Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Sequence Determinants within the Carboxyl-terminal Domain of Chemokine Receptor CCR5 That Regulate Signaling and Receptor Internalization*

The CC chemokine receptor CCR5 mediates chemotaxis of leukocytes and serves as a principal co-receptor for macrophage-tropic human immunodeficiency virus type 1. To identify determinants on the CCR5 carboxyl-terminal domain that regulate receptor signaling and internalization, we generated several CCR5 mutants, which were progressively shortened from the COOH terminus or had carboxyl-terminal serine, cysteine, or leucine residues substituted by alanine and expressed them in RBL-2H3 cells. Using fluorescence resonance energy transfer between β-arrestin and CCR5 tagged with cyan and yellow variants of green fluorescent protein, we show that high affinity association of the two molecules in living cells requires intact carboxyl-terminal serine phosphorylation sites. Phosphorylation-deficient truncation or Ser/Ala replacement mutants of CCR5 mediated a sustained calcium response and enhanced granular enzyme release in RANTES-stimulated cells. Carboxyl-terminal serine residues are critically involved in CCR5 endocytosis and a dileucine motif, similar to that implicated in the regulation of CXCR2 and CXCR4, contributes to the internalization of CCR5 in a phosphorylation-independent manner. Despite their prominent role in receptor desensitization and internalization, β-arrestins are dispensable for the CCR5-mediated stimulation of mitogen-activated protein kinase pathways in RBL-2H3 cells. We also show that CCR5 is palmitoylated on carboxyl-terminal cysteine residues. Inhibition of CCR5 palmitoylation by alanine mutagenesis of cysteines or treatment with a palmitate analogue inhibitor profoundly reduces phorbol 12-myristate 13-acetate- and RANTES-induced receptor phosphorylation, homologous desensitization, and internalization. Alanine mutagenesis of serine, cysteine, or leucine residues or the limited carboxyl-terminal truncation of CCR5 did not impair chemokine-stimulated migration of RBL-2H3 cells. Together these results indicate that post-translational modifications of carboxyl-terminal serine and cysteine residues have a significant impact on receptor deactivation and internalization.
Deletion of Rictor in Brain and Fat Alters Peripheral Clock Gene Expression and Increases Blood Pressure

The mammalian target of rapamycin complex 2 (mTORC2) contains the essential protein RICTOR and is activated by growth factors. mTORC2 in adipose tissue contributes to the regulation of glucose and lipid metabolism. In the perivascular adipose tissue, mTORC2 ensures normal vascular reactivity by controlling expression of inflammatory molecules. To assess whether RICTOR/mTORC2 contributes to blood pressure regulation, we applied a radiotelemetry approach in control and Rictor knockout (RictoraP2KO) mice generated using adipocyte protein-2 gene promoter–driven CRE recombinase expression to delete Rictor. The 24-hour mean arterial pressure was increased in RictoraP2KO mice, and the physiological decline in mean arterial pressure during the dark period was impaired. In parallel, heart rate and locomotor activity were elevated during the dark period with a pattern similar to blood pressure changes. This phenotype was associated with mild cardiomyocyte hypertrophy, decreased cardiac natriuretic peptides, and their receptor expression in adipocytes. Moreover, clock gene expression was reduced or phase-shifted in perivascular adipose tissue. No differences in clock gene expression were observed in the master clock suprachiasmatic nucleus, although Rictor gene expression was also lower in brain of RictoraP2KO mice. Thus, this study highlights the importance of RICTOR/mTORC2 for interactions between vasculature, adipocytes, and brain to tune physiological outcomes, such as blood pressure and locomotor activity.
A Denial-of-Service Attack to GSM/UMTS Networks via Attach Procedure

Mobile Network Operators (MNOs) keep a strict control over users accessing the networks by means of the Subscriber Identity Module (SIM). This module grants the user to access the network, by performing the registration and authentication of the user’s device. Without a valid SIM module and a successful authentication, mobile devices are not granted access and, hence, they are not allowed to inject any traffic in the mobile infrastructure. Nevertheless, in this thesis we describe an attack to the security of a mobile network allowing an unauthenticated malicious mobile device to inject traffic in the mobile operator’s infrastructure. We show that using a few hundreds of malicious devices without any SIM module it is possible to inject high levels of signalling traffic in the mobile infrastructure, causing significant service degradation up to a full-fledged Denial-of-Service (DoS) attack.
Linked Data for Software Security Concepts and Vulnerability Descriptions

Abstract : The Web is often the first source of information to track software vulnerabilities, exploits and cyber attacks. An important source is information found in text from security bulletins, vulnerability databases, news reports, cybersecurity blogs and Internet chat rooms. However, the data representation and interpretation of such unstructured text pose certain limitations on the automation of vulnerability management, and obtaining further contextual information from other related resources. We present an automatic framework that generates and publishes a RDF linked data resource for software security concepts and vulnerability descriptions. Vulnerability descriptions from the National Vulnerability Database (NVD) are aligned with concepts from parallel repositories such as the Common Weakness Enumeration and Common Platform Enumeration. These concepts are represented in RDF using relevant concepts from a custom ontology that models the relationships between classes and entities for the cybersecurity domain. The unstructured sources of information from the NVD are then mapped to related concepts from DBpedia using object properties from the ontology. This system leverages paradigms of the Semantic Web to effectively process unstructured text into a rich resource of machine-understandable information. The RDF linked cybersecurity data collection will make it possible for applications to look up metadata and facilitate searching. Our results demonstrate an effective model for linking key security concepts to relevant resources on the Web. We outline the use of Linked Data technologies to facilitate consumption of information related to security exploits that can be further used for vulnerability identification, mitigation and prevention efforts.
Detecting and labeling folk literature in spoken cultural heritage archives using structural and prosodic features

Spoken cultural heritage can present considerably heterogeneous content as tales, stories, recitals, poems, theatrical representations and other form of folk literature. This work investigates the automatic detection and classification of those data type in large spoken audio archives. The corpus used for this study consists of 90 radio broadcast shows collected for preserving a large variety of Swiss French dialects. Given the variability of the language spoken in the recordings, the paper proposes a language-independent system based on structural features obtained using a speaker diarization system and various acoustic/prosodic features. Results reveal that such a system can achieve an F-measure equal to 0.85 (Precision 0.88/Recall 0.84) in retrieving folk literature in those archives. Prosodic features appear more effective and complementary to structural features. Furthermore, the paper investigates whether the same approach can be used to label speech segments into five large classes (Storytelling, Poetry, Theatre, Interviews, Functionals) showing F-measures ranging from 0.52 to 0.88. As last contribution, prosodic features for disambiguating between spoken prose and spoken poetry are investigated. In summary the study shows that simple structural and acoustic/prosodic features can be used to effectively retrieve and label folk literature in broadcast archives.
The costs of fusion in smart camera networks

The choice of the most suitable fusion scheme for smart camera networks depends on the application as well as on the available computational and communication resources. In this paper we discuss and compare the resource requirements of five fusion schemes, namely centralised fusion, flooding, consensus, token passing and dynamic clustering. The Extended Information Filter is applied to each fusion scheme to perform target tracking. Token passing and dynamic clustering involve negotiation among viewing nodes (cameras observing the same target) to decide which node should perform the fusion process whereas flooding and consensus do not include this negotiation. Negotiation helps limiting the number of participating cameras and reduces the required resources for the fusion process itself but requires additional communication. Consensus has the highest communication and computation costs but it is the only scheme that can be applied when not all viewing nodes are connected directly and routing tables are not available.
Face Recognition in Global Harmonic Subspace

In this paper, a novel pattern recognition scheme, global harmonic subspace analysis (GHSA), is developed for face recognition. In the proposed scheme, global harmonic features are extracted at the semantic scale to capture the 2-D semantic spatial structures of a face image. Laplacian Eigenmap is applied to discriminate faces in their global harmonic subspace. Experimental results on the Yale and PIE face databases show that the proposed GHSA scheme achieves an improvement in face recognition accuracy when compared with conventional subspace approaches, and a further investigation shows that the proposed GHSA scheme has impressive robustness to noise.
New insights into the landscape relationships of host response to bacterial pathogens

Modern understanding of microbiology largely lays foundation in the biological characterization of microorganisms. However, the landscape relationships of host transcriptional response (HTR) to different bacterial pathogens have not yet been systematically explored. Here, we established the first generation of HTR network (HTRN) according to the HTR similarities among 21 different human pathogenic bacterial species by integrating 258 pairs of host cellular gene expression profiles upon infections. Further, the network was dissected into five bacterial communities of more consensus internal HTR. Interestingly, analysis of signature genes across different communities revealed that distinct community signatures (CS) present differential gene expression patterns. Functional annotation suggested a common feature of host cell response to bacterial infections that specific functional gene clusters (BPs and/or signaling pathways) were preferentially elicited or subverted by community bacterial pathogens. Notably, community signatures (especially key associators participating dissimilar functional profiles) were highly enriched of GWAS disease-related genes, which associated bacterial infections with common and specific non-infectious human disease(s). About 40% of the associations were confirmed by literature investigation that further indicated possible/potential association directionality. Our characterization and analysis were the first to feature differential community HTRs upon bacterial pathogen infections and suggested new perspective of understanding infection-disease associations and underlying pathogenesis.
Cognitive and Motivational Parameters in Motivated Biases in Human Judgment

Title of Document: COGNITIVE AND MOTIVATIONAL PARAMETERS IN MOTIVATED BIASES IN HUMAN JUDGMENT Xiaoyan Chen, Ph.D., 2009 Directed By: Distinguished University Professor, Arie W. Kruglanski, Department of Psychology Motivational and cognitive factors can determine the extent and direction of information processing in judgment. When biasing motives are present, information can be distorted and judgment biased. The extent of this bias can be determined by the nature of the information, the relative magnitude of competing goals, and the individual’s cognitive resources. Studies 1, 2 and 3 explored the effect of resource depletion on motivated distortions in judgment. Studies 4 and 5 examined the role of relative goal magnitude (of a biasing goal vs. a specific judgment goal ) in the phenomena. I departed from the assumption that human knowledge is malleable, and that its alteration in a motivationally desirable direction may vary in diffi culty across instances. It was assumed that overcoming the difficulty requires cognit ive and/or motivational resources hence under certain circumstances resource-deple tion should diminish individuals’ ability to motivationally bias judgments. I also hypothesiz ed when information is clear-cut (rather than ambiguous) making distortion diffic ult, a sufficient amount of biasing motivation could overcome the “reality constraints,” holding the cognitive resources constant. In my first two studies participants’ resources were depleted either via com plex or simple presentational format of the information given (Study 1), or via engagement in a fatiguing prior activity (Study 2). In the third study (Study 3), I measur ed participants’ stable cognitive capacity as a proxy for their available cognitive resources. All three studies provided supportive evidence for the hypothesis that motivated distortion is resource dependent. In Study 4 I manipulated the relative g oal magnitude by experimentally increasing goal importance for either a n ac demic success goal in line with the specific judgment task or a social wellbe ing goal as the biasing goal. In Study 5 I altered relative goal magnitude through enhancing eit her a neutral goal or health concerns as the biasing goal. In both Study 4 and 5, orthogonal to the relative goal magnitude manipulation, stimulus ambiguity was made ei ther high or low. Findings from Study 4 and 5 supported the hypothesis that sufficient magnitude of biasing goal could overcome distortion difficulty even in highly constraining circumstances. COGNITIVE AND MOTIVATIONAL PARAMETERS IN MOTIVATED BIASES IN HUMAN JUDGMENT
CTNNBL1 facilitates the association of CWC15 with CDC5L and is required to maintain the abundance of the Prp19 spliceosomal complex

In order to catalyse the splicing of messenger RNA, multiple proteins and RNA components associate and dissociate in a dynamic highly choreographed process. The Prp19 complex is a conserved essential part of the splicing machinery thought to facilitate the conformational changes the spliceosome undergoes during catalysis. Dynamic protein interactions often involve highly disordered regions that are difficult to study by structural methods. Using amine crosslinking and hydrogen–deuterium exchange coupled to mass spectrometry, we describe the architecture of the Prp19 sub-complex that contains CTNNBL1. Deficiency in CTNNBL1 leads to delayed initiation of cell division and embryonic lethality. Here we show that in vitro CTNNBL1 enhances the association of CWC15 and CDC5L, both core Prp19 complex proteins and identify an overlap in the region of CDC5L that binds either CTNNBL1 or CWC15 suggesting the two proteins might exchange places in the complex. Furthermore, in vivo, CTNNBL1 is required to maintain normal levels of the Prp19 complex and to facilitate the interaction of CWC15 with CDC5L. Our results identify a chaperone function for CTNNBL1 within the essential Prp19 complex, a function required to maintain the integrity of the complex and to support efficient splicing.
An Observational Framework to the Zipf ’ s Analysis among Different Languages

The paper introduces the used of Zipfian statistics to observe the human languages by using the same (meaning) corpus/corpora but different in grammatical and structural utterances. We used biblical texts since they contain corpuses that have been most widely and carefully translated into many languages. The idea is to reduce the possibility of noise came from the meaning of the texts in distinctive language. The result is that the robustness of the Zipfian law is observable and some statistical differences are discovered between English and widely used national and several ethnic languages in Indonesia. The paper ends by modestly propose further possible framework in interdisciplinary approaches to human language evolution. in principio erat Verbum et Verbum erat apud Deum et Deus erat Verbum 1. Introduction There have been a lot of places beyond many scientific domains exhibited the signatures of power law as an interesting statistical properties emerged from complex systems (SItungkir & Surya, 2003). Concerning the classic work of Harvard linguistic professor, G. K. Zipf (1947), the power law seems to be not that outlandish among linguist, as it has been showed that the power law in the Zipf plot is linked to the writer and reader natural behavior to minimizing their effort while communicating. In this case, the writer's
Metalevel solution to inheritance anomaly in concurrent object-oriented l~nguages

In the very near future, massive parallel architectures will be available to professionals of numerous elds in the manner personal computers and workstations are today. Professional computing is the term we use for describing the computational activities of professionals requiring immense computational power. We claim that object-oriented concurrent programming (OOCP) serves as the basis for professional computing. The ABCL project group, now at the University of Tokyo, is actively engaged in the research of OOCP[21]. A research conducted by Takuo Watanabe and Akinori Yonezawa of the ABCL project group has shown that meta-level architectures and computational re ection play significant roles for exible modeling of activities of concurrent objects. In ABCL/R, a re ectional version of ABCL/1, examples were given for dynamic acquisition of methods, monitoring of objects, and the virtual time algorithm[18]. Fully-re ectional languages such as ABCL/R, however, tends to experience ine ciency due to their interpretive mode of execution. This is a severe disadvantage for concurrent computing, for substantial gain in the speed of computation is the primary motivation for resorting to concurrency in the rst place. Fortunately, for many purposes, full computational re ection is not necessary; instead, `partial' re ection will su ce. For example, [7] reviews the controlrelated meta-level facilities in LISP, such as call/cc. [9] demonstrates the e ectiveness of partial re ection in Object-Oriented languages by extending the VM of Smalltalk so that message dispatching can be altered by the user. The Metaobject Protocol of CLOS is another example where partial re ection is e ective[2].
A Dimmable LED Driver With Resistive DAC Feedback Control for Adaptive Voltage Regulation

This paper proposes a dimmable energy-efficient light-emitting diode (LED) driver for applications in interior lighting. High efficiency is achieved by an adaptive voltage regulation, which minimizes power losses in the linear current regulator. A digital control mechanism employing a resistive digital-to-analog converter for feeding the analog feedback input of a dc-dc converter is introduced. It is shown that the digital control methodology gives maximum design flexibility and enhances control over regulation speed and stability. In an experimental setup, the proposed concept is verified and evaluated. Operating at an input voltage of 24 V, the LED driver provides a relatively wide output voltage range of 3.5-38 V. Output current is regulated to 700 mA with a steady-state precision of more than 98.8%, whereas pulse width modulation dimming with a frequency of 1 kHz and shortest on-time of 4 μs is employed. A peak efficiency of the complete system of 93.9% is achieved.
Filer Response Time Prediction Using Adaptively-Learned Forecasting Models Based on Counter Time Series Data

Ability to predict the future performance of a storage system is critical for its efficient management. A modern storage system is a very complex combination of hardware and software elements and inferring its state from those of its individual components is practically impossible. Moreover, the state of the system undergoes continuous changes due to diverse and evolving workloads, frequent configuration changes and natural degradation in components. This paper presents an approach to predict the future response time of a storage system by applying a combination of machine learning techniques on the data of its internal parameters. To the best of our knowledge, this is the first attempt to quantify the response time of a storage system directly from the time-series of its internal parameters. We present a systematic experimental setup for collecting the system data and for measuring its response time while loading the system with different standard workloads. Another novel contribution of the paper is a combination of time-series forecasting model followed by a regression model to predict the response time repeatedly in real-time. The said model is shown to be able to repeatedly predict fairly accurate 15-minutes-ahead response time values using the system data of the past 20-minutes' window. It is shown that the validation calculations typically complete within ~30 seconds on an average using a computer of moderate configurations making the presented model applicable for practically reasonable prediction horizons. The design of the model is such that it can be adaptively tuned online at regular intervals as more system data is observed.
Some Competition Programming Problems as the Beginning of Artificial Intelligence

We consider in this paper some programming competition problems (which are near to some problems of ACM competitions) of the following subjects: we can make their solution using both Prolog and a classical procedure-oriented language. Moreover, the considered problems are selected that their solution in Prolog and in a classical procedure-oriented language are similar - i.e., in other words, they use the same working mechanism, the same approach to constructing recursive functions etc. However, the main goal of this paper is to demonstrate, that the solution of most of these problems by the students is a step to design the real problems of artificial intellegence.
A Time Study of Physicians’ Work in a German University Eye Hospital to Estimate Unit Costs

Background Technical efficiency of hospital services is debated since performance has been heterogeneous. Staff time represents the main resource in patient care and its inappropriate allocation has been identified as a key factor of inefficiency. The aim of this study was to analyse the utilisation of physicians’ work time stratified by staff groups, tasks and places of work. A further aim was to use these data to estimate resource use per unit of output. Methods A self-reporting work-sampling study was carried during 14-days at a University Eye Hospital. Staff costs of physicians per unit of output were calculated at the wards, the operating rooms and the outpatient unit. Results Forty per cent of total work time was spent in contact with the patient. Thirty per cent was spent with documentation tasks. Time spent with documentation tasks declined monotonically with increasing seniority of staff. Unit costs were 56 € per patient day at the wards, 77 € and 20 € per intervention at the operating rooms for inpatients and outpatients, respectively, and 33 € per contact at the outpatient unit. Substantial differences in resources directly dedicated to the patient were found between these locations. Conclusion The presented data provide unprecedented units costs in inpatient Ophthalmology. Future research should focus on analysing factors that influence differences in time allocation, such as types of patients, organisation of care processes and composition of staff.