Miracle-dz/UniFAR-FTUs huggingface dataset:
Zoledronic acid regulates osteoblast differentiation via the mTORC 1 signaling pathway

Zoledronic acid (ZA) exerts an important role in regulating bone metabolism. The purpose of this study was to investigate mechanisms of ZA in terms of their effect on bone formation in vitro. Human osteoblasts (MG63 cell line) were treated with serial concentrations of ZA (0, 10-3, 10-6, 10-9 and 10-12 M). Cell viability, cell apoptosis, alkaline phosphatase (ALP) activity, and expression levels of the marker genes and proteins associated with osteogenic differentiation were evaluated. Signaling from mechanistic target of rapamycin complex 1 (mTORC1) of MG63 cells was detected after ZA treatment, combined with or without rapamycin (mTORC1 inhibitor). Data were statistically analyzed by one-way analysis, with a significance level of 5%. An MTT assay and a flow cytometric Annexin-V/ propidium iodide assay revealed that exposure of MG63 cells to ZA exhibited a significant inhibition of cell viability and induction of apoptosis at concentrations of 10-3 and 10-6 M, but no changes at concentrations of 10-9 and 1012 M (short-time treatment), compared to no drug. In addition, ZA caused an increased expression level of marker genes and proteins associated with osteogenic differentiation and ALP activity at concentrations of 10-9 and 10-12 M, but inhibited osteogenic differentiation at concentrations of 10-3 and 10-6 M, compared to no drug. Furthermore, research into the mechanism responsible showed that low concentrations of ZA induced osteogenic differentiation through preventing mTORC1 activity. In contrast, high concentrations of ZA reduced osteogenic differentiation through promoting mTORC1 activity. Rapamycin, an mTORC1-specific inhibitor, could rescue the defect of osteogenic differentiation caused by exposure to high concentrations of ZA. This in vitro study indicates that ZA-mediated osteoblastic differentiation is mTORC1-dependent. Low concentrations of ZA are better for regulating bone formation via inhibiting mTORC1 activity.
Improving Program Comprehension Through Dynamic Code Analysis

Most of the software that is currently being developed in the industry tends to be very complex and it is safe to assume that a lot of future software projects will keep escalating in their complexity. This means that developers need to keep track of numerous aspects of their system at all time and that new additions to the team will have difficulty adjusting to projects. In this paper, we will propose a set of techniques that combine already existing dynamic code analysis concepts to resolve the aforementioned problems, which can be summarized as being program comprehension difficulties. To do this we introduce a few novel software analysis and visualization techniques that facilitate program comprehension. The approach proposed within this paper allows for easy identification of semantic information, data available at execution time, which might be difficult or even impossible to portray in an easy to understand representation using existing software visualization techniques. During this paper we will be considering traditional object oriented programming languages, however these ideas should be useful in the context of other programming paradigms as well.
Integrating a mesoscopic traffic simulation model and a simplified NO 2 estimation model for predicting the impact of air pollution

Continuous growth in traffic demand has led to a decrease in the air quality in various urban areas. Local authorities in environmental protection and urban planners are therefore interested in performing traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness where necessary. This article is focused on the traffic and air pollution in the eco-neighbourhood “Nancy Grand Cœur”, located in a medium-size city from northeastern France. The main objective of this work is to build an integrated simulation model which would help predicting and visualizing various environmental changes inside the neighbourhood, such as air pollution, traffic flow or meteorological data. Firstly, we build the 3D mesoscopic traffic simulation model around the central train station of the eco-neighbourhood, using real data sets from the local traffic management centre. Secondly, by using reliable data sets from the local air-quality management centre, we build a regression model for predicting the evolution of NO2 concentrations, as a function of the simulated traffic flow and meteorological data. Lastly, we validate the estimated results by comparisons with the real data sets, with the purpose to support the traffic engineering decision-making and the smart city sustainability.
Agentic and communal narcissism and subjective well-being : Are narcissistic individuals unhappy ?

participants and procedure In an effort to examine the relationship between narcissism and SWB, correlational analyses of survey responses obtained from students (n = 138) were conducted. Agentic narcissism was measured using the Narcissistic Personality Inventory (NPI; Raskin & Terry, 1988) and communal narcissism by the Communal Narcissism Inventory (CNI; Gebauer et al., 2012). Subjective well-being measures included the Satisfaction With Life Scale (SWLS; Diener, Emmons, Larsen & Griffin, 1985), Positive And Negative Affect Schedule (PANAS; Watson, Clark & Tegellen, 1988), and the Social Well-being Scale (SWBS; Keyes, 1998). Self-esteem was included in the study in order to examine the potential mediating role of self-esteem in the relationship between narcissism and subjective well-being. results Agentic narcissism was positively related to the affective component of SWB whereas communal narcissism was positively related to the cognitive component of SWB. Both forms of narcissism were positively related to social well-being. All relationships were mediated by the participant’s self-esteem level.
Dynamic Batch Mode Cost-efficient Independent Task Scheduling Scheme in Cloud Computing

Cloud Computing is a paradigm of large scale distributed computing which is a repackaging of various existing concepts/ technologies such as utility computing, grid computing, autonomic computing, virtualization and Internet technologies. Cloud computing uses the Internet technologies for delivery of resources as a service to users on demand. As it is a developing technology, various issues such as resource provisioning, security, energy management and reliability need to be addressed. This work focuses on resource provisioning issue in the context of scheduling schemes adapted by cloud environment. Job scheduling is a very challenging task in cloud computing because of its complex distributed architecture. Many algorithms have been proposed using different scheduling techniques. In this paper a cost-efficient dynamic batch mode scheduling approach has been proposed based on assignment rule. The proposed work is compared with other popular scheduling approaches such as round robin, Min-Min, and Max-Min and the results show that our approach improves performance of the system by means of reducing the cost of running all jobs and reducing the average Virtual Machine (VM) utilization. CloudSim, a simulation tool has been used.
Using Speech Production Knowledge for Raw Waveform Modelling Based Styrian Dialect Identification

This paper addresses the Styrian Dialect sub-challenge of the INTERSPEECH 2019 Computational Paralinguistics Challenge. We treat this challenge as dialect identification with no linguistic resources/knowledge and with limited acoustic resources, and develop end-to-end raw waveform modelling based methods that incorporate knowledge related to speech production. In this direction, we investigate two methods: (a) modelling the signals after source system decomposition and (b) transferring knowledge from articulatory feature models trained on English language. Our investigations show that the proposed approaches on the ComParE 2019 Styrian dialect data yield systems that perform better than low level descriptorbased and bag-of-audio-word representation based approaches and comparable to sequence-to-sequence auto-encoder based approach.
Chapter 2 . 3 Mutations in a TGFß ligand , TGFB 3 , cause syndromic aortic aneurysms and dissections Mutations in TGFB 3 cause syndromic aortic aneurysms and dissections

Background Aneurysms affecting the aorta are a common condition associated with high mortality due to aortic dissection or rupture. Investigations of the pathogenic mechanisms involved in syndromic types of thoracic aortic aneurysms such as Marfan and Loeys-Dietz syndromes, have revealed an important contribution of disturbed TGFβ signaling. Objective To discover a novel gene causing syndromic aortic aneurysms in order to unravel the underlying pathogenesis. Methods We combined genome wide linkage analysis, exome sequencing and candidate gene Sanger sequencing in a total of 470 index cases with thoracic aortic aneurysms. Extensive cardiologic examination, including physical examination, electrocardiography and transthoracic echocardiography was performed. In adults, imaging of the entire aorta using computed tomography or magnetic resonance imaging was done. Results Here we report on 43 patients from 11 families with syndromic presentations of aortic aneurysms caused by TGFB3 mutations. We demonstrate that TGFB3 mutations are associated with significant cardiovascular involvement, including thoracic/abdominal aortic aneurysm and dissection and mitral valve disease. Other systemic features overlap clinically with Loeys-Dietz, Shprintzen-Goldberg and Marfan syndrome, including cleft palate, bifid uvula, skeletal overgrowth, cervical spine instability and club foot deformity. In line with previous observations in aortic wall tissues of patients with mutations in effectors of TGFβ signaling (TGFBR1/2, SMAD3 and TGFB2), we confirm a paradoxical upregulation of both canonical and non-canonical TGFβ signaling in association with upregulation of expression of TGFβ ligands. Conclusions Our findings emphasize the broad clinical variability associated with TGFB3 mutations and highlight the importance of early recognition of the disease due to high cardiovascular risk. 2 Erasmus Medical Center Rotterdam
PERPETUATION , TRANSMISSION AND MANAGEMENT OF CURVULARIA LEAF SPOT OF GLADIOLUS

Gladiolus (Gladiolus grandiflorus L.) called the “Queen of bulbous flower crops” is grown in many parts of the world (Kaikal and Nauriyal, 1964). The word gladiolus originally coined by Pliny the Elder (AD 23-79) from the Latin word ‘Gladius’ meaning ‘Sword’. So, it is also known as ‘sword lily’ on account of the shape of its leaves (Randhawa and Mukhopadhyay, 1986). In Europe, it commonly called as ‘corn flag’ (Bose and Yadav, 1989). Gladiolus spp. were recognized over 2000 years ago, growing in the fields of Asia minor and were called as ‘corn lilies’ (Wilfret, 1980). Gladiolus introduced into cultivation at the end of sixteenth century, while it is of comparatively a recent introduction in India. Gladiolus is indeed a boon to a florist. The early-sown gladioli sell at a premium price in the market but have been found suffer from very high incidence of Curvularia leaf spot in the North Indian plains due to high temperature and humidity prevailing at that time. The disease recorded in India in 1967 from the National Botanical Garden, Lucknow, Uttar Pradesh (Singh, 1968). It causes severe spotting of the stems, leaves, spikes, flowers and even the corms. The characteristic spots are circular and elongated on the leaves, irregular on the spikes. The colour of the spot is tan, surrounded by a darker reddish brown ring and an outer yellow halo. Black spore masses often seen on the central area of the spot. The brown or dead area is sunken.
Inverse method to characterize " local " and " non-local " absorbing materials submitted to a shear grazing flow

As aircraft traffic is constantly increasing, serious efforts are made to find ways to reduce noise produced by the engines. Among them, the design of performing absorbing materials, called liners, placed on the nacelle’s walls.ONERA has developed an "impedance" eduction method (code "Elvin") applied to materials with "local reaction" in the presence of shear grazing flow. The inverse process is based on wall acoustic pressure or velocity fields acquired by Laser Doppler Velocimetry (LDV) above the liner. Computations rely on the resolution of the 2D linearized Euler equations in the harmonic domain, spatially discretized by a discontinuous Galerkin scheme, which presents advantageous properties. First, it is weakly dispersive and dissipative. In addition, boundary conditions are imposed through fluxes, which is particularly robust and straightforward. The minimization of the objective function is achieved by the resolution, at each iteration on the liner impedance, of the direct and adjoint equations. After a description of the architecture and current features of Elvin code, configurations of "linear" and "non-linear" liners are tested with the corresponding impedance eduction method from data measured in Onera aeroacoustic bench (B2A) or NASA flow ducts. Values of objective function are analysed in the impedance map to evaluate standard deviation associated to identified impedance. Then, the procedure to extend the code to open-cell porous media instead of "local reaction" liners is shown. This implies the integration of a computation domain in which acoustic propagation equations are solved in the media. The objective is to extract the macroscopic parameters governing viscous dissipation of sound waves in porous media, from Biot theory or derived theories : open porosity, static flow resistivity, geometrical tortuosity, thermal and viscous characteristic dimensions. A first validation of direct equations is finally presented in impedance tube configuration (without flow) with implementation of Delany-Bazley’s approach.
Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach

There are many dynamic events like new order arrivals, machine breakdowns, changes in due dates, order cancellations, arrival of urgent orders etc. that makes static scheduling approaches very difficult. A dynamic scheduling strategy should be adopted under such production circumstances. In the present study an event driven dynamic job shop scheduling mechanism under machine capacity constraints is proposed. The proposed method makes use of the greedy randomised adaptive search procedure (GRASP) by also taking into account orders due dates and sequence-dependent set-up times. Moreover, order acceptance/rejection decision and Order Review Release mechanism are integrated with scheduling decision in order to meet customer due date requirements while attempting to execute capacity adjustments. We employed a goal programming-based logic which is used to evaluate four objectives: mean tardiness, schedule unstability, makespan and mean flow time. Benchmark problems including number of orders, number of machines and different dynamic events are generated. In addition to event-driven rescheduling strategy, a periodic rescheduling strategy is also devised and both strategies are compared for different problems. Experimental studies are performed to evaluate effectiveness of the proposed method. Obtained results have proved that the proposed method is a feasible approach for rescheduling problems under dynamic environments.
Comparison of T-matched and Double T-matched Short Dipole Tag Antennas for UHF RFID Systems ↕ 2 lines space

↕ 2 lines space Abstract: The impact of impedance uncertainties to the power transfer between complex source and load impedances is investigated. An exact lower bound for the power transmission coefficient under given source and load uncertainties is derived. Presented analysis is applied in design verification of Tand double T-matched short dipole tag antennas for UHF RFID systems and the performance of these two tag antennas is compared. ↕ 1 line space
Capacity Improved Arabic Text Steganography Technique Utilizing ‘ Kashida ’ with Whitespaces Safia

Hiding sensitive text to fully prevent detection of its secrecy is a technique called Steganography. This work of Arabic text steganography focuses on improving the hiding secrets within Arabic language text utilizing the redundant extension “Kashida” character as covering media. We propose modifying the “Kashida” stego-cover technique by embedding sensitive data within whitespaces in a new way. This modification was tested on hiding data within Arabic text of the last 30 Chapters of the Holy Quran (Sura Al-Buruj #85 to Sura An-Nas #114) comparing both the normal method and the improved proposed one. The results demonstrated clear increase in the capacity as expected without degrading the security, which is justifying this work for real promising research direction.
A Model Predictive Control Scheme for Intermodal Autonomous Mobility-on-Demand

This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers’ travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.
Identification of fatty acids composition and lipid content from liver and muscle tissues of Tenualosa ilisha in the Persian Gulf coasts ( Bandar Khorramshahr )

In the present study, the liver and muscle tissues of Tenualosa ilisha from the coastal waters of Bandar Khorramshahr, Iran in Mar 2013 were separately extracted for their fatty acids composition and lipid content using the method of Blight & Dyer. The compounds were determined by Gas Chromatography-Mass Spectrometry (GCMS). The components detected in the liver and muscle tissues, include saturated fatty acids Palmitic acid and Stearic acid, monounsaturated fatty acid Oleic acid, polyunsaturated fatty acids Docosahexaenoic acid (DHA) and (PUFA) Eicosapentaenoic acid (EPA), two methyl esters of fatty acids including Octadecanoic acid, methyl ester and Hexadecanoic acid, methyl ester ,Cholesterol and Alkane including, Heptadecane and Octadecane.The results showed that the dominant fatty acids in liver and muscle tissues were Omega-3 fatty acids Docosahexaenoic acid (DHA) and Eicosapentaenoic acid (EPA). [Rustaiyan A, Samiee K, Morady F. Identification of fatty acids composition and lipid content from liver and muscle tissues of Tenualosa ilisha in the Persian Gulf coasts (Bandar Khorramshahr). Life Sci J 2013;10(7s):795-798] (ISSN:1097-8135). http://www.lifesciencesite. com. 127
Modeling and Analysis of Complex Systems : Nature and Society

Today our planet is facing a serious problem of climate change caused by rise in its average temperature due to discharge of global warming gases from various industrial and other sources leading to undesirable consequences such as melting of glaciers, see level rise, etc. The carbon dioxide is one of the most important greenhouse gases responsible for climate change. Therefore, it is necessary to find a mechanism by which the global warming gases such as carbon dioxide from the atmosphere can be removed. In view of this, in this paper, a nonlinear mathematical model is proposed and analyzed to suggest a mechanism for the removal of carbon dioxide by spraying external species such as liquid species and particulate matters in the atmosphere above the source of emissions. When these externally sprayed species, interact with the gas, the secondary phases are formed which then get removed from the atmosphere by gravity. The dynamics of the system in such a case is assumed to be governed by five nonlinearly interacting dependent variables, namely, the concentration of carbon dioxide, the concentrations of externally sprayed liquid species and solid particulate matters, the concentration of carbon dioxide absorbed in the liquid phase and the concentration of carbon dioxide transformed by the solid particulate matters forming secondary species. The model analysis shows that the concentration of global warming gas, 2 CO , decreases as the rates of spray of liquid phase and solid particulate matters increase in the atmosphere. It is noted that this gas can be removed almost completely from the atmosphere, if the rates of spray of these external species are very large. The numerical simulation confirms these analytical results.
Computational Detection of Irony in Textual Messages

Analyzing user generated content in social media, using natural language processing methods, involves facing problematic rhetorical devices, such as sarcasm and irony. These devices can cause wrong responses from review summarization systems, sentiment classiﬁers, review ranking systems, or any kind of application dealing with the semantics and the pragmatics of text. Studies to improve the task of detecting textual irony have mostly focused mostly on simple linguistic cues, intrinsic to the text itself, although these have been insuﬃcient in detecting ironic sentences that have no intrinsic clues. However, a new approach for classifying text, which can be made to explore external information (e.g. in the form of pre-trained word embeddings) has been emerging, making use of neural networks with multiple layers of data processing. This dissertation focuses on experimenting with diﬀerent neural network architectures for addressing the problem of detecting irony in text, reporting on experiments with diﬀerent datasets, from diﬀerent domains. The re-sults from the experiments show that neural networks are able to outperform standard machine learning classiﬁers on the task of irony detection, on diﬀerent domains and particularly when using diﬀer-ent combinations of word embeddings.