Miracle-dz/UniFAR-FTUs huggingface dataset:
Epstein-Barr Virus MicroRNA miR-BART5-3p Inhibits p53 Expression

EBV encodes 44 mature microRNAs, which have been proven to promote EBV-associated diseases by targeting host genes and self-viral genes. In EBV-associated carcinomas, the expression of viral protein is limited but the expression of BART microRNAs is extremely high, suggesting that they could be major factors in the contribution of EBV-associated tumorigenesis. p53 is a critical tumor suppressor. Unlike in most human solid tumors, TP53 mutations are rare in nasopharyngeal carcinoma and EBV-associated gastric carcinoma tissues, suggesting a possibility that some EBV-encoded products suppress the functions of p53. This study provides the first evidence that a BART microRNA can suppress p53 expression by directly targeting its 3′-UTR. This study implies that EBV can use its BART microRNAs to modulate the expression of p53, thus maintaining its latency and contributing to tumorigenesis. ABSTRACT Epstein-Barr virus (EBV) is the first human virus found to encode many microRNAs. It is etiologically linked to nasopharyngeal carcinoma and EBV-associated gastric carcinoma. During the latent infection period, there are only a few EBV proteins expressed, whereas EBV microRNAs, such as the BamHI-A region rightward transcript (BART) microRNAs, are highly expressed. However, how these BART miRNAs precisely regulate the tumor growth in nasopharyngeal carcinoma and gastric carcinoma remains obscure. Here, we report that upregulation of EBV-miR-BART5-3p promotes the growth of nasopharyngeal carcinoma and gastric carcinoma cells. BART5-3p directly targets the tumor suppressor gene TP53 on its 3′-untranslated region (3′-UTR) and consequently downregulates CDKN1A, BAX, and FAS expression, leading to acceleration of the cell cycle progress and inhibition of cell apoptosis. BART5-3p contributes to the resistance to chemotherapeutic drugs and ionizing irradiation-induced p53 increase. Moreover, BART5-3p also facilitates degradation of p53 proteins. BART5-3p is the first EBV-microRNA to be identified as inhibiting p53 expression and function, which suggests a novel mechanism underlying the strategies employed by EBV to maintain latent infection and promote the development of EBV-associated carcinomas. IMPORTANCE EBV encodes 44 mature microRNAs, which have been proven to promote EBV-associated diseases by targeting host genes and self-viral genes. In EBV-associated carcinomas, the expression of viral protein is limited but the expression of BART microRNAs is extremely high, suggesting that they could be major factors in the contribution of EBV-associated tumorigenesis. p53 is a critical tumor suppressor. Unlike in most human solid tumors, TP53 mutations are rare in nasopharyngeal carcinoma and EBV-associated gastric carcinoma tissues, suggesting a possibility that some EBV-encoded products suppress the functions of p53. This study provides the first evidence that a BART microRNA can suppress p53 expression by directly targeting its 3′-UTR. This study implies that EBV can use its BART microRNAs to modulate the expression of p53, thus maintaining its latency and contributing to tumorigenesis.
Confirmation of the Role of DHX38 in the Etiology of Early-Onset Retinitis Pigmentosa

Purpose Retinitis pigmentosa (RP) is a genetically heterogeneous trait with autosomal-recessive (ar) inheritance underlying 50% of genetic disease cases. Sixty-one arRP genes have been identified, and recently, DHX38 has been reported as a potential candidate gene for arRP with only a single family reported with a variant of unknown significance. We identified a missense variant in DHX38 that co-segregates with the arRP phenotype in two Pakistani families confirming the involvement of DHX38 in the etiology of early-onset RP. Methods Exome sequencing was performed using two DNA samples from affected members of Pakistani families (MA88 and MA157) with early onset arRP. Sanger sequencing of DNA samples from all family members confirmed the segregation of candidate variant within both families. Results A novel missense DHX38 variant c.971G>A; p.(Arg324Gln) was identified which segregates with the arRP phenotype and yielded a logarithm of the odds (LOD) score of 5.0 and 4.3 for families MA88 and MA157, respectively. This variant is predicted to be conserved and deleterious by several bioinformatics tools. Conclusions We identified a second deleterious DHX38 variant that segregates with arRP in two families, providing additional evidence that DHX38 is involved in RP etiology. DHX38 encodes for pre-mRNA splicing factor PRP16, which is important in catalyzing pre-mRNA splicing.
A Human–Robot-Environment Interactive Reasoning Mechanism for Object Sorting Robot

In this paper, we design an object sorting robot system, which is based on robot operating system distributed processing framework. This system can communicate with human beings; can percept the 3-D environment by Kinect sensor; has the ability of reasoning; can transfer the natural language intention to machine instruction to control the movement of manipulator. In particular, in order to improve the intelligence and usability of our robot, we propose a human–robot-environment interactive reasoning mechanism. In our method, a “dialogue and 3-D scene interaction module” is added into the traditional case-based reasoning–belief-desire-intention mechanism. Our proposed mechanism not only realizes the traditional function of map matching but also achieves the function of desire analysis and guidance. When the user’s desire is incomplete and/or mismatched with the actual scene, our robot will take the initiative to guide users through dialogue, and the user’s input information will be used to replenish the user’s desire. Experimental results prove the advantages of our mechanism.
Green NOMA With Multiple Interference Cancellation (MIC) Using Sector-Based Resource Allocation

This paper considers a novel non-orthogonal multiple access (NOMA) enabled device-to-device (D2D) communication fifth generation cellular network, practicing sectorization. Though successive interference cancellation (SIC) is the key aspect of NOMA in the current wireless communication systems, the proposed NOMA-based approach introduces the concept of multiple interference cancellation (MIC), which stimulates the phenomenon of cancellation of interference levels in the network, involving optimal resource allocation. The interference reduction achieved with MIC is way below SIC. Such a framework improves the receiver’s signal-to-interference-plus-noise ratio (SINR), as shall be examined. Promising gains are provided by the proposed scheme, in terms of sum rate of the network, total energy-efficiency (EE), and fairness factor (FF). MIC reduces the power levels at the regenerators, achieving considerable circuit power saving. Another important redressal of the proposed scheme is the reduced level of complexity achieved with MIC. The implications of proposing MIC in the NOMA-D2D networks are evaluated through simulation results.
Claudin-4 reconstituted in unilamellar vesicles is sufficient to form tight interfaces that partition membrane proteins

ABSTRACT Tight junctions have been hypothesized to act as molecular fences in the plasma membrane of epithelial cells, helping to form differentiated apical and basolateral domains. While this fence function is believed to arise from the interaction of four-pass transmembrane claudins, the complexity of tight junctions has made direct evidence of their role as a putative diffusion barrier difficult to obtain. Here, we address this challenge by reconstituting claudin-4 into giant unilamellar vesicles using microfluidic jetting. We find that reconstituted claudin-4 alone can form adhesive membrane interfaces without the accessory proteins that are present in vivo. By controlling the molecular composition of the inner and outer leaflets of jetted vesicle membranes, we show that claudin-4-mediated interfaces can drive partitioning of extracellular membrane proteins with ectodomains as small as 5 nm but not of inner or outer leaflet lipids. Our findings indicate that homotypic interactions of claudins and their small size can contribute to the polarization of epithelial cells. Summary: Successful reconstitution of the tight junction protein claudin-4 drove interface formation between giant unilamellar vesicles. These interfaces were sufficient to act as molecular fences toward proteins but not lipids.
Coordinated Heterogeneous Distributed Perception based on Latent Space Representation

We investigate a reinforcement approach for distributed sensing based on the latent space derived from multi-modal deep generative models. Our contribution provides insights to the following benefits: Detections can be exchanged effectively between robots equipped with uni-modal sensors due to a shared latent representation of information that is trained by a Variational Auto Encoder (VAE). Sensor-fusion can be applied asynchronously due to the generative feature of the VAE. Deep Q-Networks (DQNs) are trained to minimize uncertainty in latent space by coordinating robots to a Point-of-Interest (PoI) where their sensor modality can provide beneficial information about the PoI. Additionally, we show that the decrease in uncertainty can be defined as the direct reward signal for training the DQN.
Method for choosing the variable of integration on numerical simulation of ray paths in inhomogeneous media

An algorithm for solving the Cauchy problem by a numerical method by integrating with the selection of the variable of integration (the variable with respect to which the integration is performed) at each step. The application of the numerical simulation algorithm to the calculation of ray paths in inhomogeneous media in the geometrical optics approximation is presented. The testing and estimation of the accuracy of the algorithm are carried out using the example of a problem with a known analytical solution.
A variant of the Schnorr signature using an elliptic curve over a field of characteristic two

Digital signature over elliptic curve is one of the most important applications of security because it is effective. Recently, it has been developed and defined in the various standard of security. The application of the digital signature are signer authentication, data integrity, and non-repudiation. Currently, the requirements to implement authentication process on a computer hardware with limited resource such as energy, memory and computing power are increasing. The developer should consider these factors along with security factor for the effective implement on the computer hardware with limited resource. In this paper, we propose the Schnorr signature scheme using Koblitz curve over a field of characteristic two. The advantage of Schnorr signature scheme is a good combination with Koblitz curve over a field of characteristic two, therefore its arithmetic can be performed in any computer. Moreover, we use Double-and-Add scalar multiplication to reduce time in the process of systems. In addition, this paper shows a result of time in the process of the system to compare the performance of the Schnorr signature scheme on Koblitz curve using Double-andAdd scalar multiplication with the Schnorr signature scheme on Koblitz curve using typical scalar multiplication. The result of this study is that both systems working correctly. However, the Schnorr signature scheme on Koblitz curve using Double-andAdd performs better in time efficiency than of Schnorr signature scheme on Koblitz curve using typical scalar multiplication.
Convolutional Neural Networks for Fast Approximation of Graph Edit Distance

Graph Edit Distance (GED) computation is a core operation of many widely-used graph applications, such as graph classification, graph matching, and graph similarity search. However, computing the exact GED between two graphs is NP-complete. Most current approximate algorithms are based on solving a combinatorial optimization problem, which involves complicated design and high time complexity. In this paper, we propose a novel end-to-end neural network based approach to GED approximation, aiming to alleviate the computational burden while preserving good performance. The proposed approach, named GSimCNN, turns GED computation into a learning problem. Each graph is considered as a set of nodes, represented by learnable embedding vectors. The GED computation is then considered as a two-set matching problem, where a higher matching score leads to a lower GED. A Convolutional Neural Network (CNN) based approach is proposed to tackle the set matching problem. We test our algorithm on three real graph datasets, and our model achieves significant performance enhancement against state-of-the-art approximate GED computation algorithms.
Estimating Cloud Application Performance Based on Micro-Benchmark Profiling

The continuing growth of the cloud computing market has led to an unprecedented diversity of cloud services. To support service selection, micro-benchmarks are commonly used to identify the best performing cloud service. However, it remains unclear how relevant these synthetic micro-benchmarks are for gaining insights into the performance of real-world applications. Therefore, this paper develops a cloud benchmarking methodology that uses micro-benchmarks to profile applications and subsequently predicts how an application performs on a wide range of cloud services. A study with a real cloud provider (Amazon EC2) has been conducted to quantitatively evaluate the estimation model with 38 metrics from 23 micro-benchmarks and 2 applications from different domains. The results reveal remarkably low variability in cloud service performance and show that selected micro-benchmarks can estimate the duration of a scientific computing application with a relative error of less than 10% and the response time of a Web serving application with a relative error between 10% and 20%. In conclusion, this paper emphasizes the importance of cloud benchmarking by substantiating the suitability of micro-benchmarks for estimating application performance in comparison to common baselines but also highlights that only selected micro-benchmarks are relevant to estimate the performance of a particular application.
Pengaruh Media Sosial sebagai Sumber Belajar IPS Terhadap Motivasi Belajar, Kemampuan Berpikir Kritis dan Berpikir Kreatif Siswa Sekolah Dasar

Penelitian ini dilatarbelakangi hasil pengamatan dan pengalaman peneliti, bahwa pendidikan di sekolah dasar lebih cenderung menitikberatkan pada pembelajaran konvensional, sehingga siswa kurang terlibat aktif berpikir secara kritis dan kreatif dalam proses pembelajaran disekolah. Hal ini mengakibatkan motivasi belajar siswa dalam pembelajaran kurang. Permasalahan penelitian ini adalah pengaruh penggunaan media sosial sebagai sumber pembelajaran IPS terhadap kemampuan berpikir kritis, berpikir kreatif dan motivasi belajar siswa sekolah dasar. Penelitian ini menggunakan pendekatan kuantitatif dengan subyek penelitian siswa kelas V. Jenis desain penelitian ada lah non-equivalent control group design pada siswa kelas V Sekolah Dasar di Kota Kediri. Digunakan beberapa instrumen untuk mendukung penelitian ini yaitu Pengukuran motivasi belajar siswa menggunakan angket motivasi belajar, di analisis menggunakan analisis deskriptif. Pengukuran kemampuan berpikir kritis menggunakan rubrik kemampuan berpikir kritis terintegrasi dengan tes kognitif, dan pengukuran berpikir kreatif terintegrasi pada tes hasil belajar kognitif. Data ditabulasi kemudian dianalisis. Penelitian ini menunjukkan bahwa: (1) terdapat perbedaan motivasi belajar siswa dari kelompok kontrol dengan kelompok perlakuan. (2) ada perbedaan kemampuan berpikir kritis kelompok kontrol dengan perlakuan, (α = 0,057>0,05). (3) ada perbedaan berpikir kreatif antara kelompok kontrol dan perlakuan (α= 0,055>0,05). Disimpulkan bahwa media sosial sebagai sumber belajar IPS dapat meningkatkan motivasi belajar, kemampuan berpikir kritis, kemampuan berpikir kreatif.
Empowering First Responders through Automated Multimodal Content Moderation

Social media enables users to spread information and opinions, including in times of crisis events such as riots, protests or uprisings. Sensitive event-related content can lead to repercussions in the real world. Therefore it is crucial for first responders, such as law enforcement agencies, to have ready access, and the ability to monitor the propagation of such content. Obstacles to easy access include a lack of automatic moderation tools targeted for first responders. Efforts are further complicated by the multimodal nature of content which may have either textual and pictorial aspects. In this work, as a means of providing intelligence to first responders, we investigate automatic moderation of sensitive event-related content across the two modalities by exploiting recent advances in Deep Neural Networks (DNN). We use a combination of image classification with Convolutional Neural Networks (CNN) and text classification with Recurrent Neural Networks (RNN). Our multilevel content classifier is obtained by fusing the image classifier and the text classifier. We utilize feature engineering for preprocessing but bypass it during classification due to our use of DNNs while achieving coverage by leveraging community guidelines. Our approach maintains a low false positive rate and high precision by learning from a weakly labeled dataset and then, by learning from an expert annotated dataset. We evaluate our system both quantitatively and qualitatively to gain a deeper understanding of its functioning. Finally, we benchmark our technique with current approaches to combating sensitive content and find that our system outperforms by 16% in accuracy.
Code-Based Authentication Scheme for Lightweight Integrity Checking of Smart Vehicles

The stable and seamless connection of the mobile communication system is expected to closely link an unprecedented number of things, including smart cars such as autonomous vehicles, in the near future. These vehicles will operate based on the data transmitted over hundreds of sensors. However, an attacker could remotely control a car by intercepting and tampering with these data, and even threaten the life of the driver. In this paper, we propose a code-based authentication scheme that provides both secure booting and lightweight data integrity checking to prevent unauthorized remote control. First, we split some of the core code involved in booting the car, and divide it into several secret pieces known as a share polynomial. One of these is distributed to the driver and is then used to reconstruct the booting code, allowing only the driver with this share polynomial to recover the code and start the car. The remaining share polynomials are distributed to the vehicle control unit and used to generate a lightweight pairwise key. Since the proposed scheme requires only multiplication to generate the pairwise key, it is computationally efficient compared to conventional schemes that require expensive exponential computation. Additional communication is not required to set up the pairwise key, because it needs nothing but the id value of the partner. Thus, our scheme is highly suitable for low-performance devices inside the car. We used the Robot OS system, a widely used autonomous vehicle platform, to implement the proposed scheme and evaluated its feasibility on various test cases.
Effectiveness of treatment of occipital neuralgia using the nerve block technique: a prospective analysis of 44 patients

Background. There is a great deal of tools for treatment of occipital neuralgia but currently we are lacking a complete consensus among practitioners regarding the optimal approach to this debilitating condition. Occipital nerve block (ONB) is known as one of the management options but there is lack of scientific literature exploring its effectiveness. Materials and methods. The prospective study was undertaken between March 2014 and February 2018 at the State Vilnius University Hospital. Forty-four patients aged from 28 to 84 years (age mean = 56.30 ± 14.71) of which 79.55% were female (n = 35) were diagnosed with occipital neuralgia (ON) and treated with a local anaesthetic and corticosteroids combination injection into the greater or greater plus lesser occipital nerve (n = 29 and n = 15, respectively) and followed up after 6 months. Analysis of the outcomes of those patients was done by comparing the Visual Analog Scale (VAS) and Barrow Neurological Institute Pain Intensity Score (BNIPIS) prior to treatment, 24 hours after the block, and at a follow-up 6 months later. Analgesic medication consumption before and after 6 months was recorded. A comparison of procedure efficacy in lidocaine and bupivacaine groups was made. Evaluation of block potency for acute and chronic pain categories was conducted as well. The success criteria were defined as patient satisfaction with own condition for at least 6 months, not requiring another block in order to stay comfortable. Results. Of 44 patients, 42 (95.45%) who underwent the occipital nerve block procedure showed satisfactory results for at least 6 months. Mean headache VAS scores decreased from 7.23 ± 0.93 (pre-treatment) to 1.95 ± 1.59 (24 hours after, p < 0.0001) and increased to 2.21 ± 1.73 at the follow-up after 6 months, showing no statistically significant difference between post-interventional and six-month VAS scores (p = 0.07). In all patients the necessity of medication to control pain decreased to 16.67% (n = 7) during the the check-up after 6 months. There was no statistically significant difference in the effectiveness of ONB with regard to the local anaesthetic used or the pain group targeted. Similar results were obtained comparing patients who underwent more than one ONB. Conclusions. Occipital nerve block with a local anaesthetic and corticosteroids provides a safe, simple, and effective treatment method for the patient with medically-refractory occipital neuralgia.