic information system design, analyze the external environment of the project, and, as a result of the analysis, describe the functionality and components of this system.
OpenCL and CUDA Comparison of MapReduce Performance on Distributed Heterogeneous Platform through Integration with Hadoop Cluster

An effective processing of Big Data in various application areas is an important task today. Modern development of information technology provides the ability to calculate a large number of different tasks using a certain number of computers in distributed mode. MapReduce technology allows to perform distributed calculations on a huge amount of data by dividing them into parts, performing parallel calculations of each of them and combining the results. In this paper, experimental studies were performed to compare CUDA and OpenGL frameworks performance measurements for MapReduce operations on heterogeneous cluster. It has been found that CUDA is a more suitable framework that provide a significant advantage in this regard. It is determined that the greater the amount of processing data, the greater the delay caused by OpenCL. Further research will be conducted to determine the energy consumption of both technologies.
Effectiveness of a WebGIS-based project on high school students' spatial thinking skills

This study aims at developing a learning environment for school education based on the Web-GIS Technology using the Project-Based Learning (PBL) methodology. In this framework, 3 projects were designed and implemented with Project 1 and 2 combining and incorporating fieldwork with GIS while pertaining to local community problems, and Project 3 being conducted indoors, in the computer lab only with web-based data. With the view to exploring the effectiveness of such an environment on students’ Spatial Thinking Skills on qualitative basis, a quasi-experimental research on a sample of 58 students, 10 th , and 11 th grade of High School, was carried out. Two research groups were created, the first one for the realization of Project 1 και 2 (GIS1) and the second for realization of Project 3 (GIS2). A pre-test was conducted in advance and a post-test upon concluding all three Projects. The study found significant improvement in students’ Spatial Thinking Skills. There is no obvious differentiation between genders while Spatial Thinking Skills are further boosted and enhanced when field work is applied in order to address local community issues.
Scientific Ontologies, Digital Curation and the Learning Knowledge Ecosystem

The global coronavirus pandemic has brought another ongoing crisis into the spotlight: that of digital misinformation. While society at a global scale is facing challenges that demand scientiﬁc solutions as never before, trust in experts and scientiﬁc expertise is falling, and conspiracy theories abound. At the same time, science itself is not without challenges, such as the reproducibility crisis across multiple domains. A contributing factor to misinformation is the way that scientiﬁc research is undertaken and re-ported in isolated and conﬂicting units, rather than as a holistic aggregate of information. In this position paper, I will argue that scientiﬁc ontologies and digital curation will be essential tools for transforming how scientiﬁc research is conducted and reported to address the problem of misinformation.
Air Pollution Prediction as a Source for Decision Making Framework in Medical Diagnosis

The problem of air pollution prediction is presented in the paper. It is considered regarding complex problem of creation of decision making framework in medical diagnosis. Therefore prediction is performed for a day, not for on an hour. The method of air pollution prediction is developed using Long Short Term Memory (LSTM) recurrent neural network. The LSTM-based model is used for prediction of concentration of separate air pollutant during the next day based on its concentration during the previous hours and average traffic data. The experimental investigation of the proposed method is performed by comparing it with ARIMA model, multilayer perceptron, vanilla recurrent neural networks and LSTM. The proposed method should be used in practice inside medical diagnosis tools and separate systems for air pollution analysis, enabling to obtain predicted air pollutant concentration level during the next day.
First-Principles Study on Hydrogen Annealing Effect in Si/SiO2 Interface by Thermal Oxidation

Why is high-quality Si/SiO2 interface readily fabricated by simple thermal oxidation? Even though the question is closely related to the great success of silicon technology, the answer has not yet been clarified at the atomic level. In this study, we investigated hydrogen annealing effect in thermally-oxidized Si/SiO2 interface by the first-principles calculation method to reply the question. According to the Si-emission model of thermal oxidation, Si atoms are spontaneously emitted from the interface, leading to the lattice distortion and dangling bonds at the vacancies. We clarified that the interface states originating from the dangling bonds can be passivated by hydrogen terminations of Si atoms with dangling bonds. This means that the interface-state density can be decreased by hydrogen annealing after thermal oxidation. Our results rationalize the reason why the quality of Si/SiO2 interface formed by thermal oxidation is higher than that by the other technique such as CVD.
Snow on Polar Radomes and Performance Deterioration at 26 GHz for a Receiving Ground Station: the SNOWBEAR Project

In the near future, many forthcoming Earth Observation missions operating in a Polar orbit, will rely on a new high data-rate downlink channel, defined in the frequency band from 25.5 to 27 GHZ. This band was made available to download the ever growing amount of scientific data, collected during satellite operations and stored on-board, throughout the short time in which it is visible from a ground station. For Earth Observation missions, the receiving station on Earth is naturally placed at Polar latitudes, to maximise the number of visible passes. However, the use of such frequencies for this kind of communication link is still on an early development stage. In particular, the precise link budget modelling and experimental performance validation are still open points of discussion and research. This is especially true for the effects caused by the presence of the snow on the radome, mandatory to protect the antenna from strong wind gusts. This paper presents a general overview of a dedicated project (SNOWBEAR), developed under European Space Agency coordination, for which the performance degradation due to the accumulation of snow on the radome top is addressed. AS a preliminary result, two major effects on the signal disruption have been identified, snow absorption and de-pointing.
CRC-Based Error Detection Constructions for FLT and ITA Finite Field Inversions Over GF(2m)

Binary extension finite fields <inline-formula> <tex-math notation="LaTeX">${\mathrm{ GF}}(2^{m})$ </tex-math></inline-formula> have received prominent attention in the literature due to their application in many modern public-key cryptosystems and error-correcting codes. In particular, the inversion over <inline-formula> <tex-math notation="LaTeX">${\mathrm{ GF}}(2^{m})$ </tex-math></inline-formula> is crucial for current and postquantum cryptographic applications. Schemes such as Fermat’s little theorem (FLT) and the Itoh–Tsujii algorithm (ITA) have been studied to achieve better performance; however, this arithmetic operation is a complex, expensive, and time-consuming task that may require thousands of gates, increasing its vulnerability chance to natural defects. In this work, we propose efficient hardware architectures based on cyclic redundancy check (CRC) as error detection schemes for state-of-the-art finite field inversion over <inline-formula> <tex-math notation="LaTeX">${\mathrm{ GF}}(2^{m})$ </tex-math></inline-formula> for a polynomial basis. To verify the derivations of the formulations, software implementations are performed. Likewise, hardware implementations of the original finite field inversions with the proposed error detection schemes are performed over Xilinx field-programmable gate array (FPGA) verifying that the proposed schemes achieve high error coverage with acceptable overhead.
Deep Multi-User Reinforcement Learning for Centralized Dynamic Multichannel Access

We consider the problem of dynamic multichannel access for transmission maximization in multi-user wireless communication networks. At the beginning of each time slot, the centralized node evaluates all K channels and allocate a channel to transmit a packet for each user. After each time slot, the centralized node receives feedback signals for each user indicating whether the packet was successfully delivered or not. The objective is to find a multi-user strategy that maximizes global channel utilization with a low collision in a centralized manner without any prior knowledge. Obtaining an optimal solution for centralized dynamic multichannel access problem is a difficult problem due to the large-state and large-action space. To tackle this problem, we develop a centralized dynamic multichannel access framework based on double deep recurrent reinforcement learning. Specifically, at each time slot, the centralized node map current state to channel access actions and select multiple channels where the sum of corresponding value function obtains its maximum value based on neural network. The proposed method decreases collision through centralized allocation policy to achieve an optimal multi-user channel allocation.
An Intelligent Traffic Light Control System Based on Dual Mode Special Vehicle Identification

Smart transportation is a major aspect of smart city which aims at improving traffic operation level, dispatching and overall supervision. How to alleviate traffic congestion, which is particular severe in large cities, is a cardinal facet of transportation. Special vehicles refer to vehicles dedicated to special tasks, like police cars, ambulances and are very dependent on road patency. It could be a life matter if a special vehicle on a mission encounters traffic congestion. Most of the existing traffic control systems do not target on special vehicles. Existing traffic control systems for special vehicle have weaknesses like unable to precisely detect the condition of special vehicles. In this paper, we proposed a traffic light control system based on dual mode identification, which aims at providing priority for special vehicles in a crowded city. By using dual mode identification, the condition of vehicles can be more precisely defined and accuracy performance is improved. In addition, we applied control and compensate time for the intersection to prioritize special vehicles and restore original state. In the system simulation and evaluation results, the proposed traffic light control system achieves 29% of time saving for special vehicles to get through an intersection and the intersection requires 4 cycles to fully recuperate to original condition.
Supporting Irregularity in Throughput-Oriented Computing by SIMT-SIMD Integration

The last two decades have seen continued exponential performance increases in HPC systems, well after the predicted end of Moore's Law for CPUs, largely due to the widespread adoption of throughput-oriented compute accelerators such as GPUs. When faced with irregular yet throughput-oriented applications, their simple, grid-based computing model turns into a serious limitation. Instead of repeatedly tackling the issues of irregularity on the application layer, we argue that a generalization of the CUDA model to irregular grids can be supported through minor modifications to already established throughput-oriented architectures. To that end, we propose a unifying approach that combines techniques from both SIMD and MIMD approaches, but adheres to the SIMT principles - based on an unlikely ally: a wide-SIMD vector architecture. We extend CUDA's familiar programming model and implement SIMT-inspired strategies for dealing with data and control flow irregularities. Our approach requires only minimal hardware changes and an additional compiler phase. Using a model-based software simulation, we demonstrate that the proposed system can be a first step towards native support for irregularity on throughput-oriented processors while greatly simplifying the development of irregular applications.
Design of Triangular-Latticed Sub-array Antenna Fed by Hexagonal Radial Line for K-band Applications

A circularly polarized double-stacked patch subarray hexagonal antenna fed by a radial line with internal circular coupling patches is proposed. The antenna works in K band (19.7 GHz – 20.2 GHz) with LHCP. The design approach consists of a coupling study based on the simulation of a periodic parallel-plate waveguide as a first approximation of the radial line. A phase compensation method by rotation is applied to adjust the radiating phase of each element of the subarray, which are separated 0. 7λ0. The subarray is uniformly fed in terms of amplitude and phase. An axial ratio below 1 dB is achieved for the entire frequency band, with a gain of 24 dB and a total efficiency of 87%.