Miracle-dz/UniFAR-FTUs huggingface dataset:
The mechanical regulation of RNA binding protein hnRNPC in the failing heart

Cardiac pathologies are characterized by intense remodeling of the extracellular matrix (ECM) that eventually leads to heart failure. Cardiomyocytes respond to the ensuing biomechanical stress by re-expressing fetal contractile proteins via transcriptional and post-transcriptional processes, like alternative splicing (AS). Here, we demonstrate that the heterogeneous nuclear ribonucleoprotein C (hnRNPC) is upregulated and relocates to the sarcomeric Z-disk upon ECM pathological remodeling. We show that this is an active site of localized translation, where the ribonucleoprotein associates to the translation machinery. Alterations in hnRNPC expression and localization can be mechanically determined and affect the AS of numerous mRNAs involved in mechanotransduction and cardiovascular diseases, like Hippo pathway effector YAP1. We propose that cardiac ECM remodeling serves as a switch in RNA metabolism by impacting an associated regulatory protein of the spliceosome apparatus. These findings offer new insights on the mechanism of mRNAs homeostasis mechanoregulation in pathological conditions.
CliniCal experienCe impaCt students ’ CapaCity to diagnose dentoalveolar disorders using intraoral radiography ?

Introduction: Mastery of interpretation of radiographic images is a contributing factor in correct diagnosis of conditions affecting the dentoalveolar apparatus. It is therefore essential that students’ perfect these skills while studying for their degrees. Once they embark upon their professional careers outside of the university setting, it is expected that the theoretical knowledge and clinical experience accumulated during the course of their studies will have led to improved diagnostic performance. Objective: The objective of this study was to evaluate diagnoses made using intraoral radiographs by undergraduate students at a School of Dentistry in the South of Brazil, before and after their introduction to clinical practice. Materials and methods: Diagnoses made during the second (T0) and fourth (T1) years of the undergraduate course in dentistry using interproximal and periapical radiographs were analyzed. Descriptive statistics were calculated and the McNemar test was used to compare answers at T0 and T1. Results: Fifteen students answered questionnaires at both T0 and T1. The overall percentage of correct answers was 70.5% at T0 and 61.6% at T1, with a significant difference (p = 0.024). Discussion: This study highlights the need for educational strategies that improve diagnostic competence during undergraduate clinical activities, since radiographic examinations are essential in all areas of dentistry. Conclusions: Therefore, it was concluded that the rate of correct diagnosis of dentoalveolar disorders by intraoral radiography decreased significantly after the initial years of clinical training.
RISC-V AMS VP: An Open Source Evaluation Platform for Cyber-Physical Systems

Recently, Virtual Prototypes (VPs) implemented in SystemC TLM (Transaction-Level Modeling) have been introduced into the growing RISC-V ecosystem to facilitate early software development and testing. However, accurate environment modeling, which is crucial for Cyber-Physical Systems (CPS), has been mostly neglected to this point. Thus, in this paper, we propose the RISC-V AMS VP framework, that combines an existing open source RISC-V VP with the SystemC AMS (Analog/Mixed Signal) environment modeling style to obtain a RISC-V evaluation platform tailored for CPS. As a case study we created a temperature control system that integrates a sensor and heater component together with a control software. Moreover, we present results on an exemplary fault-injection evaluation that is enabled by bringing together software, hardware and environment models in our unified RISC-V AMS VP framework. Finally, we provide the RISC-V AMS VP framework together with the temperature control system as open source to stimulate further research and as foundation for educational purposes.
Outdoor photoluminescence imaging of photovoltaic modules with sunlight excitation

To operate photovoltaic power plants at maximum capacity, it is desirable to identify cell or module failures in the field at the earliest possible stage. Currently used field inspection methods cannot detect many of the electronic defects that can be revealed with luminescence‐based techniques. In this work, photoluminescence images are acquired using the sun as the sole illumination source by separating the weak luminescence signal from the much stronger ambient sunlight signal. This is done by using an appropriate choice of optical filtering and modulation of the cells' bias between the normal operating point and open circuit condition. The switching is achieved by periodically changing the optical generation rate of at least one cell within the module. This changes the biasing condition of all other cells that are connected to the same bypass diode. This method has the advantage that it can deliver high quality images revealing electrical defects in individual cells and entire modules, without requiring any changes to the electrical connections of the photovoltaic system.
Lane detection and object detection system steering advisory for driver

— Lane detection and tracking modules are now regarded essential in the development of any Intelligent Transportation System (ITS). Different vision-based algorithms are being used in autonomous vehicles to determine road lanes are presented, reviewed, and compared in this proposed work. The main components are lane departure, lane tracking, collision avoidance, driver assistance system. Lane departure component assists in keeping the vehicle stable on the desired lane on the road. The front collision avoidance component will detect the road's frontal obstruction and shows up a pre-collision/proximity warning signal. The notice is based on the vehicle's speed and the object's distance from the cars. According to current research, the system can only identify one lane marking set in real time and is unable to provide extra lanes for assistance in scenarios such as lane closures, road repairs, and car accidents that impede the driving lane. The project proposes a method for determining the marking of two lanes that might be used in conjunction with a detecting system to detect obstructions on the road ahead of the vehicle. This method takes a strong approach to lane detection and performs admirably in a variety of lane types.
Secure & Transparent E-voting with Block-Chain

: Electronic voting or e-voting have been used in various ways over the decades, along with basic advantages over paper-based systems such as increasing efficiency and reducing error. However, the E-voting schemes bring issues mainly relating to security, credibility, transparency, reliability, and functionality which remain challenges to achieve widespread adoption of such systems especially regarding improving their resilience against potential faults. Blockchain is a distinct technology of our time that has the potential to improve the overall resilience of e-voting systems. The basic principle of the blockchain is in the immutability of the records already placed in blocks and advanced cryptography is used to ensure the chaining of blocks, and providing data integrity. This study represents an effort to leverage blockchain benefits such as cryptography and transparency to achieve an efficient voting model. Moreover, we present sec-vote, a blockchain-based voting system to bring accessibility with easy access to the web portal through the internet, reliability with face recognition and OTP, transparency with blockchain, security with hashing and cryptographic algorithms, and reduce the cost of the election
Private Platform for Teaching Blockchain at the Undergraduate Level

Private Platform for Teaching Blockchain at the Undergraduate Level Dr. Emil H Salib, James Madison University Professor in the College of Integrated Science and Engineering at James Madison University. Current Teaching Networking & Security, Introductory Programming and Cross Platform Mobile Application Development. Current Research Private Cloud Computing, Private Cellular Networking & Security, Mobile IPv6 and Design for Motivation Curriculum.
Dissuasive and biocidal activity of Salvia officinalis (Lamiaceae) with induction of malformations in Aedes aegypti (Diptera: Culicidae) / Actividad disuasiva y biocida de Salvia officinalis con inducción de malformaciones en Aedes aegypti (Diptera: Culicidae)

Different studies have evaluated the biocidal activity of essential oils (EO) in mosquito larvae of medical importance. However, limited research focused on analyzing the EO effect at all the stages of the life cycle of Aedes aegypti has been done. This study evaluates the biological activity of the EO Salvia officinalis against Ae. aegypti. The ovicidal activity was evaluated at 1, 5, 37 and 50 mg.L-1 concentrations in eggs from 0-12 h and eggs from 0-72 h. Larvicidal, pupicidal and adulticidal activities were assessed at exploratory concentrations (EC) and multiple concentrations. We employed an EC of 1,000 mg.L-1 for repellent activity and used an exposure time of 0-2 min and 2-15 min on the forearms of volunteers. The deterrent action was estimated at EC of 5, 50 and 200 mg.L-1. The EO caused malformations in embryos as well as larvae alteration. The highest larvicidal activity was at 63 and 76 mg.L-1 (27 ± 13.4 and 37 ± 18.6 %) with 24 h exposure. The greatest pupicidal mortality was at 310 and 390 mg.L-1 (89 ± 1.53 and 100 ± 0 %) with 48 h exposure. Adulticidal mortality at 300 mg.L-1 was 57.5 ± 0 % and the percentage of repellency was 42 ± 4.7 %. The dissuasive action at 200 mg.L-1 was 97 ± 4.81 %, with an oviposition activity rate of -0.94. S. officinalis showed a biocidal effect on embryos and mortality of pupae and adults, indicating that its potential use in control programs should be focused on these stages of development.
Дніпровський національний університет імені Олеся Гончара Днепровский национальный университет имени Олеся Гончара PROFESSIONAL TRAINING OF FOREIGN LANGUAGE TEACHERS: TODAY’S GLOBAL REALITIES ОСОБЛИВОСТІ ПРОФЕСІЙНОЇ ПІДГОТОВКИ ВЧИТЕЛІВ ІНОЗЕМНИХ МОВ: ГЛОБАЛЬНІ РЕАЛІЇ СУЧАСНОСТІ ОСОБЕННОСТИ ПР

This paper addresses to common problems of training a generation of students with clip thinking, fluent in IT-technologies and modern gadgets. This generation grew up surrounded by constantly improving digital technologies, accompanied by the media, which constantly provides the viewer with information mainly of entertaining or intimidating nature (talk shows, sitcoms, TV shows, contests, games, constantly interrupted by commercials, news, etc.). As a result, the brain is rebuilt, adapting to reality, and a new type of thinking arises – the socalled ‘Clip thinking’, which is characterized by fragmentation, non-linearity, emotionality, quick switching from one topic to another, illogicality. In accordance with this, the features of students’ thinking, over the years of rapid technological progress and the development of the media, have acquired new characteristics. Unlike the previous generation with digital thinking, which is characterized by rationality, ability to think algorithmically and multitasking, students with clip thinking need new approaches in teaching and preparing them for future professional activities. Given the above, it is necessary to create a new teaching technology that will contribute to the quality training of a new generation of future foreign language teachers.
Exploring Copysets under Repeated Failures

For our final project, we present a reproduction and extension of one of the results from the paper “ Copysets: Reducing the Frequency of Data Loss in Cloud Storage ” by Asaf Cidon et al [2]. First, we present a recreation and limited simulated reproduction of Figure 6, which shows the probability of data loss when 1% of the nodes in a cluster fail due to correlated failures (e.g. power loss) under different replication schemes. Additionally, we highlight a mistake we discovered in one of the equations used to generate Figure 6 in the original work. Next, we present an extension to the research, exploring the probability of data loss under repeated failure events. Our findings show that Copyset Replication parameters that yield high probability of data loss with single failures can perform better than those that yield low probability of data loss with single failures, when repeated failures are introduced at a short time interval.
Effective Content Based Image Retrieval Technique using Color and Texture Features

To find an image from large database is implemented by CBIR technique. An image has three main features such as color, texture and shape. The proposed scheme is effectively searching images by using feature extraction. Dominant color descriptor is used in image indexing and dominant color extraction. By using Gray Level Co-occurrence Matrix (GLCM), texture of an image is obtained. Color and texture features are normalized. Using Gradient Vector Flow fields, shape information is captured in terms of edge images computed. A robust feature set for image retrieval is provided by using the combination of the color, shape and texture features. In retrieving the similar images, weighted Euclidean distance of color, texture and shape features are used. To classify relevant and irrelevant images in this retrieval process, support vector machine is used. This technique is simply displayed relevant images that are similar to a query image.
Big Data and Democracy 1

One of the biggest questions in the digital age pertains to the relevance of democracy in the era of big data. Unquestioningly, the digital revolution is growing at a rapid pace and many are being caught unaware by its impact in various avenues. The amount of data available has been doubling year on year, and the conditions of usage have been evolving at a pace faster than the policies are being provided to ensure proper usage. Many have started looking for ways of turning big data into big money, an aspect that is coming at the expense of the democratic values countries have upheld for generations. Instead, it is becoming a huge problem as the history of humankind is becoming more documented now and messages are easier to send compared to a century ago. Understanding the impact of big data on democracy can help accentuate the best way of improving democratic institutions and their ability to overcome the pressure coming from evolving technology.
Effects of Pseudotime Uncertainty in Downstream Analysis for Single-cell Data: A Case Study

The analysis of high-throughput single-cell genomics data creates opportunities to uncover the temporal dynamics of cell populations undergoing some complex biological processes such as differentiation and cell division, where the development of bulk-based time course experiments is challenging or technically impossible. One popular approach is to learn a lower-dimensional manifold or trajectory called pseudotime through the data capturing major sources of variation. Most of the current approaches of trajectory inference only provide a point estimate of pseudotime, concealing the consequence of inherent biological and technical noise in single-cell data. Therefore, the intrinsic uncertainty in pseudotime and trajectory inference is not propagated to the downstream and its effects remain unknown. Here three commonly used pseudotime and trajectory inference methods have been discussed along with an important downstream analysis of trajectory inference, i.e. identiﬁcation of gene-speciﬁc branching locations. The alteration of gene-speciﬁc branching locations in downstream is illustrated for different pseudotime and trajectory inference methods used in upstream. Finally, A set of recommendations is made that the downstream results or hypotheses should be conﬁrmed using several pseudotime and trajectory inference methods.
Stress at Interface of SiO2/4H-SiC Studied by Confocal Raman Microscopy

Stress at SiO2/4H-SiC interface was investigated by using confocal Raman microscopy technique. The residual strain, which was reflected by Raman shift, was found in the HF etched samples with remained SiO2 thickness above 10 nm. This suggests that stress exists at SiO2/4HSiC interface. Then, the stress was calculated for four types of nitric oxide (NO) treated samples. Since the calculated stresses have similar values, the results show that the NO post-annealing has no drastic effect in the relaxation of stress at SiO2/4H-SiC interface.
Reducing the Manual Annotation Effort for Handwriting Recognition Using Active Transfer Learning

Reducing the Manual Annotation Effort for Handwriting Recognition Using Active Transfer Learning Eric Burdett Department of Computer Science, BYU Master of Science Handwriting recognition systems have achieved remarkable performance over the past several years with the advent of deep neural networks. For high-quality recognition, these models require large amounts of labeled training data, which can be difficult to obtain. Various methods to reduce this effort have been proposed in the realms of active and transfer learning, but not in combination. We propose a framework for fitting new handwriting recognition models that joins active and transfer learning into a unified framework. Empirical results show the superiority of our method compared to traditional active learning, transfer learning, or standard supervised training schemes.
Exploring Word Usage Change with Continuously Evolving Embeddings

The usage of individual words can change over time, for example, when words experience a semantic shift. As text datasets generally comprise documents that were collected over a longer period of time, examining word usage changes in a corpus can often reveal interesting patterns. In this paper, we introduce a simple and intuitive way to track word usage changes via continuously evolving embeddings, computed as a weighted running average of transformer-based contextualized embeddings. We demonstrate our approach on a corpus of recent New York Times article snippets and provide code for an easy to use web app to conveniently explore semantic shifts with interactive plots.
TRUTH AS A SOCIOCULTURAL PHENOMENON: MODERN INTERPRETATION

О б р а з е ц ц и т и р о в а н и я: Волнистая МГ, Коркия ЭД, Мамедов АК. Истина как социокультурное явление: современная интерпретация. Журнал Белорусского государственного университета. Социология. 2020;4:14 –22 (на англ.). https://doi.org/10.33581/2521-6821-2020-4-14-22 F o r c i t a t i o n: Volnistaya MG, Korkiya ED, Mamedov AK. Truth as a sociocultural phenomenon: modern interpretation. Journal of the Belarusian State University. Sociology. 2020;4: 14–22. https://doi.org/10.33581/2521-6821-2020-4-14-22
Low-Power Design Methodology of Voltage Over-Scalable Circuit with Critical Path Isolation and Bit-Width Scaling

SUMMARY This work proposes a design methodology that saves the powerdissipationundervoltageover-scaling(VOS)operation. Thekeyidea of the proposed design methodology is to combine critical path isolation (CPI) and bit-width scaling (BWS) under the constraint of computational quality, e.g., Peak Signal-to-Noise Ratio (PSNR) in the image processing domain. Conventional CPI inherently cannot reduce the delay of intrinsic criticalpaths(CPs), whichmaysigniﬁcantlyrestrictthepowersavingeﬀect. On the other hand, the proposed methodology tries to reduce both intrinsic and non-intrinsic CPs. Therefore, our design dramatically reduces the supply voltage and power dissipation while satisfying the quality constraint. Moreover, for reducing co-design exploration space, the proposed methodology utilizes the exclusiveness of the paths targeted by CPI and BWS, where CPI aims at reducing the minimum supply voltage of non-intrinsic CP, and BWS focuses on intrinsic CPs in arithmetic units. From this key exclusiveness, the proposed design splits the simultaneous optimization problem into three sub-problems; (1) the determination of bit-width reduction, (2) the timing optimization for non-intrinsic CPs, and (3) investigating the minimum supply voltage of the BWS and CPI-applied circuit under quality constraint, for reducing power dissipation. Thanks to the problem splitting, the proposed methodology can eﬃciently ﬁnd quality-constrained minimum-power design. Evaluation results show that CPI and BWS are highly compatible, and they signiﬁcantly enhance the eﬃcacy of VOS. In a case study of a GPGPU processor, the proposed design saves the power dissipation by 42.7% with an image processing workload and by 51.2% with a neural network inference workload.
Prebiotic, Probiotic, and Synbiotic Consumption Alter Behavioral Variables and Intestinal Permeability and Microbiota in BTBR Mice

Given that prebiotics have been shown to improve gut microbiota composition, gastrointestinal symptoms and select behaviors in autism spectrum disorder (ASD), we hypothesized that prebiotic supplementation would improve sociability, communication, and repetitive behaviors in a murine model of ASD. We also examined the effect of a synbiotic (probiotic + prebiotic). Juvenile male BTBR mice were randomized to: (1) control; (2) probiotic (1 × 1010 CFU/d Lactobacillus reuteri RC-14®; now known as Limosilactobacillus reuteri); (3) prebiotic (10% oligofructose-enriched inulin); (4) prebiotic + probiotic (n = 12/group) administered through food for 3 weeks. Sociability, communication, repetitive behavior, intestinal permeability and gut microbiota were assessed. Probiotic and symbiotic treatments improved sociability (92 s and 70 s longer in stranger than empty chamber) and repetitive behaviors (50% lower frequency), whereas prebiotic intake worsened sociability (82 s less in stranger chamber) and increased the total time spent self-grooming (96 s vs. 80 s CTR), but improved communication variables (4.6 ms longer call duration and 4 s higher total syllable activity). Mice consuming probiotics or synbiotics had lower intestinal permeability (30% and 15% lower than CTR). Prebiotic, probiotic, and symbiotic treatments shifted gut microbiota to taxa associated with improved gut health. L.reuteri may help alleviate ASD behavioral symptom severity and improve gut health. The potential use of prebiotics in an ASD population warrants further research.
Students’ approaches while solving a non-typical geometrical problem

In the paper we present the results of two teaching episodes, which took place in two middle school classes with 13and 14-year-old students. The students in both classes were asked to solve the same geometrical problem; then a discussion followed, in which they had to justify their solutions. In both cases the students had no prior experience in solving non-typical mathematical problems. Additionally, the students were asked to justify their answers, which is not a common characteristic of a ‘typical’ mathematics classroom at that level. The problem was chosen from a wider study, in which twenty classes from twenty different schools were analysed. One of the aims of the present study was to analyse the skills that require a deeper understanding of mathematical concepts and properties. Particularly, we aimed to investigate students’ different solution methods and justifications during problem solving. The results show considerable differences among the two classes, not only concerning the depth of investigating (which was expected due to the different age groups), but also concerning the relationship between achievement (as assessed by the mathematics teacher) and success in solving the problem. These results demonstrate the need for re-directing mathematics education from a pure algorithmic to a deeper thinking approach.