Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic Copy Number Profiling Using Circulating Free Tumor DNA Highlights Heterogeneity in Neuroblastoma

Purpose: The tumor genomic copy number profile is of prognostic significance in neuroblastoma patients. We have studied the genomic copy number profile of cell-free DNA (cfDNA) and compared this with primary tumor arrayCGH (aCGH) at diagnosis. Experimental Design: In 70 patients, cfDNA genomic copy number profiling was performed using the OncoScan platform. The profiles were classified according to the overall pattern, including numerical chromosome alterations (NCA), segmental chromosome alterations (SCA), and MYCN amplification (MNA). Results: Interpretable and dynamic cfDNA profiles were obtained in 66 of 70 and 52 of 70 cases, respectively. An overall identical genomic profile between tumor aCGH and cfDNA was observed in 47 cases (3 NCAs, 22 SCAs, 22 MNAs). In one case, cfDNA showed an additional SCA not detected by tumor aCGH. In 4 of 8 cases with a silent tumor aCGH profile, cfDNA analysis revealed a dynamic profile (3 SCAs, 1 NCA). In 14 cases, cfDNA analysis did not reveal any copy number changes. A total of 378 breakpoints common to the primary tumor and cfDNA of any given patient were identified, 27 breakpoints were seen by tumor aCGH, and 54 breakpoints were seen in cfDNA only, including two cases with interstitial IGFR1 gains and two alterations targeting TERT. Conclusions: These results demonstrate the feasibility of cfDNA copy number profiling in neuroblastoma patients, with a concordance of the overall genomic profile in aCGH and cfDNA dynamic cases of 97% and a sensitivity of 77%, respectively. Furthermore, neuroblastoma heterogeneity is highlighted, suggesting that cfDNA might reflect genetic alterations of more aggressive cell clones. Clin Cancer Res; 22(22); 5564–73. ©2016 AACR. See related commentary by Janku and Kurzrock, p. 5400
Targeting Endothelial Adhesion Molecule Transcription for Treatment of Inflammatory Disease: A Proof-of-Concept Study

Targeting the endothelial adhesion molecules that control leukocyte trafficking into a tissue has been explored as a biological therapy for inflammatory diseases. However, these molecules also participate in leukocyte migration for immune surveillance, and inhibiting the physiological level of an adhesion molecule might promote infection or malignancy. We explored the concept of targeting endothelial adhesion molecule transcription during inflammation in a human system. Intercellular adhesion molecule 1 (ICAM-1) mediates leukocyte migration across the retinal endothelium in noninfectious posterior uveitis. We observed an increase in the transcription factor, nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (NF-κB1), in parallel with ICAM-1, in human retinal endothelial cells treated with tumor necrosis factor-alpha (TNF-α), and identified putative binding sites for NF-κB1 within the ICAM-1 regulatory region. We targeted induced NF-κB1 expression in endothelial cells with small interfering (si)RNA. Knockdown of NF-κB1 significantly decreased cell surface expression of ICAM-1 protein induced by TNF-α but did not reduce constitutive ICAM-1 expression. Consistently, NF-κB1 knockdown significantly reduced leukocyte binding to cell monolayers in the presence of TNF-α but did not impact baseline binding. Findings of this proof-of-concept study indicate that induced transcription of endothelial adhesion molecules might be targeted therapeutically for inflammatory disease in humans.
Protein Acetylation and Acetyl Coenzyme A Metabolism in Budding Yeast

ABSTRACT Cells sense and appropriately respond to the physical conditions and availability of nutrients in their environment. This sensing of the environment and consequent cellular responses are orchestrated by a multitude of signaling pathways and typically involve changes in transcription and metabolism. Recent discoveries suggest that the signaling and transcription machineries are regulated by signals which are derived from metabolism and reflect the metabolic state of the cell. Acetyl coenzyme A (CoA) is a key metabolite that links metabolism with signaling, chromatin structure, and transcription. Acetyl-CoA is produced by glycolysis as well as other catabolic pathways and used as a substrate for the citric acid cycle and as a precursor in synthesis of fatty acids and steroids and in other anabolic pathways. This central position in metabolism endows acetyl-CoA with an important regulatory role. Acetyl-CoA serves as a substrate for lysine acetyltransferases (KATs), which catalyze the transfer of acetyl groups to the epsilon-amino groups of lysines in histones and many other proteins. Fluctuations in the concentration of acetyl-CoA, reflecting the metabolic state of the cell, are translated into dynamic protein acetylations that regulate a variety of cell functions, including transcription, replication, DNA repair, cell cycle progression, and aging. This review highlights the synthesis and homeostasis of acetyl-CoA and the regulation of transcriptional and signaling machineries in yeast by acetylation.
Fast Parallel Non-Contiguous File Access

Many applications of parallel I/O perform non-contiguous file accesses: instead of accessing a single (large) block of data in a file, a number of (smaller) blocks of data scattered throughout the file needs to be accessed in each logical I/O operation. However, only few file system interfaces directly support this kind of non-contiguous file access. In contrast, the most commonly used parallel programming interface, MPI, incorporates a exible model of parallel I/O through its MPI-IO interface. With MPI-IO, arbitrary non-contiguous file accesses are supported in a uniform fashion by the use of derived MPI datatypes set up by the user to re ect the desired I/O pattern. Despite a considerable amount of recent work in this area, current MPI-IO implementations suffer from low performance of such non-contiguous accesses when compared to the performance of the storage system for contiguous accesses. In this paper we analyze an important bottleneck in the efficient handling of non-contiguous access patterns in current implementations of MPI-IO. We present a new technique, termed listless I/O, that can be incorporated into MPI-IO implementations like the well-known ROMIO implementation, and completely eliminates this bottleneck. We have implemented the technique in MPI/SX, the MPI implementation for the NEC SX-series of parallel vector computers. Results with a synthetic benchmark and an application kernel show that listless I/O is able to increase the bandwidth for non-contiguous file access by sometimes more than a factor of 500 when compared to the traditional approach.
NIM: Scalable Distributed Stream Processing System on Mobile Network Data

As a typical example of New Moore's law, the amount of 3G mobile broad-band (MBB) data has grown from 15 to 20 times in the past two years (30TB to 40TB per day on average for a major city in China), real-time processing and mining of these data are becoming increasingly necessary. The overhead of storage and file transfer to HDFS, delay in processing, etc are making offline analysis on these datasets obsolete. Analysis of these datasets are non-trivial, examples include mobile personal recommendation, anomaly traffic detection, and network fault diagnosis. In this talk, we describe NIM - Network Intelligence Miner. NIM is a scalable and elastic streaming solution that analyzes MBB statistics and traffic patterns in real-time and provides information for real-time decision making. The accuracy of statistical analysis and pattern recognition of NIM is identical to that of off line analysis, while NIM can process data at line rate. The design and the unique features (e.g., balanced data grouping, aging strategy) of NIM will be helpful not only for the network data analysis but also for other applications.
Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification

The geographic object-based image analysis (GEOBIA) framework has gained increasing interest for the last decade. One of its key advantages is the hierarchical representation of an image, where object topological features can be extracted and modeled in the form of structured data. We thus propose to use a structured kernel relying on the concept of bag of subpaths to directly cope with such features. The kernel can be approximated using random Fourier features, allowing it to be applied on a large structure size (the number of objects in the structured data) and large volumes of data (the number of pixels or regions for training). With the so-called scalable bag of subpaths kernel (SBoSK), we also introduce a novel multi-source classification approach performing machine learning directly on a hierarchical image representation built from two images at different resolutions under the GEOBIA framework. Experiments run on an urban classification task show that the proposed approach run on a single image improves the classification overall accuracy in comparison with conventional approaches from 2% to 5% depending on the training set size and that fusing two images allows a supplementary 4% accuracy gain. Additional evaluations on public available large-scale datasets illustrate further the potential of SBoSK, with overall accuracy rates improvement ranging from 1% to 11% depending on the considered setup.
Detection of side-view faces in color images

A coarse-to-fine scheme for the detection of sideview faces in color images is proposed in this paper, which extends the current state of the art of face detection research. The input image can be of complex scene, containing cluttered background and confusing objects. The system consists of four stages, each of which is a refinement of the previous one, namely: (1) skin-tone detection by color, (2) region and edge preprocessing with morphological operations and length filtering, (3) face candidate region selection based on normalized similarity value and (4) final verification using hidden Markov models. Encouraging experimental results have been obtained, due to the utilization of multiple features of the input image and the conjunction of employment of various image processing and pattern recognition techniques. Besides providing the ability to detect faces other than frontal-view, our work has 3 original contributions, including the Normalized Similarity Value (NSV) to detect the presence of a given curve pattern, the iterative partition process to segment the object from confusing extraneous regions for higher detection accuracy and the exploration of the use of HMM to recognize objects in images.
Region-Based Dynamic Separation for STM Haskell

We present a design and implementation of dynamic separation in STM Haskell. Dynamic separation is a recent approach to software transactional memory (STM) that achieves strongly-atomic semantics with performance comparable to that of a weakly-atomic STM. STM Haskell, a lazy-versioning STM library for Haskell, previously supported strongly-atomic semantics via static separation, and we have found dynamic separation to be a natural extension of the library’s interface. Our implementation of dynamic separation has two novel extensions. First, we improve support for mutable data structures by providing protection regions, special objects that hold the protection state for multiple references. Second, we expand the set of protection states so that the interface is more expressive. We give a formal semantics for region-based dynamic separation and prove that a weak lazy-versioning STM supports strong atomicity (for a semantics without regions). We have evaluated the performance of our system on a suite of STM Haskell programs. We also discuss an alternate implementation that detects inconsistent protection states at run time.
Automatic symbolic verification of embedded systems

We present a model checking procedure and its implementation for the automatic verification of embedded systems. Systems are represented by hybrid automata - machines with finite control and real-valued variables modeling continuous environment parameters such as time, pressure, and temperature. System properties are specified in a real-time temporal logic and verified by symbolic computation. The verification procedure, implemented in Mathematica, is used to prove digital controllers and distributed algorithms correct. The verifier checks safety, liveness, time-bounded, and duration properties of hybrid automata. <<ETX>>
Multiple tree-cover states in the earth system

In this dissertation I examine the existence of multiple stable treecover states of the Earth’s forest ecosystems, with a primary focus on the boreal region. Combining remotely-sensed observations, data analysis, and conceptual models, I identify areas with alternative vegetation states under the same environmental conditions, and I explore their possible dynamics under current and future conditions. In recent years, it has been found that the distributions of remotelysensed tree cover in boreal and tropical ecosystems have three distinct modes, corresponding to treeless, open woodland, and forest states. In light of this pattern, it has been suggested that these modes reflect the presence of alternative tree-cover states. As a response to climate change, these ecosystems could undergo critical regime shifts. For the tropics, it has been shown that a positive feedback between fire and vegetation can act as switch between the three states. For the boreal forest, it has been shown that the observed multimodality is not caused by temperature and precipitation patterns. In the first part of this thesis, by means of generalised additive models, I show that the relationship between tree cover and eight remotely-sensed environmental variables varies within the boreal region. Using a classification, I identify areas which exhibit alternative tree-cover states under similar environmental conditions. These regions show a reduced resilience and can shift between states. In the second part of the thesis, I develop and employ a conceptual model to show that tree-cover multistability in the boreal region can emerge through competition between species with different evolutionary traits. By forcing the model with varying permafrost conditions, I show that the asymmetry in tree-species distribution between North America and Eurasia could be due to permafrost presence. In the third part of the thesis, employing projected environmental conditions from the Representative Concentration Pathway (RCP) 2.6 and 8.5 scenarios, I identify potentially multistable areas during the last decade of the 21st century. By including a simple effect of CO2 on plant growth in the conceptual model, I simulate the dynamics of multistable zones under projected environmental conditions. I show that the two scenarios exhibit opposite trends regarding the extent of multistable areas, and that the resilience of Eurasian species might increase, while North American forests might lose stability. In each part of this thesis, I consider limits and advantages of the tools at hand, implying that only through their combined use we can advance our knowledge of tree-cover multistability and improve the representation of the boreal forest in climate models.
Tweets Miner for Stock Market Analysis

In this paper, we present a software package for the data mining of Twitter microblogs for the purpose of using them for the stock market analysis. The package is written in R langauge using apropriate R packages. The model of tweets has been considered. We have also compared stock market charts with frequent sets of keywords in Twitter microblogs messages.
MOSFET mismatch modeling: a new approach

Digital and analog ICs generally rely on the concept of matched behavior between identically designed devices. Time-independent variations between identically designed transistors, called mismatch, affect the performance of most analog and even digital MOS circuits. This article focuses on the analysis of mismatch in MOS transistors resulting from random fluctuations of the dopant concentration, first studied by Keyes. Today, we recognize these fluctuations as the main cause of mismatch in bulk CMOS transistors.
MAS for video objects segmentation and tracking based on active contours and SURF descriptor

In computer vision, video segmentation and tracking is an important challenging issue. In this paper, we describe a new video sequences segmentation and tracking algorithm based on MAS "multi-agent systems" and SURF "Speeded Up Robust Features". Our approach consists in modelling a multi-agent system for segmenting the first image from a video sequence and tracking objects in the video sequences. The used agents are supervisor and explorator agents, they are communicating between them and they inspire in their behavior from active contours approaches. The tracking of objects is based on SURF descriptors "Speed Up Robust Features". We used the DIMA platform and "API Ateji PX" (an extension of the Java language to facilitate parallel programming on heterogeneous architectures) to implement this algorithm. The experimental results indicate that the proposed algorithm is more robust and faster than previous approaches.
Evaluating EVD and SVD errors in signal processing environments

In practical signal processing environments, the error perturbations in the eigenvalue decomposition (EVD) or singular value decomposition (SVD) will be due to both noise and numerical errors. Many numerical analysts have touted the SVD as being superior to the EVD because it has less numerical error. However, these errors are often insignificant when the noise perturbation is large enough. Since the EVD is computationally cheaper to compute than the SVD, it should be used when possible. In this paper, both stochastic and asymptotic upper bound approaches are used to estimate the valid signal to noise ratio range in which an EVD should be used in place of an SVD. The results may likewise be used to calculate the required numerical precision for a given signal to noise ratio. A model for floating point errors for matrix cross-products is also developed which by itself is a useful result.
The effects of positive versus negative impact reflection on change in job performance and work-life conflict

Research on task significance and relational job design suggests that information from beneficiaries of one’s work fosters perceptions of impact, and thus improved work outcomes. This paper presents results from a longitudinal field experiment examining the effect of another strategy for fostering perceptions of impact – engaging employees in regular reflection about how their work benefits others. With a sample of professionals from multiple organizations, this longitudinal study examined the effect on job performance and work-life conflict of both positive and negative impact reflection. Results show that negative impact reflection had a pronounced negative effect on job performance, but no effect on work-life conflict. Positive impact reflection had a weak positive effect on work-life conflict, but no significant effect on job performance. The direction of effects seen in the no intervention condition mirrored that of the negative impact reflection condition, suggesting a possible buffering effect for positive impact reflection. This research provides empirical and theoretical contributions to the literatures on relational job design and task significance.
Quality of service based routing: a performance perspective

Recent studies provide evidence that Quality of Service (QoS) routing can provide increased network utilization compared to routing that is not sensitive to QoS requirements of traffic. However, there are still strong concerns about the increased cost of QoS routing, both in terms of more complex and frequent computations and increased routing protocol overhead. The main goals of this paper are to study these two cost components, and propose solutions that achieve good routing performance with reduced processing cost. First, we identify the parameters that determine the protocol traffic overhead, namely (a) policy for triggering updates, (b) sensitivity of this policy, and (c) clamp down timers that limit the rate of updates. Using simulation, we study the relative significance of these factors and investigate the relationship between routing performance and the amount of update traffic. In addition, we explore a range of design options to reduce the processing cost of QoS routing algorithms, and study their effect on routing performance. Based on the conclusions of these studies, we develop extensions to the basic QoS routing, that can achieve good routing performance with limited update generation rates. The paper also addresses the impact on the results of a number of secondary factors such as topology, high level admission control, and characteristics of network traffic.
c-Abl phosphorylates Dok1 to promote filopodia during cell spreading

Filopodia are dynamic F-actin structures that cells use to explore their environment. c-Abl tyrosine kinase promotes filopodia during cell spreading through an unknown mechanism that does not require Cdc42 activity. Using an unbiased approach, we identified Dok1 as a specific c-Abl substrate in spreading fibroblasts. When activated by cell adhesion, c-Abl phosphorylates Y361 of Dok1, promoting its association with the Src homology 2 domain (SH2)/SH3 adaptor protein Nck. Each signaling component was critical for filopodia formation during cell spreading, as evidenced by the finding that mouse fibroblasts lacking c-Abl, Dok1, or Nck had fewer filopodia than cells reexpressing the product of the disrupted gene. Dok1 and c-Abl stimulated filopodia in a mutually interdependent manner, indicating that they function in the same signaling pathway. Dok1 and c-Abl were both detected in filopodia of spreading cells, and therefore may act locally to modulate actin. Our data suggest a novel pathway by which c-Abl transduces signals to the actin cytoskeleton through phosphorylating Dok1 Y361 and recruiting Nck.