Miracle-dz/UniFAR-FTUs huggingface dataset:
Proteome-wide identification of poly(ADP-ribose) binding proteins and poly(ADP-ribose)-associated protein complexes

Poly(ADP-ribose) (pADPr) is a polymer assembled from the enzymatic polymerization of the ADP-ribosyl moiety of NAD by poly(ADP-ribose) polymerases (PARPs). The dynamic turnover of pADPr within the cell is essential for a number of cellular processes including progression through the cell cycle, DNA repair and the maintenance of genomic integrity, and apoptosis. In spite of the considerable advances in the knowledge of the physiological conditions modulated by poly(ADP-ribosyl)ation reactions, and notwithstanding the fact that pADPr can play a role of mediator in a wide spectrum of biological processes, few pADPr binding proteins have been identified so far. In this study, refined in silico prediction of pADPr binding proteins and large-scale mass spectrometry-based proteome analysis of pADPr binding proteins were used to establish a comprehensive repertoire of pADPr-associated proteins. Visualization and modeling of these pADPr-associated proteins in networks not only reflect the widespread involvement of poly(ADP-ribosyl)ation in several pathways but also identify protein targets that could shed new light on the regulatory functions of pADPr in normal physiological conditions as well as after exposure to genotoxic stimuli.
Demo: Acoustic Sensing Based Indoor Floor Plan Construction Using Smartphones

This demo presents BatMapper, an acoustics sensing technology for fast, fine-grained and low cost floor plan construction. BatMapper operates by emitting sound signal and capturing its reflections by two microphones on smartphones. We develop robust probabilistic echo-object association and outlier removal algorithms to identify the correspondence between distances and objects, thus the geometry of corridors. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically. Additionally, we leverage structure cues in indoor environments for user trace calibration. The demo will enable any person to hold the smartphone and walk along a corridor to map the corridor shape and detect doors in real-time.
CD8 T cell memory in B cell-deficient mice

Antigen presentation by B cells and persistence of antigen-antibody complexes on follicular dendritic cells (FDC) have been implicated in sustaining T cell memory. In this study we have examined the role of B cells and antibody in the generation and maintenance of CD8+ cytotoxic T lymphocyte (CTL) memory. To address this issue we compared CTL responses to lymphocytic choriomeningitis virus (LCMV) in normal (+/+) versus B cell-deficient mice. The CTL response to acute LCMV infection can be broken down into three distinct phases: (a) the initial phase (days 3-8 after infection) of antigen-driven expansion of virus- specific CD8+ T cells and the development of effector CTL (i.e., direct ex vivo killers); (b) a phase of death (between days 10 and 30 after infection) during which >95% of the virus-specific CTL die and the direct effector activity subsides; and (c) the phase of long-term memory (after day 30) that is characterized by a stable pool of memory CTL that persist for the life span of the animal. The role of B cells in each of these three phases of the CTL response was analyzed. We found that B cells were not required for the expansion and activation of virus-specific CTL. The kinetics and magnitude of the effector CTL response, as measured by direct killing of infected targets by ex vivo isolated splenocytes, was identical in B cell-deficient and +/+ mice. Also, the expansion of CD8+ T cells was not affected by the absence of B cells and/or antibody; in both groups of mice there was an approximately 10,000-fold increase in the number of LCMV-specific CTL and a greater than 10-fold increase in the total number of activated (CD44hi) CD8+ T cells during the first week after virus infection. Although no differences were seen during the "expansion" phase, we found that the "death" phase was more pronounced in B cell-deficient mice. However, this increased cell death was not selective for LCMV- specific CTL, and during this period the total number of CD8+ T cells also dropped substantially more in B cell-deficient mice. As a result of this, the absolute numbers of LCMV-specific CTL were lower in B cell- deficient mice but the frequencies were comparable in both groups of mice. More significantly, the memory phase of the CTL response was not affected by the absence of B cells and a stable number of LCMV-specific CTL persisted in B cell-deficient mice for up to 6 mo. Upon reinfection, B cell-deficient mice that had resolved an acute LCMV infection were able to make accelerated CTL responses in vivo and eliminated virus more efficiently than naive B cell-deficient mice. Thus, CTL memory, as assessed by frequency of virus-specific CTL or protective immunity, does not decline in the absence of B cells. Taken together, these results show that neither B cells nor antigen-antibody complexes are essential for the maintenance of CD8+ CTL memory.
Use of Coherence Measurements between EEG and EMG on Identification of the Myoclonus Locus

Myoclonus, an abrupt spasm of a muscle or muscles, occurs in some neurological diseases and is originated by discharges in the motor cortex. It is based on these discharges that diagnosticians try to discover where the focus is and if it is connected with a functional or structural lesion. Coherence measurements between EEG and EMG are helpful to find such locus. This work presents a methodology to answer if the observed muscular events came from the cerebral cortex and in such case how to identify the discharges source. The main purpose is to establish a topographical mapping that allows visualizing the sources of neuronal discharges responsible for such events. Examples are presented to illustrate the approach efficiency. Keywords-component; electroencephalography EEG, Electromyography (EMG), epilepsy
Hsp90 is required to localise cyclin B and Msps/ch-TOG to the mitotic spindle in Drosophila and humans

During mitosis, cyclin B is extremely dynamic and although it is concentrated at the centrosomes and spindle microtubules (MTs) in organisms ranging from yeast to humans, the mechanisms that determine its localisation are poorly understood. To understand how cyclin B is targeted to different locations in the cell we have isolated proteins that interact with cyclin B in Drosophila embryo extracts. Here we show that cyclin B interacts with the molecular chaperone Hsp90 and with the MT-associated protein (MAP) Mini spindles (Msps; the Drosophila orthologue of XMAP215/ch-TOG). Both Hsp90 and Msps are concentrated at centrosomes and spindles, and we show that Hsp90, but not Msps, is required for the efficient localisation of cyclin B to these structures. We find that, unlike what happens with other cell cycle proteins, Hsp90 is not required to stabilise cyclin B or Msps during mitosis. Thus, we propose that Hsp90 plays a novel role in regulating the localisation of cyclin B and Msps during mitosis.
Fingerprint Kalman Filter in indoor positioning applications

In this paper, we present a new filter, the Fingerprint Kalman Filter (FKF), and apply it to indoor positioning. FKF enables sequential position estimation using WLAN RSSI measurements and fingerprint data. Fingerprints that are collected beforehand in a calibration phase contain samples of the radio map in certain points, namely, calibration points. This means that FKF does not need an analytic formula of the measurement equation like conventional Kalman-type filters do (e.g. the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF)). Like EKF and UKF, FKF is based on the recursive computation of the Best Linear Unbiased Estimator (BLUE) and has small computational and memory requirements. An often-used Kalman-type filter for this problem is so-called Position Kalman Filter (PKF) that uses static position solutions as measurements for the conventional Kalman filter. In the test part of the paper, we compare FKF, PKF and different static location estimation methods, namely, the Nearest Neighbor (NN) and the Kernel method. The test data is real WLAN RSSI measurement data. The results indicate that the filters give more accurate position estimates than the static methods. FKF performs better than PKF with NN as the static estimator, and the computational load of FKF is smaller than PKF with the Kernel method.
The Genetic Basis of Resistance to Anticoagulants in Rodents

Anticoagulant compounds, i.e., derivatives of either 4-hydroxycoumarin (e.g., warfarin, bromadiolone) or indane-1,3-dione (e.g., diphacinone, chlorophacinone), have been in worldwide use as rodenticides for >50 years. These compounds inhibit blood coagulation by repression of the vitamin K reductase reaction (VKOR). Anticoagulant-resistant rodent populations have been reported from many countries and pose a considerable problem for pest control. Resistance is transmitted as an autosomal dominant trait although, until recently, the basic genetic mutation was unknown. Here, we report on the identification of eight different mutations in the VKORC1 gene in resistant laboratory strains of brown rats and house mice and in wild-caught brown rats from various locations in Europe with five of these mutations affecting only two amino acids (Tyr139Cys, Tyr139Ser, Tyr139Phe and Leu128Gln, Leu128Ser). By recombinant expression of VKORC1 constructs in HEK293 cells we demonstrate that mutations at Tyr139 confer resistance to warfarin at variable degrees while the other mutations, in addition, dramatically reduce VKOR activity. Our data strongly argue for at least seven independent mutation events in brown rats and two in mice. They suggest that mutations in VKORC1 are the genetic basis of anticoagulant resistance in wild populations of rodents, although the mutations alone do not explain all aspects of resistance that have been reported. We hypothesize that these mutations, apart from generating structural changes in the VKORC1 protein, may induce compensatory mechanisms to maintain blood clotting. Our findings provide the basis for a DNA-based field monitoring of anticoagulant resistance in rodents.
Investigating the Impact of Unsupervised Feature-Extraction from Multi-Wavelength Image Data for Photometric Classification of Stars, Galaxies and QSOs

Accurate classification of astronomical objects currently relies on spectroscopic data. Acquiring this data is time-consuming and expensive compared to photometric data. Hence, improving the accuracy of photometric classification could lead to far better coverage and faster classification pipelines. This paper investigates the benefit of using unsupervised feature-extraction from multi-wavelength image data for photometric classification of stars, galaxies and QSOs. An unsupervised Deep Belief Network is used, giving the model a higher level of interpretability thanks to its generative nature and layer-wise training. A Random Forest classifier is used to measure the contribution of the novel features compared to a set of more traditional baseline features.
Increased Facilitatory Connectivity from the Pre-SMA to the Left Dorsal Premotor Cortex during Pseudoword Repetition

Previous studies have demonstrated that the repetition of pseudowords engages a network of premotor areas for articulatory planning and articulation. However, it remains unclear how these premotor areas interact and drive one another during speech production. We used fMRI with dynamic causal modeling to investigate effective connectivity between premotor areas during overt repetition of words and pseudowords presented in both the auditory and visual modalities. Regions involved in phonological aspects of language production were identified as those where regional increases in the BOLD signal were common to repetition in both modalities. We thus obtained three seed regions: the bilateral pre-SMA, left dorsal premotor cortex (PMd), and left ventral premotor cortex that were used to test 63 different models of effective connectivity in the premotor network for pseudoword relative to word repetition. The optimal model was identified with Bayesian model selection and reflected a network with driving input to pre-SMA and an increase in facilitatory drive from pre-SMA to PMd during repetition of pseudowords. The task-specific increase in effective connectivity from pre-SMA to left PMd suggests that the pre-SMA plays a supervisory role in the generation and subsequent sequencing of motor plans. Diffusion tensor imaging-based fiber tracking in another group of healthy volunteers showed that the functional connection between both regions is underpinned by a direct cortico-cortical anatomical connection.
Automatic loop kernel analysis and performance modeling with Kerncraft

Analytic performance models are essential for understanding the performance characteristics of loop kernels, which consume a major part of CPU cycles in computational science. Starting from a validated performance model one can infer the relevant hardware bottlenecks and promising optimization opportunities. Unfortunately, analytic performance modeling is often tedious even for experienced developers since it requires in-depth knowledge about the hardware and how it interacts with the software. We present the "Kerncraft" tool, which eases the construction of analytic performance models for streaming kernels and stencil loop nests. Starting from the loop source code, the problem size, and a description of the underlying hardware, Kerncraft can ideally predict the single-core performance and scaling behavior of loops on multicore processors using the Roofline or the Execution-Cache-Memory (ECM) model. We describe the operating principles of Kerncraft with its capabilities and limitations, and we show how it may be used to quickly gain insights by accelerated analytic modeling.
Changes in Gray Matter Induced by Learning—Revisited

Background Recently, activation-dependant structural brain plasticity in humans has been demonstrated in adults after three months of training a visio-motor skill. Learning three-ball cascade juggling was associated with a transient and highly selective increase in brain gray matter in the occipito-temporal cortex comprising the motion sensitive area hMT/V5 bilaterally. However, the exact time-scale of usage-dependant structural changes occur is still unknown. A better understanding of the temporal parameters may help to elucidate to what extent this type of cortical plasticity contributes to fast adapting cortical processes that may be relevant to learning. Principal Findings Using a 3 Tesla scanner and monitoring whole brain structure we repeated and extended our original study in 20 healthy adult volunteers, focussing on the temporal aspects of the structural changes and investigated whether these changes are performance or exercise dependant. The data confirmed our earlier observation using a mean effects analysis and in addition showed that learning to juggle can alter gray matter in the occipito-temporal cortex as early as after 7 days of training. Neither performance nor exercise alone could explain these changes. Conclusion We suggest that the qualitative change (i.e. learning of a new task) is more critical for the brain to change its structure than continued training of an already-learned task.
Isolation and characterization of a novel cytokinesis‐deficient mutant in Dictyostelium discoideum

Cytokinesis is a dramatic event in the life of any cell during which numerous mechanisms must coordinate the legitimate and complete mechanical separation into two daughter cells. We have used Dictyostelium discoideum as a model system to study this highly orchestrated event through genetic analysis. Transformants were generated using a method of insertional mutagenesis known as restriction enzyme‐mediated integration (REMI) and subsequently screened for defects in cytokinesis. Mutants isolated in a similar screen suffered a disruption in the myosin II heavy chain gene, a protein known to be essential for cytokinesis and in a novel gene encoding a rho‐like protein termed racE [Larochelle et al., 1996]. In the screen reported here we isolated a third type of mutant, called 10BH2, which also had a complete defect in cytokinesis. 10BH2 mutant cells are able to propagate on tissue culture plates by fragmenting into smaller cells by a process known as traction‐mediated cytofission. However, when grown in suspension culture, 10BH2 cells fail to divide and become large and multinucleate. Phenotypic characterization of the mutant cells showed that other cytoskeletal functions are preserved. The distribution of myosin and actin is identical to wild type cells. The cells can chemotax, phagocytose, cap crosslinked receptors, and contract normally. However, the 10BH2 mutants are unable to complete the Dictyostelium developmental program beyond the finger stage. The mutant cells contain functional genes for myosin II heavy and light chains and the racE gene. Thus, based on these findings, we conclude that 10BH2 represents a novel cytokinesis‐deficient mutant. © 1996 Wiley‐Liss, Inc.
Pose Graph Optimization in the Complex Domain: Lagrangian Duality, Conditions For Zero Duality Gap, and Optimal Solutions

Pose Graph Optimization (PGO) is the problem of estimating a set of poses from pairwise relative measurements. PGO is a nonconvex problem, and currently no known technique can guarantee the computation of an optimal solution. In this paper, we show that Lagrangian duality allows computing a globally optimal solution, under certain conditions that are satisfied in many practical cases. Our first contribution is to frame the PGO problem in the complex domain. This makes analysis easier and allows drawing connections with the recent literature on unit gain graphs. Exploiting this connection we prove non-trival results about the spectrum of the matrix underlying the problem. The second contribution is to formulate and analyze the dual problem in the complex domain. Our analysis shows that the duality gap is connected to the number of eigenvalues of the penalized pose graph matrix, which arises from the solution of the dual. We prove that if this matrix has a single eigenvalue in zero, then (i) the duality gap is zero, (ii) the primal PGO problem has a unique solution, and (iii) the primal solution can be computed by scaling an eigenvector of the penalized pose graph matrix. The third contribution is algorithmic: we exploit the dual problem and propose an algorithm that computes a guaranteed optimal solution for PGO when the penalized pose graph matrix satisfies the Single Zero Eigenvalue Property (SZEP). We also propose a variant that deals with the case in which the SZEP is not satisfied. The fourth contribution is a numerical analysis. Empirical evidence shows that in the vast majority of cases (100% of the tests under noise regimes of practical robotics applications) the penalized pose graph matrix does satisfy the SZEP, hence our approach allows computing the global optimal solution. Finally, we report simple counterexamples in which the duality gap is nonzero, and discuss open problems.
Evolution of blood-brain-barrier permeability after acute ischemic stroke

The dynamics of BBB permeability after AIS in humans are not well understood. In the present study we measured the evolution of BBB permeability after AIS in humans using MRI. Patients presenting to our institution with a diagnosis of AIS underwent a single dynamic contrast-enhanced MRI (DCE-MRI) sequence to measure BBB permeability during their initial workup. Forty-two patients were included in the final analysis. The patient sample underwent DCE-MRI at a mean time of 23.8hrs after the onset of AIS symptoms (range: 1.3–90.7hrs). At all time-points the BBB permeability within the infarct region of the brain as defined on DWI/ADC was higher compared to the homologous region of the contralateral hemisphere (p<0.005). BBB permeability, expressed as a ratio of infarct permeability to contralateral permeability, was greatest at 6-48hrs after the onset of AIS. Although the data was not acquired longitudinally, these findings suggest that the permeability of the BBB is continually elevated following AIS, which contradicts previous assertions that BBB permeability after AIS follows a biphasic course. Knowledge of BBB dynamics following AIS may provide insight into future treatments for AIS, especially BBB stabilizing agents.
Understanding and Automatically Preventing Injection Attacks on N ODE . JS

The NODE.JS ecosystem has lead to the creation of many modern applications, such as server-side web applications and desktop applications. Unlike client-side JavaScript code, NODE.JS applications can interact freely with the operating system without the benefits of a security sandbox. The complex interplay between NODE.JS modules leads to subtle injection vulnerabilities being introduced across module boundaries. This paper presents a large-scale study across 235,850 NODE.JS modules to explore such vulnerabilities. We show that injection vulnerabilities are prevalent in practice, both due to eval, which was previously studied for browser code, and due to the powerful exec API introduced in NODE.JS. Our study shows that thousands of modules may be vulnerable to command injection attacks and that even for popular projects it takes long time to fix the problem. Motivated by these findings, we present SYNODE, an automatic mitigation technique that combines static analysis and runtime enforcement of security policies for allowing vulnerable modules to be used in a safe way. The key idea is to statically compute a template of values passed to APIs that are prone to injections, and to synthesize a grammar-based runtime policy from these templates. Our mechanism does not require the modification of the NODE.JS platform, is fast (sub-millisecond runtime overhead), and protects against attacks of vulnerable modules while inducing very few false positives (less than 10%).
Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees

We explore the task of constructing a parallel Gibbs sampler, to both improve mixing and the exploration of high likelihood states. Recent work in parallel Gibbs sampling has focused on update schedules which do not guarantee convergence to the intended stationary distribution. In this work, we propose two methods to construct parallel Gibbs samplers guaranteed to draw from the targeted distribution. The first method, called the Chromatic sampler, uses graph coloring to construct a direct parallelization of the classic sequential scan Gibbs sampler. In the case of 2-colorable models we relate the Chromatic sampler to the Synchronous Gibbs sampler (which draws all variables simultaneously in parallel), and reveal new ergodic properties of Synchronous Gibbs chains. Our second method, the Splash sampler, is a complementary strategy which can be used when the variables are tightly coupled. This constructs and samples multiple blocks in parallel, using a novel locking protocol and an iterative junction tree generation algorithm. We further improve the Splash sampler through adaptive tree construction. We demonstrate the benefits of our two sampling algorithms on large synthetic and real-world models using a 32 processor multi-core system.