Miracle-dz/UniFAR-FTUs huggingface dataset:
Macaque-tropic human immunodeficiency virus type 1: breaking out of the host restriction factors

Macaque monkeys serve as important animal models for understanding the pathogenesis of lentiviral infections. Since human immunodeficiency virus type 1 (HIV-1) hardly replicates in macaque cells, simian immunodeficiency virus (SIV) or chimeric viruses between HIV-1 and SIV (SHIV) have been used as challenge viruses in this research field. These viruses, however, are genetically distant from HIV-1. Therefore, in order to evaluate the efficacy of anti-HIV-1 drugs and vaccines in macaques, the development of a macaque-tropic HIV-1 (HIV-1mt) having the ability to replicate efficiently in macaques has long been desired. Recent studies have demonstrated that host restriction factors, such as APOBEC3 family and TRIM5, impose a strong barrier against HIV-1 replication in macaque cells. By evading these restriction factors, others and we have succeeded in developing an HIV-1mt that is able to replicate in macaques. In this review, we have attempted to shed light on the role of host factors that affect the susceptibility of macaques to HIV-1mt infection, especially by focusing on TRIM5-related factors.
Intrusion diagnosis and prediction with expert system

Network diagnosis and attack prediction can help the network administrator to take timely actions to defend against well-planned attacks that exploit a chain of vulnerabilities. One important data source for such analysis is the alerts generated by intrusion detection systems (IDS) deployed over the network. However, IDS typically generates overwhelming amount of alerts, where one cannot simply aggregate or discard. In addition, the chance of a successful exploit depends on many hidden factors such as system status and attacker power, and thus the dependencies among exploits and conditions are typically too complicated to analyze under probability framework. In this paper, we employ expert system to deal with such uncertainties and conduct certainty factor inference. We show that analysis in fuzzy system is tractable and we propose an algorithm to analyze the network status and predict the potential attacks. Finally, we give a case study to illustrate our algorithm and evaluate the effectiveness of our approach on the DARPA data sets. Copyright © 2011 John Wiley & Sons, Ltd.
Fifty years of Monte Carlo simulations for medical physics

Monte Carlo techniques have become ubiquitous in medical physics over the last 50 years with a doubling of papers on the subject every 5 years between the first PMB paper in 1967 and 2000 when the numbers levelled off. While recognizing the many other roles that Monte Carlo techniques have played in medical physics, this review emphasizes techniques for electron–photon transport simulations. The broad range of codes available is mentioned but there is special emphasis on the EGS4/EGSnrc code system which the author has helped develop for 25 years. The importance of the 1987 Erice Summer School on Monte Carlo techniques is highlighted. As an illustrative example of the role Monte Carlo techniques have played, the history of the correction for wall attenuation and scatter in an ion chamber is presented as it demonstrates the interplay between a specific problem and the development of tools to solve the problem which in turn leads to applications in other areas.
FoxM1 is degraded at mitotic exit in a Cdh1-dependent manner

The Forkhead transcription factor FoxM1 is required for the timely expression of many mitotic regulators, such as Cyclin B, Plk1, Aurora B and Cdc25B1-3. For this, FoxM1 is specifically activated in G2 phase through Cyclin A/cdk2-dependent phosphorylation4-6. However, it is currently unclear how FoxM1 activity is removed as cells complete mitosis, and need to shut down expression of the mitotic regulators that are transcriptional targets of FoxM1. Here, we demonstrate that FoxM1 is actively degraded during exit from mitosis by the APC/C. We find that FoxM1 degradation requires Cdh1, a known co-factor for APC/C that is responsible for degradation of many mitotic regulators from anaphase until early G1. FoxM1 binds to Cdh1, and FoxM1 degradation involves both D- and KEN-boxes present in the N-terminal part of FoxM1. Based on these data we propose that Cdh1-dependent degradation of FoxM1 is required to shut down transcriptional activation of mitotic regulators during exit from mitosis.
Galactic Globular and Open Clusters in the Sloan Digital Sky Survey. I. Crowded-Field Photometry and Cluster Fiducial Sequences in ugriz

We present photometry for globular and open cluster stars observed with the Sloan Digital Sky Survey (SDSS). In order to exploit the over 100 million stellar objects with r < 22.5 mag observed by SDSS, we need to understand the characteristics of stars in the SDSS ugriz filters. While star clusters provide important calibration samples for stellar colors, the regions close to globular clusters, where the fraction of field stars is smallest, are too crowded for the standard SDSS photometric pipeline to process. To complement the SDSS imaging survey, we reduce the SDSS imaging data for crowded cluster fields using the DAOPHOT/ALLFRAME suite of programs and present photometry for 17 globular clusters and three open clusters in a SDSS value-added catalog. Our photometry and cluster fiducial sequences are on the native SDSS 2.5 m ugriz photometric system, and the fiducial sequences can be directly applied to the SDSS photometry without relying on any transformations. Model photometry for red giant branch and main-sequence stars obtained by Girardi et al. cannot be matched simultaneously to fiducial sequences; their colors differ by ~0.02-0.05 mag. Good agreement (≲0.02 mag in colors) is found with Clem et al. empirical fiducial sequences in u′g′r′i′z′ when using the transformation equations in Tucker et al.
Translating Domain-Specific Expressions in Knowledge Bases with Neural Machine Translation

Our work presented in this paper focuses on the translation of domain-specific expressions represented in semantically structured resources, like ontologies or knowledge graphs. To make knowledge accessible beyond language borders, these resources need to be translated into different languages. The challenge of translating labels or terminological expressions represented in ontologies lies in the highly specific vocabulary and the lack of contextual information, which can guide a machine translation system to translate ambiguous words into the targeted domain. Due to the challenges, we train and translate the terminological expressions in the medial and financial domain with statistical as well as with neural machine translation methods. We evaluate the translation quality of domain-specific expressions with translation systems trained on a generic dataset and experiment domain adaptation with terminological expressions. Furthermore we perform experiments on the injection of external knowledge into the translation systems. Through these experiments, we observed a clear advantage in domain adaptation and terminology injection of NMT methods over SMT. Nevertheless, through the specific and unique terminological expressions, subword segmentation within NMT does not outperform a word based neural translation model.
Energy-Efficient Backbone Construction, Broadcasting, and Area Coverage in Sensor Networks

A backbone is a subset of sensors that is sufficient for performing assigned tasks. The exact definition depends on the task or the particular desirable properties of a backbone. We discuss two specific kinds of backbones, neighbor and area dominating sets, that we believe are the essential and perhaps only backbones required for sensor networks. A sensor is covered by a backbone if it is in the backbone or is a neighbor to a sensor in the backbone. This type of backbone is referred to here as neighbor-dominating sets, or simply dominating sets. A point within a monitoring area is covered by a sensor backbone if it is within sensing range of at least one sensor from the backbone. This type of backbone is called area-dominating set. In a broadcasting (also known as data-dissemination) task, a message is sent from one node, which could be a monitoring center, to all nodes in the network. Sensors, which are randomly placed in an area, decide which of them should be active and monitor an area, and which of them may sleep and become active at a later time. The communication connectivity is important so that the measured data can be reported to a monitoring center. This problem is known as the sensor-area coverage problem, and needs to be solved efficiently to enable sensor functioning for a prolonged time. Sensors may also be placed deterministically in an area to optimize coverage and reduce power consumption. Most solutions considered in this chapter are based
Phosphorylation switches specific for the cardiac isoform of myosin binding protein‐C: a modulator of cardiac contraction?

Cardiac myosin binding protein‐C (cardiac MyBP‐C, cardiac C protein) belongs to a family of proteins implicated in both regulatory and structural functions of striated muscle. For the cardiac isoform, regulatory phosphorylation in vivo by cAMP‐dependent protein kinase (PKA) upon adrenergic stimulation is linked to modulation of cardiac contraction. The sequence of human cardiac MyBP‐C now reveals regulatory motifs specific for this isoform. Site‐directed mutagenesis identifies a LAGGGRRIS loop in the N‐terminal region of cardiac MyBP‐C as the key substrate site for phosphorylation by both PKA and a calmodulin‐dependent protein kinase associated with the native protein. Phosphorylation of two further sites by PKA is induced by phosphorylation of this isoform‐specific site. This phosphorylation switch can be mimicked by aspartic acid instead of phosphoserine. Cardiac MyBP‐C is therefore specifically equipped with sensors for adrenergic regulation of cardiac contraction, possibly implicating cardiac MyBP‐C in cardiac disease. The gene coding for cardiac MyBP‐C has been assigned to the chromosomal location 11p11.2 in humans, and is therefore in a region of physical linkage to subsets of familial hypertrophic cardiomyopathy (FHC). This makes cardiac MyBP‐C a candidate gene for chromosome 11‐associated FHC.
Provably Efficient Adaptive Scheduling for Parallel Jobs

Scheduling competing jobs on multiprocessors has always been an important issue for parallel and distributed systems. The challenge is to ensure global, systemwide efficiency while offering a level of fairness to user jobs. Various degrees of successes have been achieved over the years. However, few existing schemes address both efficiency and fairness over a wide range of work loads. Moreover, in order to obtain analytical results, most of them require prior information about jobs, which may be difficult to obtain in real applications. This paper presents two novel adaptive scheduling algorithms – GRAD for centralized scheduling, and WRAD for distributed scheduling. Both GRAD and WRAD ensure fair allocation under all levels of workload, and they offer provable efficiency without requiring prior information of job’s parallelism. Moreover, they provide effective control over the scheduling overhead and ensure efficient utilization of processors. To the best of our knowledge, they are the first non-clairvoyant scheduling algorithms that offer such guarantees. We also believe that our new approach of resource request-allotment protocol deserves further exploration. Specifically, both GRAD and WRAD are O(1)competitive with respect to mean response time for batched jobs, and O(1)-competitive with respect to makespan for nonbatched jobs with arbitrary release times. The simulation results show that, for non-batched jobs, the makespan produced by GRAD is no more than 1.39 times of the optimal on average and it never exceeds 4.5 times. For batched jobs, the mean response time produced by GRAD is no more than 2.37 times of the optimal on average, and it never exceeds 5.5 times.
SAPS: Software Defined Network Aware Pub/Sub -- A Design of the Hybrid Architecture Utilizing Distributed and Centralized Multicast

Pub/Sub communication model becomes a basis of various applications, e.g. IoT/M2M, SNS. These application domains require new properties of the Pub/Sub infrastructure, for example, supporting a large number of devices with widely distributed manner, handling emergency messaging with priority control and so on. In order to meet the demands, we proposed Software Defined Network Aware Pub/Sub (SAPS) which utilize the both Application Layer Multicast (ALM) and SDN, especially Open Flow based multicast (OFM). A simulation was done for evaluating the hybrid architecture in traffic and transmission delay reduction, and then the issues to be solved in the current design were discussed.
Dayside aurora classification via BIFs-based sparse representation using manifold learning

Aurora is the typical ionosphere track generated by the interaction of solar wind and magnetosphere, whose modality and variation are significant to the study of space weather activity. A new aurora classification algorithm based on biologically inspired features (BIFs) and discriminative locality alignment (DLA) is proposed in this paper. First, an aurora image is represented by the BIFs, which combines the C1 units from the hierarchical model of object recognition in cortex and the gist features from the saliency map; then, the manifold learning method called DLA is used to obtain the effective sparse representation for auroras based on BIFs; finally, classification results using support vector machine and nearest neighbour with three sets of features: the C1 unit features, the gist features and the BIFs illustrate the effectiveness and robustness of our method on the real aurora image database from Chinese Arctic Yellow River Station.
International Conference on Computer Vision and Pattern Recognition , New York , NY , June 17-22 , 2006 1 3 D Alignment of Face in a Single Image Lie Gu and Takeo Kanade Computer Science

We present an approach for aligning a 3D deformable model to a single face image. The model consists of a set of sparse 3D points and the view-based patches associated with every point. Assuming a weak perspective projection model, our algorithm iteratively deforms the model and adjusts the 3D pose to fit the image. As opposed to previous approaches, our algorithm starts the fitting without resorting to manual labeling of key facial points. And it makes no assumptions about global illumination or surface properties , so it can be applied to a wide range of imaging conditions. Experiments demonstrate that our approach can effectively handle unseen faces with a variety of pose and illumination variations.
Salivary Peptide Tyrosine–Tyrosine 3–36 Modulates Ingestive Behavior without Inducing Taste Aversion

Hormone peptide tyrosine–tyrosine (PYY) is secreted into circulation from the gut L-endocrine cells in response to food intake, thus inducing satiation during interaction with its preferred receptor, Y2R. Clinical applications of systemically administered PYY for the purpose of reducing body weight were compromised as a result of the common side effect of visceral sickness. We describe here a novel approach of elevating PYY in saliva in mice, which, although reliably inducing strong anorexic responses, does not cause aversive reactions. The augmentation of salivary PYY activated forebrain areas known to mediate feeding, hunger, and satiation while minimally affecting brainstem chemoreceptor zones triggering nausea. By comparing neuronal pathways activated by systemic versus salivary PYY, we identified a metabolic circuit associated with Y2R-positive cells in the oral cavity and extending through brainstem nuclei into hypothalamic satiety centers. The discovery of this alternative circuit that regulates ingestive behavior without inducing taste aversion may open the possibility of a therapeutic application of PYY for the treatment of obesity via direct oral application.
Investigation of human mitochondrial myopathies by phosphorus magnetic resonance spectroscopy

Abnormal mitochondria are an increasingly recognized cause of neuromuscular disease. We have used phosphorus magnetic resonance spectroscopy to monitor noninvasively the metabolism of high‐energy phosphates and the intracellular pH of human skeletal muscle in vivo in 12 patients with mitochondrial myopathy. At rest, an abnormality could be demonstrated in 11 of 12 patients. Ten patients had evidence of a reduced muscle energy state with at least one of the following abnormalities: low phosphorylation potential, low phosphocreatine concentration, high adenosine diphosphate concentration, or high inorganic phosphate concentration. Two patients had abnormal resting muscle intracellular pH. In some patients phosphocreatine concentration decreased to low values during exercise despite limited work output. This was not accompanied by particularly severe intracellular acidosis. Evidence of impaired rephosphorylation of adenosine diphosphate to adenosine triphosphate during recovery from exercise was found in approately half the patients. Phosphorus magnetic resonance spectroscopy is useful in the noninvasive diagnosis of mitochondrial myopathies and in defining the pathophysiological basis of these disorders.
Modeling Heat, Water Vapor, and Carbon Dioxide Flux Distribution Inside Canopies Using Turbulent Transport Theories

This study reports recent developments in mulitlayer turbulent transport methods to compute distributions of strengths of scalar sources and sinks Sc as well as turbulent fluxes Fc within the plant–atmosphere continuum. In particular, we focus on the so‐called “inverse methods” that estimate Sc from measured mean scalar concentration (or temperature) distribution within the canopy without resorting to any ecophysiologically based input. These approaches are able to reproduce measured turbulent fluxes above and within the canopy without relying on gradient diffusion formulation. Comparison between measured and modeled sensible heat flux vertical attenuation within the canopy suggests that all three methods provided comparable root‐mean squared error (RMSE) (∼50 W m−2). Furthermore, correcting for local atmospheric stability significantly improved the agreement between model calculations and measurements. Comparisons between measured and modeled land surface fluxes of sensible heat, latent heat, and CO2 above the canopy are conducted using wavelet spectral methods applied to a wide range of temporal scales (30 min–2 yr). This study is the first to rigorously assess the performance of several inverse methods for such a broad range of time scales. We found that the three inverse modeled flux spectra bound the measured one. Hence, spectral agreement among the three models provides the necessary confidence in calculated fluxes and scalar sources. Conversely, large disagreement between the inverse models “flags” large uncertainties at those particular time scales.
Modeling XML Content Explained

Over the years, a lot of course material has been developed to explain to undergraduate students the fundamentals of XML, and schema languages such as DTD and XMLSchema. Typically, the syntax of these languages is discussed and examples are given. How to find a schema for some XML content is often not covered by the material. As a result, students have problems to start with modeling a complex schema, many of their inferred XML schemas are too liberal, and some are even incorrect. In this paper we present a systematic approach for modeling XML content models based on rewriting regular expressions. A smallscale experiment has demonstrated that the quality of the models is improved, and that the approach helps students to begin modeling XML content.
An intelligent classification model for phishing email detection

Phishing attacks are one of the trending cyber-attacks that apply socially engineered messages that are communicated to people from professional hackers aiming at fooling users to reveal their sensitive information, the most popular communication channel to those messages is through users’ emails. This paper presents an intelligent classification model for detecting phishing emails using knowledge discovery, data mining and text processing techniques. This paper introduces the concept of phishing terms weighting which evaluates the weight of phishing terms in each email. The pre-processing phase is enhanced by applying text stemming and WordNet ontology to enrich the model with word synonyms. The model applied the knowledge discovery procedures using five popular classification algorithms and achieved a notable enhancement in classification accuracy; 99.1% accuracy was achieved using the Random Forest algorithm and 98.4% using J48, which is –to our knowledge- the highest accuracy rate for an accredited data set. This paper also presents a comparative study with similar proposed classification techniques.
GFAP-Cre-Mediated Transgenic Activation of Bmi1 Results in Pituitary Tumors

Bmi1 is a member of the polycomb repressive complex 1 and plays different roles during embryonic development, depending on the developmental context. Bmi1 over expression is observed in many types of cancer, including tumors of astroglial and neural origin. Although genetic depletion of Bmi1 has been described to result in tumor inhibitory effects partly through INK4A/Arf mediated senescence and apoptosis and also through INK4A/Arf independent effects, it has not been proven that Bmi1 can be causally involved in the formation of these tumors. To see whether this is the case, we developed two conditional Bmi1 transgenic models that were crossed with GFAP-Cre mice to activate transgenic expression in neural and glial lineages. We show here that these mice generate intermediate and anterior lobe pituitary tumors that are positive for ACTH and beta-endorphin. Combined transgenic expression of Bmi1 together with conditional loss of Rb resulted in pituitary tumors but was insufficient to induce medulloblastoma therefore indicating that the oncogenic function of Bmi1 depends on regulation of p16INK4A/Rb rather than on regulation of p19ARF/p53. Human pituitary adenomas show Bmi1 overexpression in over 50% of the cases, which indicates that Bmi1 could be causally involved in formation of these tumors similarly as in our mouse model.
Arrest of -Amyloid Fibril Formation by a Pentapeptide Ligand (*)

Polymerization of amyloid β-peptide (Aβ) into amyloid fibrils is a critical step in the pathogenesis of Alzheimer's disease. Here, we show that peptides incorporating a short Aβ fragment (KLVFF; Aβ) can bind full-length Aβ and prevent its assembly into amyloid fibrils. Through alanine substitution, it was demonstrated that amino acids Lys, Leu, and Phe are critical for binding to Aβ and inhibition of Aβ fibril formation. A mutant Aβ molecule, in which these residues had been substituted, had a markedly reduced capability of forming amyloid fibrils. The present data suggest that residues Aβ serve as a binding sequence during Aβ polymerization and fibril formation. Moreover, the present KLVFF peptide may serve as a lead compound for the development of peptide and non-peptide agents aimed at inhibiting Aβ amyloidogenesis in vivo.