Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of intramembrane charged residues in the quality control of unassembled T-cell receptor α-chains at the endoplasmic reticulum

Endoplasmic reticulum (ER)-associated degradation (ERAD) of unassembled T-cell receptor α-chain (TCRα) is reliant on the presence of two basic residues in the transmembrane (TM) segment of TCRα. The precise role of these residues in ER quality control is unclear. Here, we show that a TCRα mutant lacking these intramembrane charged residues has a tendency to form homooligomers through an interchain disulfide bond that involves a specific pair of cysteine residues. Covalent oligomerization of TCRα appears to stabilize it at the ER membrane. The presence of a single lysine residue at specific positions within the TCRα TM domain abolishes its oligomerization and causes its rapid degradation. Conversely, when TCRα oligomerization is induced by a bivalent compound, the degradation of TCRα is inhibited. Together, these results suggest that the intramembrane charged residues in TCRα do not function as a signal for substrate recognition in ERAD. Instead, their primary role is to reduce TCRα oligomerization, maintaining it in a retrotranslocation-competent state. Our results also suggest that the ERAD machinery is inefficient when coping with oligomerized substrates, indicating a requirement for chaperone-mediated protein disassembly in the ER lumen prior to retrotranslocation.
The correction method of air velocity transducer in underground mine roadway

As the air velocity is unevenly distributed in the underground mine roadway section, the hanging position and correction method of air velocity transducer have seriously affect the monitoring accuracy. In order to improve the air velocity monitoring accuracy, this paper ascertains 4 main impacted factors including section shape, section height, section width and average value of air velocity. Combining with the computational fluid simulation technology, the orthogonal test method and the dimensionless approach, the distribution of air velocity are analyzed in the vertical midline of the roadway section. The applications show that the best hanging position of air velocity transducer is the 0.8 times relative height from the bottom border, and the correction formula is ν' = 0.9573 ν + 0.0027. It can provide the protection for realizing the automation of mine ventilation optimization and control.
A distribution-based clustering algorithm for mining in large spatial databases

The problem of detecting clusters of points belonging to a spatial point process arises in many applications. In this paper, we introduce the new clustering algorithm DBCLASD (Distribution-Based Clustering of LArge Spatial Databases) to discover clusters of this type. The results of experiments demonstrate that DBCLASD, contrary to partitioning algorithms such as CLARANS (Clustering Large Applications based on RANdomized Search), discovers clusters of arbitrary shape. Furthermore, DBCLASD does not require any input parameters, in contrast to the clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) requiring two input parameters, which may be difficult to provide for large databases. In terms of efficiency, DBCLASD is between CLARANS and DBSCAN, close to DBSCAN. Thus, the efficiency of DBCLASD on large spatial databases is very attractive when considering its nonparametric nature and its good quality for clusters of arbitrary shape.
Network anomaly detection in time series using distance based outlier detection with cluster density analysis

It is common place in any organizational environment that data stored internally does not necessarily belong to the company storing the data. In such cases, keeping this data secured is of critical importance. If such data is compromised, it can lead to devastating effects on both the public image of the organization and the relations between said company and its business partners. To combat this surge in malicious activity in recent years, research has focused on using anomaly detection techniques to detect possible malicious activity on a network. This paper proposes an evolution of the MCOD (Micro-Clustering Outlier Detection) machine learning algorithm. Designed to implement a time-series approach along with using both distance based outlier detection and cluster density analysis, we analysis the results of this algorithm on real-world data.
Using An Instrumentation based Approach to Detect Inter-Component Leaks in Android Apps

The success of the Android OS in its user base as well as in its developer base can partly be attributed to its communication model, named Inter-Component Communication (ICC), which promotes the development of loosely-coupled applications. By dividing applications into components that can exchange data within a single application and across several applications, Android encourages software reuse, and thus reduces developer burden. Unfortunately, the ICC model, which provides a message passing mechanism for data exchange among components, can be misused by malicious apps to threaten user privacy. Indeed, researchers have shown that Android apps frequently send users private data outside the device without their prior consent. Those applications are said to leak private data [2]. However, there is still a lack of a comprehensive study on the characteristics of the usage of ICCs by Android malware. Typically, what is the extent of the presence of privacy leaks in Android malware? To answer such a question, an Android analysis tool has to be developed for tracking privacy leaks. Although, most of the privacy leaks are simple, i.e., easily identifiable as they operate within a single component. Thus, analyzing components separately is not enough to detect leaks: it is necessary to perform an inter-component analysis of applications. Android app analysts could leverage such a tool to identify malicious apps that leak private data. For the tool to be useful, it has to be highly precise and minimize the false positive rate when reporting applications leaking private data. Thus, we propose IccTA1 , an Inter-component communication Taint Analysis tool, for a sound and precise detection of ICC links and leaks. Although our approach is generic and can be used for any data-flow analysis, we focus in this paper on using IccTA to detect ICC-based privacy leaks. we test IccTA on 15,000 real-world apps randomly selected from Google Play market in which we detect 337 apps with 2,395 ICC leaks. We also launch IccTA on the MalGenome [5] set containing 1260 malware, where IccTA reports 108 apps with 534 ICC leaks. By comparing the detecting rate r = # of detected apps # of tested apps of the two data sets, we found that rMalGenome = 8.6% is much higher than rGoogleP lay = 2.2%. Thus, we can conclude that malware are using ICC to leak private data more than benign apps, making ICC a potential feature for malware 1 https://sites.google. com/site/icctawebpage/ detection. This paper is an extended abstract version of our research paper [3], where interested readers can find more details of this work.
Apprentissage interactif pour la recherche par le contenu dans les bases multimédias. (Interactive Learning for Content-Based Retrieval in Multimedia Databases)

Les bases actuelles de donnees multimedia necessitent des outils de plus en plus avances pour pouvoir etre parcourues avec efficacite. Dans ce contexte, la recherche en interaction avec un utilisateur est une approche qui permet de resoudre des requetes a la semantique complexe avec rapidite, sans pour autant necessiter un haut niveau d'expertise utilisateur. Parmi les differents elements intervenant dans la conception d'un systeme de recherche interactive, deux parties essentielles interviennent: l'indexation et la similarite entre les documents multimedia, et la gestion du processus interactif. Dans le contexte de la recherche multimedia par le contenu, on s'appuie sur des descriptions visuelles extraites automatiquement des documents. Suite a cette etape initiale, il est necessaire de produire des structures de donnees, appelees index, ainsi qu'une metrique capable de comparer ces structures. Pour ce faire, nous proposons de representer un document sous la forme d'un graphe, ou chaque sommet du graphe represente une partie du document (region, point d'interet, .) et chaque arete du graphe represente une relation entre deux parties du document. Puis, nous introduisons des metriques associees, sous la forme de fonctions noyaux sur graphes, qui permettent d'utiliser ces representations complexes avec les methodes d'apprentissages Hilbertiennes, telle que les SVMs. La gestion du processus interactif entre le systeme et un utilisateur a fait d'important progres grâce a l'approche dite par apprentissage actif. Les premieres approches proposent des criteres pertinents pour la selection de document a faire annoter par l'utilisateur, dans le but de trouver les documents recherches au plus vite. Dans ce contexte, nous proposons d'aller plus loin en nous interessant a la fabrication "en ligne" d'index et metriques associees en fonction de la nature de la recherche. En d'autres termes, nous proposons de remplacer le schema traditionnel, ou un unique index general est utilise, par un schema d'indexation active, ou chaque utilisateur dispose d'un index dedie a sa requete.
How linguistic descriptions of data can help to the teaching-learning process in higher education, case of study: artificial intelligence

Artificial Intelligence is a central topic in the computer science curriculum. From the year 2011 a project-based learning methodology based on computer games has been designed and implemented into the intelligence artificial course at the University of the Bio-Bio. The project aims to develop software-controlled agents (bots) which are programmed by using heuristic algorithms seen during the course. This methodology allows us to obtain good learning results, however several challenges have been founded during its implementation. In this paper we show how linguistic descriptions of data can help to provide students and teachers with technical and personalized feedback about the learned algorithms. Algorithm behavior profile and a new Turing test for computer games bots based on linguistic modelling of complex phenomena are also proposed in order to deal with such challenges. In order to show and explore the possibilities of this new technology, a web platform has been designed and implemented by one of authors and its incorporation in the process of assessment allows us to improve the teaching learning process.
Capturing the Design Space of Sequential Space-Filling Layouts

We characterize the design space of the algorithms that sequentially tile a rectangular area with smaller, fixed-surface, rectangles. This space consist of five independent dimensions: Order, Size, Score, Recurse and Phrase. Each of these dimensions describe a particular aspect of such layout tasks. This class of layouts is interesting, because, beyond encompassing simple grids, tables and trees, it also includes all kinds of treemaps involving the placement of rectangles. For instance, Slice and dice, Squarified, Strip and Pivot layouts are various points in this five dimensional space. Many classic statistics visualizations, such as 100% stacked bar charts, mosaic plots and dimensional stacking, are also instances of this class. A few new and potentially interesting points in this space are introduced, such as spiral treemaps and variations on the strip layout. The core algorithm is implemented as a JavaScript prototype that can be used as a layout component in a variety of InfoViz toolkits.
Cross-cultural gerontology research methods: challenges and solutions

ABSTRACT Cross-cultural research in the behavioural and social sciences uses data from several societies or distinct cultural groups to describe the diversity of human behaviour and test hypotheses about behaviour and culture. This paper reviews the historical development and current state of cross-cultural research in the social sciences and gerontology. Cross-cultural research in gerontology is important because the social processes of ageing vary. It aims to distinguish universal from culturally-specific processes and determine how cultural factors influence individual and population ageing. It has to overcome many challenges: how to design an equivalent and unbiased study, how to access different cultures, how to contextualise these cultures, and how to ensure that questions are meaningful for different cultures. Appropriate strategies include using an international multicultural research team, becoming familiar with the local culture, maintaining good relationships with community leaders, studying only those aspects of behaviour that are functionally equivalent while avoiding the idiosyncratic, using appropriate measures, and encouraging equal partnership and open communication among colleagues. Cross-cultural research has been growing and has become a basis for globally-relevant social gerontology. To highlight the complexity of cross-cultural research and lessons learnt from such research experience, this paper describes an example study of long-term care that involved researchers from more than 30 countries and from many disciplines.
Do Sympathy Biases Induce Charitable Giving? The Effects of Advertising Content

We randomize advertising content motivated by the psychology literature on sympathy generation and framing effects in mailings to about 185,000 prospective new donors in India. We find significant impact on the number of donors and amounts donated consistent with sympathy biases such as the “identifiable victim,” “in-group” and “reference dependence. ” A monthly reframing of the ask amount increases donors and amount donated relative to daily reframing. A second field experiment targeted to past donors, finds that the effect of sympathy bias on giving is smaller in percentage terms but statistically and economically highly significant in terms of the magnitude of additional dollars raised. Methodologically, the paper complements the work of behavioral scholars by adopting an empirical researchers’ lens of measuring relative effect sizes and economic relevance of multiple behavioral theoretical constructs in the sympathy bias and charity domain within one field setting. Beyond the benefit of conceptual replications, the effect sizes provide guidance to managers on which behavioral theories are most managerially and economically relevant when developing advertising content.
Pelvic fractures in pregnancy: factors influencing maternal and fetal outcomes.

BACKGROUND The purpose of this study was to determine factors influencing maternal and fetal outcomes associated with pelvic fractures in pregnancy. METHODS A literature review of pelvic and acetabular fractures during pregnancy was performed, providing 101 cases for analysis (1 case report was included). Factors influencing maternal and fetal mortality were evaluated. RESULTS Pelvic and acetabular fractures during pregnancy were associated with a high maternal (9%) and a higher fetal (35%) mortality rate. Automobile-pedestrian collisions had a trend toward a higher maternal mortality rate, and vehicular collisions had a trend toward a higher fetal mortality rate, compared with falls. Injury severity influenced both maternal and fetal outcomes. Fracture classification (simple vs. complex), fracture type (acetabular vs. pelvic), the trimester of pregnancy, and the era of literature reviewed did not influence mortality rates. When considering potential causes of fetal death, direct trauma to the uterus, placenta, or fetus was not associated with a higher fetal mortality rate, compared with maternal hemorrhage. Pelvic and acetabular fracture surgery has rarely been reported in this patient population. CONCLUSION Pelvic and acetabular fractures in pregnancy continue to be associated with a high fetal mortality rate. Mechanism of injury and injury severity appeared to influence mortality rates, whereas the fracture classification, the fracture type, the trimester of pregnancy, and the era of literature reviewed did not.
Design of reduced order sliding mode governor for hydro-turbines

This paper investigates modeling and control problems of the speed governing system of a hydro-generator unit with one upstream surge tank, driven by a Francis turbine. This governing system is organized into four main functional blocks, namely the hydrodynamic, mechanical, electrical, and servo subsystems. Mathematic models of the individual components are developed and are subsequently interconnected to obtain a model for the governor design. From the viewpoint of modern control theory, only a part of states of this speed governing system are measurable. By introducing an additional state variable, a reduced order sliding mode controller is presented. Simulation results illustrate the feasibility and robustness of the presented method.
Integrated Expression Profiles of mRNA and miRNA in Polarized Primary Murine Microglia

Neuroinflammation contributes to many neurologic disorders including Alzheimer’s disease, multiple sclerosis, and stroke. Microglia is brain resident myeloid cells and have emerged as a key driver of the neuroinflammatory responses. MicroRNAs (miRNAs) provide a novel layer of gene regulation and play a critical role in regulating the inflammatory response of peripheral macrophages. However, little is known about the miRNA in inflammatory activation of microglia. To elucidate the role that miRNAs have on microglial phenotypes under classical (M1) or alternative (M2) activation under lipopolysaccharide (‘M1’-skewing) and interleukin-4 (‘M2a’-skewing) stimulation conditions, we performed microarray expression profiling and bioinformatics analysis of both mRNA and miRNA using primary cultured murine microglia. miR-689, miR-124, and miR-155 were the most strongly associated miRNAs predicted to mediate pro-inflammatory pathways and M1-like activation phenotype. miR-155, the most strongly up-regulated miRNA, regulates the signal transducer and activator of transcription 3 signaling pathway enabling the late phase response to M1-skewing stimulation. Reduced expression in miR-689 and miR-124 are associated with dis-inhibition of many canonical inflammatory pathways. miR-124, miR-711, miR-145 are the strongly associated miRNAs predicted to mediate anti-inflammatory pathways and M2-like activation phenotype. Reductions in miR-711 and miR-124 may regulate inflammatory signaling pathways and peroxisome proliferator-activated receptor-gamma pathway. miR-145 potentially regulate peripheral monocyte/macrophage differentiation and faciliate the M2-skewing phenotype. Overall, through combined miRNA and mRNA expression profiling and bioinformatics analysis we have identified six miRNAs and their putative roles in M1 and M2-skewing of microglial activation through different signaling pathways.
Cloud and cloud shadow removal of landsat 8 images using Multitemporal Cloud Removal method

Cloud and cloud shadow cover on satellite images limit remote sensing and geo-information systems (GIS) applications in all application areas, especially for change detection and time series analyses. A novel method of cloud and cloud shadow removal called Multitemporal Cloud Removal (MCR) is proposed in this paper. The method has main steps: (1) radiometric correction, (2) cloud and cloud shadow detection, and (3) image reconstruction. Top of Atmosphere (TOA) radiometric correction converts digital number values to TOA reflectance for Landsat 8 OLI was first completed. In the second step, Multi-temporal Cloud Masking (MCM) was used to detect cloud and cloud shadow. This method uses a target image which has cloud and cloud shadow contaminated pixels and a reference image which is clear. The aim is to obtain the difference in reflectance values in visible, near-infrared and short wave infrared bands between target and reference images. These values can be used to detect cloud and cloud shadow in Landsat 8 images. The Landsat 8 cirrus band is used to detect thin cirrus cloud in this method. We use target image and reference image from a sequence acquisition dates of Landsat 8 images to avoid the significant land cover change. In the last step, we use multitemporal images to reconstruct pixels which are contaminated by cloud and cloud shadow. Cloud and cloud shadow contaminated pixels on the target image are replaced by pixels from the reference image. Landsat 8 images which have heterogeneous land cover and variety of cloud types are chosen in the experiments to prove that MCR is robust method for removing cloud and cloud shadow and can be used for image that has heterogeneous land cover and variety of cloud types. We use visual and statistical assessments to evaluate the results. As results show, cloud and cloud shadow can be removed by MCR. In visual evaluation, the corrected images are similar to the reference image. In statistical assessments between corrected and references images, the correlation coefficient for each band is quite high (>0.9) for the thick cloud case and equal to 1 for thin cloud case. Within band tandard deviations in the reference image and the corrected image were higher in the corrected image compared to the the reference image and original image. Although not comprehensive, the visual and statistical assessments, provide some indication that th MCR method was robust method for removing cloud and cloud shadow in Landsat 8 images examined in this work. The advantage of this appoach is that original reflectance values can be retained as long as they are not contaminated by cloud and cloud shadow. In addition, as we use a sequence acquisition date of Landsat 8 images, we can produce free cloud and cloud shadow images.
Generic Plan Recognition for Dialogue Systems

We describe a general framework for encoding rich domain models and sophisticated plan reasoning capabilities. The approach uses graph-based reasoning to address a wide range of tasks that typically arise in dialogue systems. The graphical plan representation is independent of but connected to the underlying representation of action and time. We describe types of plan recognition that are needed, illustrating these with examples from dialogues collected as part of the TRAINS project. The algorithms for the tasks are presented, and issues in the formalization of the reasoning processes are discussed.
A Pilot Study Exploring the Educational and Social/Emotional Benefits of Web-Based Groups for Parents of Adolescents with Autism Spectrum Disorders.

Research has indicated that parents of children with Autism Spectrum Disorders (ASDs) experience more feelings of isolation, depression and stress than those of children with other disorders including ADHD and Down Syndrome. While these feelings are especially elevated in parents living in rural communities who may have limited access to services, they are also present in those living in urban areas but are restricted due to challenges with childcare, transportation, finances and time. Furthermore, research posits that services for parents of the newly diagnosed or of younger children are abundant while services and education for parents of adolescents are hard to access regardless of location. This study explored the use of technology as a viable option for providing education and emotional support to parents of adolescents with ASDs and may assist in guiding future studies of this nature. Group sessions were facilitated remotely using video-conference technology and covered the topics of stress reduction, IEP’s, puberty, and the transition to adulthood. This study employed a mixed methods design that utilized a concurrent triangulation approach. Within this approach, quantitative and qualitative data were collected concurrently and then compared to determine if there was convergence, differences, or some combination. The quantitative tools included, first, the Stress Index for Parents of Adolescents (SIPA) which was used to measure any change in stress levels that occurred throughout the treatment.