Search results for: discovery system

Authors: Ahmed Al-Saadi, Ali Hassanpour, Tariq Mahmud

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The main objective of this study is to investigate ways to reduce the aerodynamic drag coefficient and to increase the stability of the full-size Sport Utility Vehicle using three-dimensional Computational Fluid Dynamics (CFD) simulation. The baseline model in the simulation was the Land Rover Discovery 4. Many aerodynamic devices and external design modifications were used in this study. These reduction aerodynamic techniques were tested individually or in combination to get the best design. All new models have the same capacity and comfort of the baseline model. Uniform freestream velocity of the air at inlet ranging from 28 m/s to 40 m/s was used. ANSYS Fluent software (version 16.0) was used to simulate all models. The drag coefficient obtained from the ANSYS Fluent for the baseline model was validated with experimental data. It is found that the use of modern aerodynamic add-on devices and modifications has a significant effect in reducing the aerodynamic drag coefficient.

Keywords: aerodynamics, RANS, sport utility vehicle, turbulent flow

Procedia PDF Downloads 311

Authors: Chih-Ping Chang

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Different from traditional objective evidence, social media evidence has its own characteristics with easily tampering, recoverability, and cannot be read without using other devices (such as a computer). Simply taking a screenshot from social network sites must be questioned its original identity. When the police search and seizure digital information, a common way they use is to directly print out digital data obtained and ask the signature of the parties at the presence, without taking original digital data back. In addition to the issue on its original identity, this conduct to obtain evidence may have another two results. First, it will easily allege that is tampering evidence because the police wanted to frame the suspect and falsified evidence. Second, it is not easy to discovery hidden information. The core evidence associated with crime may not appear in the contents of files. Through discovery the original file, data related to the file, such as the original producer, creation time, modification date, and even GPS location display can be revealed from hidden information. Therefore, how to show this kind of evidence in the courtroom will be arguably the most important task for ruling social media evidence. This article, first, will introduce forensic software, like EnCase, TCT, FTK, and analyze their function to prove the identity with another digital data. Then turning back to the court, the second part of this article will discuss legal standard for authentication of social media evidence and application of that forensic software in the courtroom. As the conclusion, this article will provide a rethinking, that is, what kind of authenticity is this rule of evidence chase for. Does legal system automatically operate the transcription of scientific knowledge? Or furthermore, it wants to better render justice, not only under scientific fact, but through multivariate debating.

Keywords: federal rule of evidence, internet forensic, printouts as evidence, social media evidence, United States v. Vayner

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Authors: Manssour Habbash, Srinivasa Rao Idapalapati

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Although writing is a multifaceted task, teaching writing is a demanding task basically for two reasons: Grammar and Syntax. This article provides a method of teaching writing that was found to be effective in improving students’ academic writing composition skill. The article explains the concepts of ‘guided-discovery’ and ‘guided-construction’ upon which a method of teaching writing is grounded and developed. Providing a brief commentary on what the core could mean primarily, the article presents an exposition of understanding and identifying the core and building upon the core that can demonstrate the way a teacher can make use of the concepts in teaching for improving the writing skills of their students. The method is an adaptation of grammar translation method that has been improvised to suit to a student-centered classroom environment. An intervention of teaching writing through this method was tried out with positive outcomes in formal classroom research setup, and in view of the content’s quality that relates more to the classroom practices and also in consideration of its usefulness to the practicing teachers the process and the findings are presented in a narrative form along with the results in tabular form.

Keywords: core of a text, guided construction, guided discovery, theme of a text

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Authors: Arya Farjand

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This study delves into the Early Pleistocene fauna of the Yuanmou Basin, highlighting two significant findings. The first is the discovery of exceptionally well-preserved canid coprolites, which provide a rare glimpse into the diet and ecological niche of these ancient carnivores. The analysis of these coprolites has revealed a diet rich in diverse prey species, suggesting a complex food web and a dynamic ecological environment. This discovery not only sheds light on the dietary habits of these canids but also offers broader insights into the region's ecological dynamics during the Early Pleistocene. Additionally, the preservation of these coprolites allows for detailed study of the carnivore's role in the ecosystem, including their interactions with other species and the overall health of the environment. The second major finding is the identification of a mustelid species, Eirictis yuanmouensis, from the same fossil horizon as the coprolites. This discovery is crucial for understanding the diversity and evolution of Mustelidae in the region. The detailed analysis of cranial and dental morphology of Eirictis yuanmouensis indicates unique adaptations that suggest a specialized ecological niche. This finding, in conjunction with the coprolite analysis, provides a comprehensive view of the ecological niches occupied by both mustelids and hyenas, enhancing our understanding of their adaptations and interactions within this paleoenvironment. The study's significance is further amplified by the analysis of pollen data from the same horizon, which indicates a paleoenvironment characterized by rapid climatic changes and a dominant semiarid climate. This combination of faunal and floral data paints a detailed picture of the Early Pleistocene environment in the Yuanmou Basin, offering valuable insights into the interactions between different carnivore species and their adaptation strategies in response to changing environmental conditions.

Keywords: Yuanmou Basin, coprolite, Hyaena, eirictis yuanmouensis, early pleistocene

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Authors: Jin-Feng Hu

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Over the past decades, the global biodiversity has continued to decline. The threats to the terrestrial plant species have increased under anthropogenic activities and other massive ecological change impacts. The situation is much more serious in China, the third richest countries regarding plant biodiversity in the world. It was not until 1992 that the first volume of the China Plant Red Data Book was published. Nowadays, a significant number of Chinese endemic plants have been threatened (The IUCN Red List). Nevertheless, plant-originated natural products (NPs) have continued to play a crucial role in the drug discovery and development process. The opportunity for identifying new chemical entities for emerging and malignant diseases depends on a diversity of drug-producing species. Several statistical surveys unveiled that the rare and endangered plants (REPs) have proven to be better sources for drug discovery than other botanic sources. The identification of bioactive NPs from REPs reveals the importance of conservation efforts in preventing species diversity loss and addressing human diseases at the same time. Thus, there is an urgent need to investigate these fragile REPs. Since 2013, our group has initially launched a special program to systematically identify bioactive/novel NPs from REPs native to China. The selected plant species were generally collected from the remote Mountain areas, and have never been chemically or pharmacologically investigated. Due to the difficult collection of the mass-limited samples of REPs, studies on the secondary metabolites of REPs-associated endophytes would provide a promising alternative potential solution. This presentation details the achievements that related to a series of “Phytochemical and biological studies on rare and endangered plants endemic to China”.

Keywords: bioactive naturally-occrring compounds, rare and endengered plants (REPs), plant endophytes, drug discovery

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Authors: J. Hidalgo de Quintana, I. Stoner, M. Tackett, G. Doran, C. Rafferty, A. Windemuth, J. Tytell, D. Pregibon

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We have developed the Multiplexed Circulating microRNA assay that allows the detection of up to 68 microRNA targets per sample. The assay combines particle­based multiplexing, using patented Firefly hydrogel particles, with single­ step RT-PCR signal. Thus, the Circulating microRNA assay leverages PCR sensitivity while eliminating the need for separate reverse transcription reactions and mitigating amplification biases introduced by target­-specific qPCR. Furthermore, the ability to multiplex targets in each well eliminates the need to split valuable samples into multiple reactions. Results from the Circulating microRNA assay are interpreted using Firefly Analysis Workbench, which allows visualization, normalization, and export of experimental data. To aid discovery and validation of biomarkers, we have generated fixed panels for Oncology, Cardiology, Neurology, Immunology, and Liver Toxicology. Here we present the data from several studies investigating circulating and tumor microRNA, showcasing the ability of the technology to sensitively and specifically detect microRNA biomarker signatures from fluid specimens.

Keywords: biomarkers, biofluids, miRNA, photolithography, flowcytometry

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Authors: Neelam Rashid, Muhammad Zafar, Mushtaq Ahmad, Khafsa Malik, Syed Nasar Shah

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The present study was conducted to study the role of medicinal plants used to cure different ailments in Azad Kashmir. Various ethno medicinal surveys were carried out during 2016 to enlist the uses of plants against various ailments by rural communities of the area. Information was obtained from 60 local people including 45 males (10 traditional health practitioners) and 15 females by semi structured interviews and group discussions. 65 plant species belonging to 45 families were reported. The dominant plant habit was herbaceous (56%) while decoction was the most common method of utilization (40%). The most cited turmoil was the gastrointestinal disorders. The data obtained were analyzed using ethno medicinal indices such as FL, UV, ICF, FC, and RFC. Results revealed that various species had numerous uses in curing of diseases. So conservation of biodiversity of these medicinal plants and traditional knowledge can play important role in improving the local health conditions of rural people and modern drug discovery and development.

Keywords: medicinal plants, ailments, drug, health, traditional

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Authors: Anahita Farhang Ghahfarokhi, Alessandro Berti, Wil M.P. van der Aalst

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Process mining provides ways to analyze business processes. Common process mining techniques consider the process as a whole. However, in real-life business processes, different behaviors exist that make the overall process too complex to interpret. Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes. Process cubes organize event data using different dimensions. Each cell contains a set of events that can be used as an input to apply process mining techniques. Existing work on process cubes assume single case notions. However, in real processes, several case notions (e.g., order, item, package, etc.) are intertwined. Object-centric process mining is a new branch of process mining addressing multiple case notions in a process. To make a bridge between object-centric process mining and process comparison, we propose a process cube framework, which supports process cube operations such as slice and dice on object-centric event logs. To facilitate the comparison, the framework is integrated with several object-centric process discovery approaches.

Keywords: multidimensional process mining, mMulti-perspective business processes, OLAP, process cubes, process discovery, process mining

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Authors: Mai Ghazal, Ahmed Hammad

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Cost overruns in construction projects are considered as worldwide challenges since the cost performance is one of the main measures of success along with schedule performance. To overcome this problem, studies were conducted to investigate the cost overruns' factors, also projects' historical data were analyzed to extract new and useful knowledge from it. This research is studying and analyzing the effect of some factors causing cost overruns using the historical data from completed construction projects. Then, using these factors to estimate the probability of cost overrun occurrence and predict its percentage for future projects. First, an intensive literature review was done to study all the factors that cause cost overrun in construction projects, then another review was done for previous researcher papers about mining process in dealing with cost overruns. Second, a proposed data warehouse was structured which can be used by organizations to store their future data in a well-organized way so it can be easily analyzed later. Third twelve quantitative factors which their data are frequently available at construction projects were selected to be the analyzed factors and suggested predictors for the proposed model.

Keywords: construction management, construction projects, cost overrun, cost performance, data mining, data warehousing, knowledge discovery, knowledge management

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Authors: Vani Singhal, Jitendra Parmar, Satyendra Singh Chouhan

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Unseen class discovery has now become an important part of a machine-learning algorithm to judge new classes. Unseen classes are the classes on which the machine learning model is not trained on. With the advancement in technology and AI replacing humans, the amount of data has increased to the next level. So while implementing a model on real-world examples, we come across unseen new classes. Our aim is to find the number of unseen classes by using a hierarchical-based active learning algorithm. The algorithm is based on hierarchical clustering as well as active sampling. The number of clusters that we will get in the end will give the number of unseen classes. The total clusters will also contain some clusters that have unseen classes. Instead of first discovering unseen classes and then finding their number, we directly calculated the number by applying the algorithm. The dataset used is for intent classification. The target data is the intent of the corresponding query. We conclude that when the machine learning model will encounter real-world data, it will automatically find the number of unseen classes. In the future, our next work would be to label these unseen classes correctly.

Keywords: active sampling, hierarchical clustering, open world learning, unseen class discovery

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Authors: Amna J. Ghith, Khaled Abu Zid, Khairia Youssef, Nasser Saad

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Backgrounds: The multikinase and vascular endothelial growth factor (VEGF) receptor inhibitors interrupt the pathway by which angiogenesis becomes established and promulgated, resulting in the inadequate nourishment of metastatic disease. VEGFR-2 has been the principal target of anti-angiogenic therapies. We disclose the new thieno pyrimidines as inhibitors of VEGFR-2 designed by a molecular modeling approach with increased synergistic activity and decreased side effects. Purpose: 2-substituted thieno pyrimidines are designed and synthesized with anticipated anticancer activity based on its in silico molecular docking study that supports the initial pharmacophoric hypothesis with a same binding mode of interaction at the ATP-binding site of VEGFR-2 (PDB 2QU5) with high docking score. Methods: A series of compounds were designed using discovery studio 4.1/CDOCKER with a rational that mimic the pharmacophoric features present in the reported active compounds that targeted VEGFR-2. An in silico ADMET study was also performed to validate the bioavailability of the newly designed compounds. Results: The Compounds to be synthesized showed interaction energy comparable to or within the range of the benzimidazole inhibitor ligand when docked with VEGFR-2. ADMET study showed comparable results most of the compounds showed absorption within (95-99) zone varying according to different substitutions attached to thieno pyrimidine ring system. Conclusions: A series of 2-subsituted thienopyrimidines are to be synthesized with anticipated anticancer activity and according to docking study structure requirement for the design of VEGFR-2 inhibitors which can act as powerful anticancer agents.

Keywords: docking, discovery studio 4.1/CDOCKER, heterocycles based anticancer agents, 2-subsituted thienopyrimidines

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Shasline Gedeon, Tiffany W. Ardley, Ying Wang, Nathan J. Gesmundo, Katarina A. Sarris, Ana L. Aguirre

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Drug discovery and delivery involve drug targeting, an approach that helps find a drug against a chosen target through high throughput screening and other methods by way of identifying the physical properties of the potential lead compound. Physical properties of potential drug candidates have been an imperative focus since the unveiling of Lipinski's Rule of 5 for oral drugs. Throughout a compound's journey from discovery, clinical phase trials, then becoming a classified drug on the market, the desirable properties are optimized while minimizing/eliminating toxicity and undesirable properties. In the pharmaceutical industry, the ability to generate molecules in parallel with maximum efficiency is a substantial factor achieved through sp²-sp² carbon coupling reactions, e.g., Suzuki Coupling reactions. These reaction types allow for the increase of aromatic fragments onto a compound. More recent literature has found benefits to decreasing aromaticity, calling for more sp³-sp² carbon coupling reactions instead. The objective of this project is to provide a comparison between various sp³-sp² carbon coupling methods and reaction conditions, collecting data on production of the desired product. There were four different coupling methods being tested amongst three cores and 4-5 installation groups per method; each method ran under three distinct reaction conditions. The tested methods include the Photoredox Decarboxylative Coupling, the Photoredox Potassium Alkyl Trifluoroborate (BF3K) Coupling, the Photoredox Cross-Electrophile (PCE) Coupling, and the Weix Cross-Electrophile (WCE) Coupling. The results concluded that the Decarboxylative method was very difficult in yielding product despite the several literature conditions chosen. The BF3K and PCE methods produced competitive results. Amongst the two Cross-Electrophile coupling methods, the Photoredox method surpassed the Weix method on numerous accounts. The results will be used to build future libraries.

Keywords: drug discovery, high throughput chemistry, photoredox chemistry, sp³-sp² carbon coupling methods

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Authors: Nadjat Zerf Boudjettou, Fahima Nader, Rachid Chalal

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Knowledge management is to acquire and represent knowledge relevant to a domain, a task or a specific organization in order to facilitate access, reuse and evolution. This usually means building, maintaining and evolving an explicit representation of knowledge. The next step is to provide access to that knowledge, that is to say, the spread in order to enable effective use. Knowledge management in the medical field aims to improve the performance of the medical organization by allowing individuals in the care facility (doctors, nurses, paramedics, etc.) to capture, share and apply collective knowledge in order to make optimal decisions in real time. In this paper, we propose a knowledge management approach based on integration technique of heterogeneous data in the medical field by creating a data warehouse, a technique of extracting knowledge from medical data by choosing a technique of data mining, and finally an exploitation technique of that knowledge in a case-based reasoning system.

Keywords: data warehouse, data mining, knowledge discovery in database, KDD, medical knowledge management, Bayesian networks

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Authors: Michio Kurosu

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Alterations in glycosylation not only directly impact cell growth and survival but also facilitate tumor-induced immunomodulation and eventual metastasis. Identification of cell type-specific glycoconjugates (tumor markers) has led to the discovery of new assay systems for certain cancers via immunodetection reagents. N- and O-linked glycans are the most abundant forms of glycoproteins. Recent studies of cancer immunotherapy are based on the immunogenicity of truncated O-glycan chains (e.g., Tn, sTn, T, and sLea/x). The prevalence of N-linked glycan changes in the development of tumor cells is known; however, therapeutic antibodies against N-glycans have not yet been developed. This is due to the lack of specificity of N-linked glycans between normal/healthy and cancer cells. Abnormal branching of N-linked glycans has been observed, particularly in solid cancer cells. While the discovery of drug-like glycosyltransferase inhibitors that block the biosynthesis of specific branching has a very low likelihood of success, altered glycosylation levels can be exploited by suppressing N-glycan biosynthesis through the inhibition of dolichyl-phosphate N-acetylglucosaminephosphotransferase1 (DPAGT1) activity. Inhibition of DPAGT1 function leads to changes of O-glycosylation on proteins associated with mitochondria and zinc finger binding proteins (indirect effects). On the basis of dynamic crosstalk between DPAGT1 and Snail/Slung/ZEB1 (a family of transcription factors that promote the repression of the adhesion molecules), we have developed pharmacologically acceptable selective DPAGT1 inhibitors. Tunicamycin kills a wide range of cancer and healthy cells in a non-selective manner. In sharp contrast, our DPAGT1 inhibitors display strong cytostatic effects against 16 solid cancers, which require the overexpression of DPAGT1 in their progression but do not affect the cell viability of healthy cells. The identified DPAGT1 inhibitors possess impressive anti-metastatic ability in various solid cancer cell lines and induce their mitochondrial structural changes, resulting in apoptosis. A prototype DPAGT1 inhibitor, APPB has already been proven to shrink solid tumors (e.g., pancreatic cancers, triple-negative breast cancers) in vivo while suppressing metastases and has strong synergistic effects when combined with current cytotoxic drugs (e.g., paclitaxel). At this conference, our discovery of selective DPAGT1 inhibitors with drug-like properties and proof-of-pharmaceutical concept studies of a novel DPAGT1 inhibitor are presented.

Keywords: DPAGT1 inhibitors, anti-metastatic drugs, natural product based drug designs, cytostatic effects

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: Alyona Cerfontyne, Levita D'Souza, Lefteris Patlamazoglou

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Unlike adoption and donor-assisted reproduction, misattributed paternity occurring within the context of spontaneous conception and outside of formally recognised practices of having a child remains largely an understudied phenomenon. In adulthood, to discover misattributed paternity, i.e., that the man you call your father is not related to you genetically, can have profound implications for everyone affected. Until the advent of direct-to-consumer DNA testing 20 years ago, such discoveries were relatively rare. Despite the growing number of individuals uncovering their biogenetic paternity through genetic testing, there is very limited research on misattributed paternity from the perspective of adult children affected by it. No research exists on how to support these individuals through counselling post-discovery. Framed as insider action research, this study aimed to explore the perceived psychosocial consequences of misattributed paternity discoveries and coping strategies used by individuals who discover their misattributed paternity status in adulthood. In total, 12 individuals with misattributed paternity participated in semi-structured interviews in July-August 2022. The collected data was analysed using reflexive thematic analysis. The study’s results indicate that discovering misattributed paternity in adulthood can be likened to a watershed moment forever changing the trajectory of one’s life. Psychological experiences consistent with trauma, as well as grief and loss, re-evaluation of close family relationships, reestablishment of one’s identity, as well as experiencing a profound need to belong are the key themes emerging from the analysis of psychosocial experiences. Post-discovery, individuals with misattributed paternity employ a wide range of emotional and problem-focused coping strategies, amongst which seeking connection with those who understand, searching for information on the new biogenetic family and finding new meanings to life are most prominent. The study contributes both to the academic and practical knowledge of experiences of misattributed paternity and highlights the importance of further research on the topic.

Keywords: discovery of misattributed paternity, misattributed paternity, paternal discrepancy, psychosocial consequences, coping

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Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

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Authors: Sutapa Som Chaudhury, Chitrangada Das Mukhopadhyay

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Alzheimer’s disease is a well-known form of dementia since its discovery in 1906. Current Food and Drug Administration approved medications e.g. cholinesterase inhibitors, memantine offer modest symptomatic relief but do not play any role in disease modification or recovery. In last three decades many small molecules, chaperons, synthetic peptides, partial β-secretase enzyme blocker have been tested for the development of a drug against Alzheimer though did not pass the 3rd clinical phase trials. Here in this study, we designed a PEGylated, aminogenic, tripeptidic polymer with two different molecular weights based on the aggregation prone amino acid sequence 17-20 in amyloid beta (Aβ) 1-42. Being conjugated with poly-ethylene glycol (PEG) which self-assembles into hydrophilic nanoparticles, these PEGylated tripeptides constitute a very good drug delivery system crossing the blood brain barrier while the peptide remains protected from proteolytic degradation and non-specific protein interactions. Moreover, being completely aminogenic they would not raise any side effects. These peptide inhibitors were evaluated for their effectiveness against Aβ42 fibrillation at an early stage of oligomer to fibril formation as well as preformed fibril clearance via Thioflavin T (ThT) assay, dynamic light scattering analyses, atomic force microscopy and scanning electron microscopy. The inhibitors were proved to be safe at a higher concentration of 20µM by the reduction assay of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) dye. Moreover, SHSY5Y neuroblastoma cells have shown a greater survivability when treated with the inhibitors following Aβ42 fibril and oligomer treatment as compared with the control Aβ42 fibril and/or oligomer treated neuroblastoma cells. These make the peptidic inhibitors a promising compound in the aspect of the discovery of alternative medication for Alzheimer’s disease.

Keywords: Alzheimer’s disease, alternative medication, amyloid beta, PEGylated peptide

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Authors: Hema Bhardwaj, D. Srinivasa Rao

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Technology for analysing, processing, and extracting meaningful data from enormous and complicated datasets can be termed as "big data." The technique of big data mining and big data analysis is extremely helpful for business movements such as making decisions, building organisational plans, researching the market efficiently, improving sales, etc., because typical management tools cannot handle such complicated datasets. Special computational and statistical issues, such as measurement errors, noise accumulation, spurious correlation, and storage and scalability limitations, are brought on by big data. These unique problems call for new computational and statistical paradigms. This research paper offers an overview of the literature on big data mining, its process, along with problems and difficulties, with a focus on the unique characteristics of big data. Organizations have several difficulties when undertaking data mining, which has an impact on their decision-making. Every day, terabytes of data are produced, yet only around 1% of that data is really analyzed. The idea of the mining and analysis of data and knowledge discovery techniques that have recently been created with practical application systems is presented in this study. This article's conclusion also includes a list of issues and difficulties for further research in the area. The report discusses the management's main big data and data mining challenges.

Keywords: big data, data mining, data analysis, knowledge discovery techniques, data mining challenges

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Authors: Rima A. Ajlouni

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The discovery of quasi-periodic structures in material science is revealing an exciting new class of symmetries, which has never been explored before. Due to their unique structural and visual properties, these symmetries are drawing interest from many scientific and design disciplines. Especially, in art and architecture, these symmetries can provide a rich source of geometry for exploring new patterns, forms, systems, and structures. However, the structural systems of these complicated symmetries are still posing a perplexing challenge. While much of their local order has been explored, the global governing system is still unresolved. Understanding their unique global long-range order is essential to their generation and application. The recent discovery of dodecagonal quasi-periodic patterns in historical Islamic architecture is generating a renewed interest into understanding the mathematical principles of traditional Islamic geometry. Astonishingly, many centuries before its description in the modern science, ancient artists, by using the most primitive tools (a compass and a straight edge), were able to construct patterns with quasi-periodic formations. These ancient patterns can be found all over the ancient Islamic world, many of which exhibit formations with 5, 8, 10 and 12 quasi-periodic symmetries. Based on the examination of these historical patterns and derived from the generating principles of Islamic geometry, a global multi-level structural model is presented that is able to describe the global long-range order of dodecagonal quasi-periodic formations in Islamic Architecture. Furthermore, this method is used to construct new quasi-periodic tiling systems as well as generating their deflation and inflation rules. This method can be used as a general guiding principle for constructing infinite patches of dodecagon-based quasi-periodic formations, without the need for local strategies (tiling, matching, grid, substitution, etc.) or complicated mathematics; providing an easy tool for scientists, mathematicians, teachers, designers and artists, to generate and study a wide range of dodecagonal quasi-periodic formations.

Keywords: dodecagonal, Islamic architecture, long-range order, quasi-periodi

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Authors: Elena G. Salina, Vadim A. Makarov

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With the emergence of primary resistance to the current drugs and wide distribution of latent tuberculosis infection, a need for new compounds with a novel mode of action is growing steadily. Copper-mediated innate immunity and antibacterial toxicity propose novel strategies in TB drug discovery and development. Transcriptome of M. tuberculosis was obtained by RNA-seq, intracellular copper content was measured by ISP MS and complexes of 1-hydroxy-2-thiopyridines with copper were detected by HPLC.1-hydroxy-2-thiopyridine derivatives were found to be highly active in vitro against both actively growing and dormant non-culturable M. tuberculosis. Transcriptome response to 1-hydroxy-2-thiopyridines revealed signs of copper toxicity in M. tuberculosis bacilli. Indeed, Cu was found to accumulate inside cells treated with 1-hydroxy-2-thiopyridines. These compounds were found to form stable charged lipophylic complexes with Cu²⁺ ions which transport into mycobacterial cell. Subsequent metabolic destruction of the complex led to transformation of 1-hydroxy-2-thiopyridines into 2-methylmercapto-2-ethoxycarbonylpyridines, which did not possess antitubercular activity and releasing of free Cu²⁺ in the cytoplasm. 1-hydroxy-2-thiopyridines are a potent class of Cu-dependent inhibitors of M. tuberculosis which may control M. tuberculosis infection by impairment of copper homeostasis. Acknowledgment: This work was financially supported by the Ministry of Education and Science of the RussianFederation (Agreement No 14.616.21.0065; unique identifier RFMEFI61616X0065).

Keywords: copper toxicity, drug discovery, M. tuberculosis inhibitors, 2-thiopyridines

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Authors: Yusra Abdulsalam Alqamati, Ahmed Alkilany

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The teaching staff is a distinct group whose impact is on the educational process and which plays an important role in enhancing the quality of the academic education process. To improve the management effectiveness of the academy, the Teaching Staff Management System (TSMS) proposes that all teacher processes be digitized. Since the BPMN approach can accurately describe the processes, it lacks a clear picture of the process flow map, something that the process mining approach has, which is extracting information from event logs for discovery, monitoring, and model enhancement. Therefore, these two methodologies were combined to create the most accurate representation of system operations, the ability to extract data records and mining processes, recreate them in the form of a Petri net, and then generate them in a BPMN model for a more in-depth view of process flow. Additionally, the TSMS processes will be orchestrated to handle all requests in a guaranteed small-time manner thanks to the integration of the Google Cloud Platform (GCP), the BPM engine, and allowing business owners to take part throughout the entire TSMS project development lifecycle.

Keywords: process mining, BPM, business process model and notation, Petri net, teaching staff, Google Cloud Platform

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Authors: Reem Al-Shidhani, Isabelle S. R. Storer, Michael J. Bromley, Lydia Tabernero

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Invasive fungal diseases are an increasing global health concern that contributes to the high mortality rates in immunocompromised patients. The rising of antifungal resistance severely lowers the efficacy of the limited antifungal agents available. New antifungal drugs that target new mechanisms are necessary to tackle the current shortfalls. Amongst post- modifications, phosphorylation is a predominant and an outstanding protein alteration in all eukaryotes. In fungi, protein phosphorylation plays a vital role in many signal transduction pathways, including cell cycle, cell growth, metabolism, transcription, differentiation, proliferation, and virulence. The investigation of Aspergillus fumigatus phosphatases revealed seven genes essential for viability. Inhibiting one of these phosphatases is a new interesting route to develop novel antifungal drugs. In this study, we carried out an early drug discovery process targeting oneessential phosphatase, GlcA. Here, we report the identification of new GlcA inhibitors that show antifungal activity. These important finding open a new avenue to the development of novel antifungals to expand the current narrow arsenal of clinical candidates.

Keywords: invasive fungal diseases, phosphatases, GlcA, competitive inhibitors

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Authors: Pablo Chico De Guzman, Cesar Sanchez

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Microservice architectures are increasingly in distributed cloud applications due to the advantages on the software composition, development speed, release cycle frequency and the business logic time to market. On the other hand, these architectures also introduce some challenges on the testing and release phases of applications. Container technology solves some of these issues by providing reproducible environments, easy of software distribution and isolation of processes. However, there are other issues that remain unsolved in current container technology when dealing with multiple machines, such as networking for multi-host communication, service discovery, load balancing or data persistency (even though some of these challenges are already solved by traditional cloud vendors in a very mature and widespread manner). Container cluster management tools, such as Kubernetes, Mesos or Docker Swarm, attempt to solve these problems by introducing a new control layer where the unit of deployment is the container (or the pod — a set of strongly related containers that must be deployed on the same machine). These tools are complex to configure and manage and they do not follow a pure immutable infrastructure approach since servers are reused between deployments. Indeed, these tools introduce dependencies at execution time for solving networking or service discovery problems. If an error on the control layer occurs, which would affect running applications, specific expertise is required to perform ad-hoc troubleshooting. As a consequence, it is not surprising that container cluster support is becoming a source of revenue for consulting services. This paper presents i2kit, a deployment tool based on the immutable infrastructure pattern, where the virtual machine is the unit of deployment. The input for i2kit is a declarative definition of a set of microservices, where each microservice is defined as a pod of containers. Microservices are built into machine images using linuxkit —- a tool for creating minimal linux distributions specialized in running containers. These machine images are then deployed to one or more virtual machines, which are exposed through a cloud vendor load balancer. Finally, the load balancer endpoint is set into other microservices using an environment variable, providing service discovery. The toolkit i2kit reuses the best ideas from container technology to solve problems like reproducible environments, process isolation, and software distribution, and at the same time relies on mature, proven cloud vendor technology for networking, load balancing and persistency. The result is a more robust system with no learning curve for troubleshooting running applications. We have implemented an open source prototype that transforms i2kit definitions into AWS cloud formation templates, where each microservice AMI (Amazon Machine Image) is created on the fly using linuxkit. Even though container cluster management tools have more flexibility for resource allocation optimization, we defend that adding a new control layer implies more important disadvantages. Resource allocation is greatly improved by using linuxkit, which introduces a very small footprint (around 35MB). Also, the system is more secure since linuxkit installs the minimum set of dependencies to run containers. The toolkit i2kit is currently under development at the IMDEA Software Institute.

Keywords: container, deployment, immutable infrastructure, microservice

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Authors: Bing Li, Zhi Li, Yilong Yang

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Web classification can promote the quality of service discovery and management in the service repository. It is widely used to locate developers desired services. Although traditional classification methods based on supervised learning models can achieve classification tasks, developers need to manually mark web services, and the quality of these tags may not be enough to establish an accurate classifier for service classification. With the doubling of the number of web services, the manual tagging method has become unrealistic. In recent years, the attention mechanism has made remarkable progress in the field of deep learning, and its huge potential has been fully demonstrated in various fields. This paper designs a multi-head attention graph convolutional network (MHAGCN) service classification method, which can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. The framework combines the advantages of the attention mechanism and graph convolutional neural network. It can classify web services through automatic feature extraction. The comprehensive experimental results on a real dataset not only show the superior performance of the proposed model over the existing models but also demonstrate its potentially good interpretability for graph analysis.

Keywords: attention mechanism, graph convolutional network, interpretability, service classification, service discovery

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Authors: Bastola Narayan, Rajeev Ranjan

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The morphotropic phase boundary (MPB) in the magnetoelectric (1-x)BiFeO3-(x)PbTiO3 has remained a matter of controversy ever since its discovery in 1964. The nature of the phase stabilized (single phase tetragonal or coexistence of tetragonal and rhombohedral phases) is very sensitive to the slight changes in the synthesis conditions. It thus remained an enigma as to what is the essential physical factor which is controlled by the slight difference in the synthesis conditions that finally determines, whether the phase formed will be single phase or coexistence of phases. In this paper, we demonstrate that the nature of the phase stabilized in this system is uniquely dependent on the crystallite size. The system is shown to exhibit features of abnormal grain growth (AGG) during sintering with abrupt increase in the grain size from ~ 1 micron to ~ 10 microns. The 10 micron grains exhibit pure tetragonal phase while the 1 micron grains exhibit coexistence of rhombohedral and tetragonal ferroelectric phases. The Rietveld analysis of powder neutron diffraction shows a paramagnetic to antiferromagnetic order transition inducing with crystalline size reduction from 10 micron to 1 micron. Since tetragonal phase is known to have paramagnetic order and rhombohedral phase has antiferromagnetic order in room temperature, this further strengthens our argument of size induced structure transition.

Keywords: size driven MPB, size driven magnetic ordering, abnormal grain growth, phase formation in BF-PT system

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Authors: Ann Ying-An Chen, Yi-Lun Tsai, Hso-Chi Chaung

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An understanding of pathogens and the immune system has played an utmost important role in agricultural research for the development of vaccinations. The immunological synapse, cell to cell interaction play a crucial role in triggering the body's immune system, such as activation between antigen-presenting cells (APCs) and different subsets of T-cell. If these interactions are regulated appropriately, the host has the ability to defend itself against a wide spectrum of infectious pathogens. The aim of this study is to establish and to characterize a porcine immune synapse system by co-culturing T cell/APC. In this study, blood samples were collected from specific-pathogen-free piglets, and peripheral blood mononuclear cells (PBMC) were separated by using Ficoll-Pague. The PBMC were then stained with CD4 (FITC) and CD25 (PE) antibodies. Different subsets of T cells sorted by fluorescence-activated cell sorting flow cytometer were co-cultured for 24 hrs with alveolar macrophages, and the profiles of cytokine secretion and mRNA transcription levels of Toll-like receptors were examined after. Results showed that the three stages of immune synapse were clearly visible and identified under both transmission and scanning electron microscope (TEM and SEM). The significant interaction differences in toll-like receptor expressions within the co-cultured cell system were observed. The TLR7 mRNA expressions in CD4+CD25- cells were lower than those in CD4+CD25+ and CD4 -CD25+. Interestingly, the IL-10 production levels in CD4+CD25- cells (7.732 pg/mL) were significantly higher than those of CD4+CD25+ (2.636 pg/mL) and CD4 -CD25+ (2.48 pg/mL). These findings demonstrated that a clear understanding of the porcine immune synapse system can contribute greatly for further investigations on the mechanism of T-cell activation, which can benefit in the discovery of potential adjuvant candidate or effective antigen epitopes in the development of vaccinations with high efficacy.

Keywords: antigen-presenting cells, immune synapse, pig, T subsets, toll-like receptor

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Authors: Aliasghar Mehdizadeh Dastjerdi, Francisco Camara Pereira

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The increasing complexity and demand of transport services strains transportation systems especially in urban areas with limited possibilities for building new infrastructure. The solution to this challenge requires changes of travel behavior. One of the proposed means to induce such change is multimodal travel apps. This paper describes a study of the intention to use a real-time multi-modal travel app aimed at motivating travel behavior change in the Greater Copenhagen Region (Denmark) toward promoting sustainable transport options. The proposed app is a multi-faceted smartphone app including both travel information and persuasive strategies such as health and environmental feedback, tailoring travel options, self-monitoring, tunneling users toward green behavior, social networking, nudging and gamification elements. The prospective for mobility management travel apps to stimulate sustainable mobility rests not only on the original and proper employment of the behavior change strategies, but also on explicitly anchoring it on established theoretical constructs from behavioral theories. The theoretical foundation is important because it positively and significantly influences the effectiveness of the system. However, there is a gap in current knowledge regarding the study of mobility-management travel app with support in behavioral theories, which should be explored further. This study addresses this gap by a social cognitive theory‐based examination. However, compare to conventional method in technology adoption research, this study adopts a reverse approach in which the associations between theoretical constructs are explored by Max-Min Hill-Climbing (MMHC) algorithm as a hybrid causal discovery method. A technology-use preference survey was designed to collect data. The survey elicited different groups of variables including (1) three groups of user’s motives for using the app including gain motives (e.g., saving travel time and cost), hedonic motives (e.g., enjoyment) and normative motives (e.g., less travel-related CO2 production), (2) technology-related self-concepts (i.e. technophile attitude) and (3) use Intention of the travel app. The questionnaire items led to the formulation of causal relationships discovery to learn the causal structure of the data. Causal relationships discovery from observational data is a critical challenge and it has applications in different research fields. The estimated causal structure shows that the two constructs of gain motives and technophilia have a causal effect on adoption intention. Likewise, there is a causal relationship from technophilia to both gain and hedonic motives. In line with the findings of the prior studies, it highlights the importance of functional value of the travel app as well as technology self-concept as two important variables for adoption intention. Furthermore, the results indicate the effect of technophile attitude on developing gain and hedonic motives. The causal structure shows hierarchical associations between the three groups of user’s motive. They can be explained by “frustration-regression” principle according to Alderfer's ERG (Existence, Relatedness and Growth) theory of needs meaning that a higher level need remains unfulfilled, a person may regress to lower level needs that appear easier to satisfy. To conclude, this study shows the capability of causal discovery methods to learn the causal structure of theoretical model, and accordingly interpret established associations.

Keywords: travel app, behavior change, persuasive technology, travel information, causality

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Authors: Christina Williams, Mehari Weldemariam, Ann M. Farese, Thomas J. MacVittie, Maureen A. Kane

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Radiation exposure results in dose-dependent and time-dependent multi-organ damage. Drug development of medical countermeasures (MCM) for radiation-induced injury occurs under the FDA Animal Rule because human efficacy studies are not ethical or feasible. The FDA Animal Rule requires the representation of both sexes and describes several uses for biomarkers in MCM drug development studies. Currently, MCMs are limited and there is no FDA-approved biomarker for any radiation injury. Sex as a variable is essential to identifying biomarkers and developing effective MCMs for acute radiation exposure (ARS) and delayed effects of acute radiation exposure (DEARE). These studies aim to address the death of information on sex differences that have not been determined by studies that included only male, single-sex cohorts. Studies have reported differences in radiosensitivity according to sex. As such, biomarker discovery for radiation-induced damage must consider sex as a variable. This study evaluated the plasma proteomic profile of Rhesus macaque non-human primates after different exposures and doses, as well as time points after radiation. Exposures and doses included total body irradiation between 5-7.5 Gy and partial body irradiation with 5% bone marrow sparing at 9, 9.5 and 10 Gy. Timepoints after irradiation included days 1, 3, 60, and 180, which encompassed both acute radiation syndromes and delayed effects of acute radiation exposure. Bottom-up proteomic analyses of plasma included equal numbers of males and females. In the control animals, few proteomic differences are observed between the sexes. In the irradiated animals, there are a few sex differences, with changes mostly consisting of proteins upregulated in the female animals. Multiple canonical pathways were upregulated in irradiated animals relative to the control animals when subjected to pathway analysis, but differential responses between the sexes are limited. These data provide critical baseline differences according to sex and establish sex differences in non-human primate models relevant to drug development of MCM under the FDA Animal Rule.

Keywords: ionizing radiation, sex differences, plasma proteomics, biomarker discovery

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