Miracle-dz/UniFAR-FTUs huggingface dataset:
Manipulation of Focal Adhesion Signaling by Pathogenic Microbes

Focal adhesions (FAs) serve as dynamic signaling hubs within the cell. They connect intracellular actin to the extracellular matrix (ECM) and respond to environmental cues. In doing so, these structures facilitate important processes such as cell–ECM adhesion and migration. Pathogenic microbes often modify the host cell actin cytoskeleton in their pursuit of an ideal replicative niche or during invasion to facilitate uptake. As actin-interfacing structures, FA dynamics are also intimately tied to actin cytoskeletal organization. Indeed, exploitation of FAs is another avenue by which pathogenic microbes ensure their uptake, survival and dissemination. This is often achieved through the secretion of effector proteins which target specific protein components within the FA. Molecular mimicry of the leucine–aspartic acid (LD) motif or vinculin-binding domains (VBDs) commonly found within FA proteins is a common microbial strategy. Other effectors may induce post-translational modifications to FA proteins through the regulation of phosphorylation sites or proteolytic cleavage. In this review, we present an overview of the regulatory mechanisms governing host cell FAs, and provide examples of how pathogenic microbes have evolved to co-opt them to their own advantage. Recent technological advances pose exciting opportunities for delving deeper into the mechanistic details by which pathogenic microbes modify FAs.
SSMP: Server Selection for Multipath TCP in CDN Environments

Nowadays, mobile devices are equipped with multiple interfaces connected to various networks, which makes it possible to aggregate bandwidth in actual application. Multipath TCP (MPTCP) is one of the transport protocols that uses multiple interfaces simultaneously and provides robust and efficient data transmission. In practice, MPTCP will interact with various network facilities. Among them, Content Delivery Network (CDN) is a popular one, which is a widely distributed network system deployed across the Internet. Using MPTCP in CDN could provide better performance for users, however, we find that CDN may not give full play to its functions when working with MPTCP. Because the Default Server Selection (DSS) mechanism in CDN only obtains servers optimal in single path connection scenarios, it may not provide the globally optimal server for MPTCP. In this paper, we propose a new algorithm called Server Selection for MPTCP (SSMP), which utilizes all available multi-homed sources to provide the globally optimal performance. SSMP modifies the DNS mechanism to return the optimal server for each available interface by the origin strategy and further selects the globally optimal server for both elephant and mice flows. We compare SSMP with DSS through experiments under video streaming and file download scenarios with both stable and variable environments. Our results show that SSMP consistently utilizes available paths more efficiently than DSS, particularly for servers with a great gap in server quality.
The Optimal Design of Bioleaching Processes

Abstract The development of bioleaching processes has reached the point where the relevant question is not “Do they work? ”, but “How can they be made to work at the lowest possible total cost? ”. Designers of the next generation of processes must consider not only conventional heap leach and slurry-tank systems, which were developed from non-biological extraction techniques, but also designs that follow logically from the unique, autocatalytic, autotrophic and product inhibited nature of bioleaching kinetics. Such processes are conceptualized here in terms of the residence times distributions of the four phases they contain, water, solids, biomass and air. How should they differ from each other, and how might the optimum configuration be approximated in practice? The economic viability of most large-scale bioprocesses depends on achieving a high mean cell residence time either by cell recycle or immobilization, and this insight must be transferred to bioleaching. Since it takes longer to process a large mi.
Nesnelerin İnternetinde Kullanılan Kablosuz Algılayıcı Cihazlar için Bilgi Merkezli Ağ Mimarisinin Uygulanabilirliği Araştırması

Bilgi merkezli aglar, (ICN – Information Centric Networks), istemci-sunucu arasinda uctan uca iletisimin kurulmadigi, kullanicilarin icerik talebini farkli sunuculardan karsilayabildigi bir ag mimarisidir. Bu mimari, talep edilen verinin kaynak haricinde agdaki ara dugumlerin onbellegine kaydedilmesi ile, veriye hizli ve kolay erisim saglamaktadir. Ayrica bu sekilde agda yayilan veri, icerigini sertifikalandirmasi sayesinde guvenli olarak temin edilebilmektedir. Bu nedenle ICN mimarisi, sadece geleneksel Internet uygulamalari icin degil, bunun otesinde Nesnelerin Internet’i (IoT) uygulamalarinda da cazip hale gelmektedir. Nesnelerin Internet’i kullanici ve cevresinin web uygulamalari ve servisleri ile butunlesmesini amaclamaktadir. Bu amac dogrultusunda kablosuz algilayicilar, internete bagli cihazlara duyusal bir uzanti saglayarak “nesne” olarak tanimlanabilmekte, cok sayida uygulama ve servis bu nesnelerin cevreleri ile ilgili urettigi iceriklerden faydalanabilmektedir. Dolayisiyla IoT uygulamalarinin cogunlugunda icerik, uretildigi konum veya cihaza kiyasla daha on plana cikmaktadir. Bu kapsamda IoT kavraminin en onemli bileseni olan, farkli donanim ve haberlesme teknolojilerine sahip dusuk maliyetli kablosuz algilayici aglarin, mevcut internet altyapisina dahil edilmeleri ve birbirleriyle urettikleri icerikleri paylasmasi gerekmektedir. Ancak kablosuz algilayici cihazlarin islemci gucu, bellek kapasitesi ve enerji kaynaklari sinirlidir. Bu cihazlarin, mevcut haberlesme standartlarini kullanarak internete entegre olmalari maliyetli olabilmektedir. Kaynaklari kisitli olan kablosuz algilayici cihazlarin, kuresel bir iletisim aginin parcasi haline gelmesi uygulama ve ag katmaninda cozulmesi gereken pek cok yeni problemi ortaya cikarmaktadir. Bu dogrultuda kablosuz algilayici cihazlar yer aldiklari uygulamanin gereksinimleri ve kullanim alanlarina bagli olarak, baglanti merkezli (uctan uca) veya bilgi merkezli ag mimarilerinden faydalanabileceklerdir. Bu calismada, bilgi merkezli ag mimarisinin kablosuz algilayici cihazlar icin uygulanabilirligi, sunacagi avantajlar ve mevcut eksikleri literaturde yapilan calismalar goz onune alinarak irdelenmistir.
Molecular dual actions of hsa-miRNA and v-miRNA in oncogenic EBV

Viruses are one of the main reasons that cause healthy cells to proliferate and become cancerous. Several viruses have been identified as causative factors for various forms of cancer. Tumor occurrence can be caused by viral oncoprotein activity, persistent infection or inflammation. The molecular process is still complicated to be understood. In recent decades, Homo sapiens cell microRNA (hsa-miRNA) has been discovered by small non-coding RNAs that affect post-transcriptional gene expression. hsa-miRNA is a key control factor for several key biological processes and has a much greater impact on the target gene group. even though they occupy a small part of the genome, they play a great role in the development of cancer. Several viruses produce this tiny RNA, which can regulate their gene expression or affect the host's gene expression. A new hypothesis is that Epstein-Barr virus (EBV) is the first cancer causing virus that is found to produce microRNAs (v-miRNAs). In addition, evidence shows that miRNA encoded by EBV contributes to the occurrence and progression of EBV-related malignancies. Generally, these compounds reduce messenger RNA (mRNA) instability, such as genes that regulate tumorigenesis mechanisms like inflammation, cell cycle control, stress response, differentiation, apoptosis, invasion, and immune pathways. Therefore, EBV-miRNAs are important in the complex interaction between host, virus and EBV tumorigenesis. In terms of malignant tumors, the combinatorial process behind EBV-miRNA still needs further study. In this article, we will introduce EBV-miRNA, including the cellular processes affected by the virus, and their ability to promote cancer.
A Comprehensive Guideline for Bengali Sentiment Annotation

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral. Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.
Nutritional Properties and Benefits of Camelina Oil and Meal

The relation between malnutrition and illnesses has long been known. Food consumed by people in industrialized countries has higher levels of n-3 fatty acids. The benefits and risks of consuming edible fats are always discussed in scientific sources and mass media. Camelina oil has many properties and applications. This oil contains high amounts of omega-3, which helps prevent cancer and obesity. It is used in industry as a biofuel, in the production of resins, waxes, as well as in the production of cosmetics and pharmaceuticals. Vegetable oil factories add industrial antioxidants to prevent spoilage and oxidation and increase the shelf life of the oil which are extremely dangerous to human health. However, due to high contents of alpha-tocopherol and vitamin E in camelina oil, which are powerful antioxidants, it does not need any additives for shelf life. The fatty acid profile of camelina oil depends on a number of factors, including the pedoclimatic conditions of the crop, such as the weather conditions during the growing season, genetic diversity between varieties, even there are differences between the characteristics of winter and spring camelina. Qualitative differences in the content of linoleic acid and alpha linolenic acid are considerable, but there is less variation in other fatty acids. The average fatty acid content of linoleic acid and alpha linolenic acid in Camelina oil is 12.4-15.3% and 36.8-40.8%, respectively. The average content of tocopherols in this oil is between 806-1008 mg/g. Camelina oil is golden yellow with a slight mustard aroma. The average of some physical properties of camelina oil colour such as refractive index, density, soap number and iodine number is equal to 1.4756 (at 25 ° C), 0.92 cc (25 ℃), 105 (iodine in 100 g of oil) and 187.8 (mg/g of oil), respectively.
Design and development of medical electronic instrumentation : a practical perspective of the design, construction, and test of medical devices

Preface. Disclaimer. About the Authors. 1. Biopotential Amplifiers. 2. Bandpass Selection for Biopotential Amplifiers. 3. Design of Safe Medical Device Prototypes. 4. Electromagnetic Compatibility and Medical Devices. 5. Signal Conditioning, Data Acquisition, and Spectral Analysis. 6. Signal Sources for Simulation, Testing, and Calibration. 7. Stimulation of Excitable Tissues. 8. Cardiac Pacing and Defibrillation. Epilogue. Appendix A: Sources for Materials and Components. Appendix B: Accompanying CD--ROM Content. Index. About the Authors.
COMPOSITIONS AND METHODS USEFUL FOR THE TREATMENT AND DAGNOSIS OF NFLAMMLATORY BOWEL DISEASE

Ahmed et al., “Diagnostic MicroRNA Markers for Screening Spo radic Human Colon Cancer and Active Ulcerative Colitis in Stool and Tissue” 6 Cancer Genomics & Proteomics 281-296 (2009). * Sharbati-Tehrani et al., "Concatameric cloning of porcine microRNA molecules after assembly PCR” 375 Biochemical and Biophysical Research Communications 484-489 (2008). * Taylor et al., “MicroRNA signatures of tumor-dervied exosomes as diagnostic biomarkers of ovarian cancer 110 Gynecologic Oncol ogy 13-21 (2008). * Wu et al. “Identification of MicroRNAs associated with illeal and colonic Crohn's disease. ” Inflamm Bowl Dis. Jun. 2, 2010; 16:1729 1738. Okubo et al. "Associated study of common genetic variants in pre microRNAs in patients with ulcerative colitis. ” JClin Immunol. Sep. 17, 2010:31:69-73. Dalalet al., “The rold of microRNA in inflammatory bowel disease. ” Gastroenterology & Heptatology. Nov. 2010:6 (11):714-722. Feng Wu et al., “Peripheral blood MicroRNAs distinguish active ulcerative colitis and Crohn's disease. ” Inflammatory Bowel Dis eases (2010) 17:241-250. Feng Wu et al., “MicroRNAs are differentially expressed in ulcer ative colitis and alter expression of macrophage inflammatory peptide-2a. ” Gastroenterology (2008) 135:1624-1635. Adam M. Zahm et al., “Circulating MicroRNA is a biomarker of pediatric Crohn disease. ” Journal of Pediatric Gastroenterology and Nutrition (2011) 53:26-33. Extended European Search Report from European Patent Applica tion No. 1 1831280.0, dated Jan. 31, 2014.
Use of Three-Dimensional Pavement Surface Data to Analyze Crack Deterioration: Pilot Study on Georgia State Route 26

Cracking is one of the predominant types of pavement surface distresses. The study of crack deterioration is important for establishing accurate pavement deterioration models and determining the timing of an adequate treatment method. It is also crucial to support the validation of the Mechanistic–Empirical Pavement Design Guide. Although many efforts have been made in the laboratory or controlled environments to simulate the crack deterioration behavior, it is still difficult to fully model actual pavement crack deterioration behavior because of the complicated roadway conditions in the real-world environment. With the advance of sensing technology, it has become possible to collect detailed crack data in the field. The objective of this study was to evaluate the feasibility of monitoring detailed crack deterioration behaviors in the field with the use of three-dimensional sensing technology, crack detection, and multiscale crack property extraction. This effort directly supports the study of multiscale crack deterioration behaviors. In this research, a pilot study was conducted on Georgia State Route 26, and the crack deterioration behaviors were monitored over 2 years. Analysis was conducted at the 1-mi segment level, the 100-ft section level, and the detailed crack level. The study shows promising results for observing and quantifying crack deterioration behaviors. The issues for analyzing long-term monitoring data are also discussed in this paper. Ultimately, the proposed method should be able to be used to support the development of a long-term performance forecasting model.
Clustering based Evolving Neural Network Intrusion Detection for MCPS Traffic Security

— In the era of Internet, exploits and vulnerabilities of our systems can be used by attackers to violate confidentiality, integrity, and availability. These attacks pose even more serious consequences when we consider medical networks such as Medical Cyber Physical Systems (MCPS). Therefore, the design of an efficient intrusion detection system is vital. However, the success of most of these systems is linked to custom statistical signature based solutions. It becomes a limiting constraint when there are myriad possible attacks emerging every day. To solve the above issues, several machine learning techniques have been developed to form robust detection systems. Nevertheless, these systems are not efficient with low-frequency attacks and are often considered as outliers, even though the consequences of missing upon such attacks can be dangerous. Therefore, this paper proposes an evolving machine learning technique, based on clustering and neural network classification to improve the detection accuracy of all forms of network intrusion traffic. Our experimental results on the standardized Knowledge Discovery and Data Mining (KDD) Cup 99 public dataset show that the proposed mechanism can outperform the well-established boosted decision tree algorithm under different selected features environments.
Spiking Neural Network Based on Multi-Scale Saliency Fusion for Breast Cancer Detection

Deep neural networks have been successfully applied in the field of image recognition and object detection, and the recognition results are close to or even superior to those from human beings. A deep neural network takes the activation function as the basic unit. It is inferior to the spiking neural network, which takes the spiking neuron model as the basic unit in the aspect of biological interpretability. The spiking neural network is considered as the third-generation artificial neural network, which is event-driven and has low power consumption. It modulates the process of nerve cells from receiving a stimulus to firing spikes. However, it is difficult to train spiking neural network directly due to the non-differentiable spiking neurons. In particular, it is impossible to train a spiking neural network using the back-propagation algorithm directly. Therefore, the application scenarios of spiking neural network are not as extensive as deep neural network, and a spiking neural network is mostly used in simple image classification tasks. This paper proposed a spiking neural network method for the field of object detection based on medical images using the method of converting a deep neural network to spiking neural network. The detection framework relies on the YOLO structure and uses the feature pyramid structure to obtain the multi-scale features of the image. By fusing the high resolution of low-level features and the strong semantic information of high-level features, the detection precision of the network is improved. The proposed method is applied to detect the location and classification of breast lesions with ultrasound and X-ray datasets, and the results are 90.67% and 92.81%, respectively.
Tensor, Symmetric, Exterior, and Other Powers of Persistence Modules

We reformulate the persistent (co)homology of simplicial filtrations, viewed from a more algebraic setting, namely as the (co)homology of a chain complex of graded modules over polynomial ring $K[t]$. We also define persistent (co)homology of groups, associative algebras, Lie algebras, etc. \par Then we obtain formulas for tensor powers $T^n(M),S^n(M),\Lambda^{\! n}(M)$ where $M$ is a persistence module. We discuss the cyclic and dihedral powers of persistence modules, and more generally quotients of $T^n(M)$ by a group action.
An Approach to Improving the Efficiency of the Database of a Large Industrial Enterprise

The paper describes an approach to improving the efficiency of queries in the SQL language in order to optimize the time of retrieving and processing data from highly loaded DBMS. The proposed approach involves the formation of a knowledge base in the form of sets of rules that allow you to optimize the time of data retrieval from relational databases. The set of rules that increase the efficiency of processing data from the database can be divided into sets that solve the following problems: the lack of profiling, performance issues caused by combining of SQL and the algorithmic part of the task, SQL execution structure. Also, the paper presents the results of experiments on data processing of the information system of the Branch of PJSC "Il" - Aviastar.
A robust malware traffic classifier to combat security breaches in industry 4.0 applications

Industry 4.0 integrates cyber systems, physical devices, and digital networks to automate the industrial process. Many sectors aim to adopt the best practices outlined in Industry 4.0. This indicates well for the future networking of an increasing number of devices. As crucial as intelligent automation is, it is essential that it be protected. The proliferation of Internet‐enabled gadgets could raise vulnerability to a variety of threats, malware among them. Intruders see a synthesis of factors as a chance to carry out their malicious plan. Keeping sensitive data and information protected from malicious software is a high responsibility for all industries. It is critical to have both a trustworthy approach and a large dataset to work with when constructing a malware traffic classifier. Malware's capacity to elude detection by antivirus programs improves with the day. Because this malware has the potential to compromise the entire network, establishing a malware traffic classifier requires a strong approach. As the number of data increases, the classifier has a harder time distinguishing between benign and malicious network entries. As a result, weighing too many factors is a time‐consuming process. To assist with these types of real‐world challenges, we construct an effective hybrid selection component, which is subsequently followed by a neural network classifier in this research. The Malware traffic classifier provided here selects the principal feature using filter and wrapper techniques. The feature columns provided by the feature selection program are used to construct a neural network‐based binary malware classifier. The given malware traffic classification framework was tested using the MTA‐KDD'19 dataset. We set up an experiment in this investigation to examine the way different feature counts perform using a neural‐based classifier. The suggested framework achieves 96.8% accuracy while just considering the bare minimum of five features, which is a substantial increase over alternative methods.