Miracle-dz/UniFAR-FTUs huggingface dataset:
In Vivo Stable Isotope Labeling of Fruit Flies Reveals Post-transcriptional Regulation in the Maternal-to-zygotic Transition*

An important hallmark in embryonic development is characterized by the maternal-to-zygotic transition (MZT) where zygotic transcription is activated by a maternally controlled environment. Post-transcriptional and translational regulation is critical for this transition and has been investigated in considerable detail at the gene level. We used a proteomics approach using metabolic labeling of Drosophila to quantitatively assess changes in protein expression levels before and after the MZT. By combining stable isotope labeling of fruit flies in vivo with high accuracy quantitative mass spectrometry we could quantify 2,232 proteins of which about half changed in abundance during this process. We show that ∼500 proteins increased in abundance, providing direct evidence of the identity of proteins as a product of embryonic translation. The group of down-regulated proteins is dominated by maternal factors involved in translational control of maternal and zygotic transcripts. Surprisingly a direct comparison of transcript and protein levels showed that the mRNA levels of down-regulated proteins remained relatively constant, indicating a translational control mechanism specifically targeting these proteins. In addition, we found evidence for post-translational processing of cysteine proteinase-1 (Cathepsin L), which became activated during the MZT as evidenced by the loss of its N-terminal propeptide. Poly(A)-binding protein was shown to be processed at its C-terminal tail, thereby losing one of its protein-interacting domains. Altogether this quantitative proteomics study provides a dynamic profile of known and novel proteins of maternal as well as embryonic origin. This provides insight into the production, stability, and modification of individual proteins, whereas discrepancies between transcriptional profiles and protein dynamics indicate novel control mechanisms in genome activation during early fly development.
Direct measurement of local dissolved oxygen concentration spatial profiles in a cell culture environment

Controlling local dissolved oxygen concentration (DO) in media is critical for cell or tissue cultures. Various biomaterials and culture methods have been developed to modulate DO. Direct measurement of local DO in cultures has not been validated as a method to test DO modulation. In the present study we developed a DO measurement system equipped with a Clark‐type oxygen microelectrode manipulated with 1 μm precision in three‐dimensional space to explore potential applications for tissue engineering. By determining the microelectrode tip position precisely against the bottom plane of culture dishes with rat or human cardiac cells in static monolayer culture, we successfully obtained spatial distributions of DO in the medium. Theoretical quantitative predictions fit the obtained data well. Based on analyses of the variance between samples, we found the data reflected “local” oxygen consumption in the vicinity of the microelectrode and the detection of temporal changes in oxygen consumption rates of cultured cells was limited by the diffusion rate of oxygen in the medium. This oxygen measuring system monitors local oxygen consumption and production with high spatial resolution, and can potentially be used with recently developed oxygen modulating biomaterials to design microenvironments and non‐invasively monitor local DO dynamics during culture. Biotechnol. Bioeng. 2015;112: 1263–1274. © 2015 Wiley Periodicals, Inc.
Automatic task graph generation techniques

Presents a model of parallel computation, the parameterized task graph, which is a compact, problem-size-independent representation of some frequently-used directed acylic task graphs. Techniques for automating the construction of such a representation, starting from an annotated sequential program, are proposed. We show how to represent the computational load for each node and the communication volume for each edge in a problem-size-independent way. Lastly, we describe related work and techniques required in order to generate local data structures, computation code and communication code. <<ETX>>
The M‐current inhibitor XE991 decreases the stimulation threshold for long‐term synaptic plasticity in healthy mice and in models of cognitive disease

Aging, mental retardation, number of psychiatric and neurological disorders are all associated with learning and memory impairments. As the underlying causes of such conditions are very heterogeneous, manipulations that can enhance learning and memory in mice under different circumstances might be able to overcome the cognitive deficits in patients. The M‐current regulates neuronal excitability and action potential firing, suggesting that its inhibition may increase cognitive capacities. We demonstrate that XE991, a specific M‐current blocker, enhances learning and memory in healthy mice. This effect may be achieved by altering basal hippocampal synaptic activity and by diminishing the stimulation threshold for long‐term changes in synaptic efficacy and learning‐related gene expression. We also show that training sessions regulate the M‐current by transiently decreasing the levels of KCNQ/Kv7.3 protein, a pivotal subunit for the M‐current. Furthermore, we found that XE991 can revert the cognitive impairment associated with acetylcholine depletion and the neurodegeneration induced by kainic acid. Together, these results show that inhibition of the M‐current as a general strategy may be useful to enhance cognitive capacities in healthy and aging individuals, as well as in those with neurodegenerative diseases. © 2009 Wiley‐Liss, Inc.
Evaluation of Face Recognition Algorithms on Avatar Face Datasets

Art metrics, a field of study that identifies, classifies and authenticates virtual reality avatars and intelligent software agents, has been proposed as a tool for fighting crimes taking place in virtual reality communities and in multiplayer game worlds. Forensic investigators are interested in developing tools for accurate and automated tracking and recognition of avatar faces. In this paper, we evaluate state of the art academic and commercial algorithms developed for human face recognition in the new domain of avatar recognition. While the obtained results are encouraging, ranging from 53.57% to 79.9% on different systems, the paper clearly demonstrated that there is room for improvement and presents avatar face recognition as an open problem to the pattern recognition and biometric communities.
E-Embargoes: Discouraging the Deployment of Traffic Manipulating Boxes With Economic Incentives

An increasing number of systems have been proposed or deployed to the transit core of the Internet with the goal of observing and manipulating traffic in flight, systems we term Traffic Manipulating Boxes. Examples of these include: decoy routing systems, surveillance infrastructure like the NSA's alleged QUANTUM project, and traffic shaping middleboxes. In this work, we examine a new approach that a routing capable adversary might take to resisting these systems: the use of economic pressure to incentivize ISPs to remove them. Rather than directly attacking the availability of these systems, our attack inflicts economic losses, in the form of reduced transit revenue, on ISPs that deploy them, while at the same time incentivizing ISPs that do not. We alter and expand upon previous routing around decoys attack of Schuchard et al., by adjusting the priority given to avoiding TMBs. This reduces or eliminates the key costs faced by routing capable adversary while maintaining the effectiveness of the attack. Additionally, we show that since the flow of traffic on the Internet is directly related to the flow of cash between ISPs, a routing capable adversary is actually a powerful economic adversary. Our findings show that by preferentially using routes which are free of TMBs, some routing capable adversaries can inflict in excess of a billion dollars in annual revenue losses.
Application of the BLEU Method for Evaluating Free-text Answers in an E-learning Environment

We have applied BLEU (Papineni et al., 2001), a method originally designed to evaluate automatic Machine Translation systems, in assessing short essays written by students. We study how much BLEU scores correlate to human scorings and other keyword-based evaluation metrics. We conclude that, although it is only applicable to a restricted category of questions, BLEU attains better results than other keyword-based procedures. Its simplicity and language-independence makes it a good candidate to be combined with other well-studied computer assessment scoring procedures.
Definition of standard ML

From the Publisher: Standard ML is general-purpose programming language designed for large projects. This book provides a formal definition of Standard ML for the benefit of all concerned with the language, including users and implementers. Because computer programs are increasingly required to withstand rigorous analysis, it is all the more important that the language in which they are written be defined with full rigor. The authors have defined their semantic objects in mathematical notation that is completely independent of Standard ML.
Comparative evaluation of the stochastic simplex bisection algorithm and the SciPy.Optimize module

The stochastic simplex bisection (SSB) algorithm is evaluated against the collection of optimizers in the Python SciPy.Optimize module on a prominent test set. The SSB algorithm greatly outperforms all SciPy optimizers, save one, in exactly half the cases. It does slightly worse on quadratic functions, but excels at trigonometric ones, highlighting its multimodal prowess. Unlike the SciPy optimizers, it sustains a high success rate. The SciPy optimizers would benefit from a more informed metaheuristic strategy and the SSB algorithm would profit from quicker local convergence and better multi-dimensional capabilities. Conversely, the local convergence of the SciPy optimizers is impressive and the multimodal capabilities of the SSB algorithm in separable dimensions are uncanny.
Adolescent internalizing symptoms and negative life events: The sensitizing effects of earlier life stress and cortisol

Abstract Although adolescence is marked by increased negative life events and internalizing problems, few studies investigate this association as an ongoing longitudinal process. Moreover, while there are considerable individual differences in the degree to which these phenomena are linked, little is known about the origins of these differences. The present study examines early life stress (ELS) exposure and early-adolescent longitudinal afternoon cortisol level as predictors of the covariation between internalizing symptoms and negative life events across high school. ELS was assessed by maternal report during infancy, and the measure of cortisol was derived from assessments at ages 11, 13, and 15 years. Life events and internalizing symptoms were assessed at ages 15, 17, and 18 years. A two-level hierarchical linear model revealed that ELS and cortisol were independent predictors of the covariation of internalizing symptoms and negative life events. Compared to those with lower levels of ELS, ELS-exposed adolescents displayed tighter covariation between internalizing symptoms and negative life events. Adolescents with lower longitudinal afternoon cortisol displayed tighter covariation between negative life events and internalizing symptoms, while those with higher cortisol demonstrated weaker covariation, partially due to increased levels of internalizing symptoms when faced with fewer negative life events.
Recognizing Human Postures and Poses in Monocular Still Images

In this paper, person detection with simultaneous or subsequent human body posture recognition is achieved using parts-based models, since the search space for typical poses is much smaller than the kinematics space. Posture recovery is carried out by detecting the human body, its posture and orientation at the same time. Since features of different human postures can be expected to have some shared subspace against the non-person class, detection and classification simultaneously is tenable. Contrary to many related efforts, we focus on postures that cannot be easily distinguished after segmentation by their aspect ratio or silhouette, but rather require a texture-based feature vector. The approaches presented do not rely on explicit models nor on labeling individual body parts. Both the detection and classification are performed in one pass on the image, where the score of the detection is an ensemble of votes from parts patches.
DTWFF-pitch feature and faster neural network convergence for speech recognition

This paper presents the pre-processing of speech templates for artificial neural network (ANN). The processed features are pitch and Linear Predictive Coefficients (LPC) for input and reference templates, based on Dynamic Time Warping (DTW) algorithm. The first task is to extract pitch features using Pitch Scale Harmonic Filter algorithm. Another task is to align the input frames (test set) to the reference template (training set) using DTW fixing frame (DTW-FF) algorithm. This is a time normalization process in which it is needed for data with unequal length. By doing time normalization, the test set and the training set are adjusted to the same number of frames. Having both pitch and LPC features fixed frames, speech recognition using neural network can be performed. A high recognition rate is obtained using combined features of DTW-FF and pitch for Malay digit words of 0-9, as high as 100% is achieved. Another task included in this paper is to find the optimal global minimum of the NN surface using the conjugate gradient algorithm to replace the steepest gradient descent in the back-propagation algorithm. Results showed that conjugate gradient algorithm is able to find a better optimal global minimum.
Unbiased random mutagenesis contributes to a better understanding of the virulent behaviour of Paenibacillus larvae

American foulbrood, caused by the Gram‐positive bacteria Paenibacillus larvae, is one of the most severe bacterial diseases of the European honey bee. The bacterium has been known for long, but only the last decade the mechanisms used by the pathogen to cause disease in its host are starting to unravel. In this study, the knowledge of this virulent behaviour is expanded and several possible virulence factors are suggested.
Aviation Fuel Cavitation in a CD Nozzle: A Quantitative Experimental Characterization

This work describes experiments aimed at clarifying several physical aspects of aviation fuel cavitation. The experiments are performed in a simple two-dimensional converging-diverging (CD) nozzle geometry with JP-8 fuel replaced by dodecane, a single component surrogate and its primary component by weight. Experiments are focused on gaseous cavitation with air micro-bubbles injected at the nozzle inlet. Carefully controlled initial void fraction leads to bubble growth in the bulk of the fluid and quasi-1D flow patterns downstream. These experiments seek to characterize the flow patterns with particular focus on propagating shock waves that form due to collapse of gaseous bubble clouds. Experimental documentation is made through arrays of static and unsteady pressure sensors, high-speed video and a volumetric flow rate meter. The results are analyzed to obtain choked mass flow rate, distributions of mean streamwise pressure, shock propagation speed, local shock passage frequency, and static pressure jump across the bubbly shock.
Vaginal lactobacilli as potential probiotics against Candida SPP.

Urogenital infections affect millions of people every year worldwide. The treatment of these diseases usually requires the use of antimicrobial agents, and more recently, the use of probiotic lactic acid bacteria (LAB) cultures for the management of vaginal infections has been extensively studied. In this work, 11 vaginal lactobacilli isolates, previously obtained from healthy patients, were studied to screen microorganisms with probiotic properties against Candida spp. The LAB were tested for their ability of auto-aggregation, co-aggregation with C. albicans, C. glabrata, C. krusei, and C. tropicalis, adhesion to Caco-2 epithelial cells and production of lactic acid and hydrogen peroxide (H2O2). All lactobacilli isolates tested were able to auto-aggregate (ranging from 25.3% to 75.4% assessed at 4 hours of incubation) and to co-aggregate with the four Candida species into different degrees; among them L. crispatus showed the highest scores of co-aggregation. The highest amount of lactic acid was produced by L. salivarius (13.9 g/l), followed by L. johnsonii (6.5 g/l), L. acidophilus (5.5 g/l), and L. jensenii (5.4 g/l). All isolates produced H2O2, but the highest levels (3 – 10 mg/l) were observed for L. acidophilus, L. crispatus, L. gasseri, L. johnsonii, and L. vaginalis. Only L. agilis, L. jensenii, L. johnsonii and L. ruminus were able to adhere to epithelial Caco-2 cells. Among the isolates evaluated, L agilis, L. jensenii, L. johnsonii, and L. ruminus exhibited simultaneously several desirable properties as potential probiotic strains justifying future studies to evaluate their technological properties in different pharmaceutical preparations for human use.
Data-driven fault diagnosis of a wind farm benchmark model

The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances.
Anti-Gamma Interferon Antibodies Enhance the Immunogenicity of Recombinant Adenovirus Vectors

ABSTRACT Vaccination for eliciting antigen-specific memory CD8+ T cells may be facilitated by manipulating the pleiotropic effects of gamma interferon (IFN-γ). We assessed strategies for modulating the contribution of IFN-γ during the development of antigen-specific cytotoxic T lymphocyte (CTL) populations. We first showed that recombinant IFN-γ suppressed antigen expression in vitro from a recombinant adenovirus (rAd) vector in a dose-dependent manner and that addition of an anti-IFN-γ antibody (Ab) eliminated this suppression. Consistent with these in vitro findings, we found that HIV-1 envelope (Env)-specific CTL responses were higher in IFN-γ-knockout (GKO) mice than in wild-type mice following immunization with rAd. Since these observations suggested that IFN-γ might suppress rAd-induced CTL development, we assessed the ability of anti-IFN-γ Ab administration to augment rAd-elicited CTL in vivo. In fact, blockage of IFN-γ activity by monoclonal Ab administration was associated with elevated levels of interleukin 7 receptor alpha chain-positive (IL-7Rα+) Env-specific CTL populations postboost. These observations illustrate the utility of an anti-IFN-γ Ab for potentiating rAd immunizations to effect quantitative and qualitative changes in the effector and memory CTL populations.
Sense and Antisense DMPK RNA Foci Accumulate in DM1 Tissues during Development

Myotonic dystrophy type 1 (DM1) is caused by an unstable expanded CTG repeat located within the DMPK gene 3’UTR. The nature, severity and age at onset of DM1 symptoms are very variable in patients. Different forms of the disease are described, among which the congenital form (CDM) is the most severe. Molecular mechanisms of DM1 are well characterized for the adult form and involve accumulation of mutant DMPK RNA forming foci in the nucleus. These RNA foci sequester proteins from the MBNL family and deregulate CELF proteins. These proteins are involved in many cellular mechanisms such as alternative splicing, transcriptional, translational and post-translational regulation miRNA regulation as well as mRNA polyadenylation and localization. All these mechanisms can be impaired in DM1 because of the deregulation of CELF and MBNL functions. The mechanisms involved in CDM are not clearly described. In order to get insight into the mechanisms underlying CDM, we investigated if expanded RNA nuclear foci, one of the molecular hallmarks of DM1, could be detected in human DM1 fetal tissues, as well as in embryonic and neonatal tissues from transgenic mice carrying the human DMPK gene with an expanded CTG repeat. We observed very abundant RNA foci formed by sense DMPK RNA and, to a lesser extent, antisense DMPK RNA foci. Sense DMPK RNA foci clearly co-localized with MBNL1 and MBNL2 proteins. In addition, we studied DMPK sense and antisense expression during development in the transgenic mice. We found that DMPK sense and antisense transcripts are expressed from embryonic and fetal stages in heart, muscle and brain and are regulated during development. These results suggest that mechanisms underlying DM1 and CDM involved common players including toxic expanded RNA forming numerous nuclear foci at early stages during development.
Modelling and validation of flow over a wall with large surface roughness

Fluid flow over surfaces with large surface roughness is important in many applications such as hydropower tunnels and natural channels. When creating numerical models of such applications it is very impractical to include the wall roughness in the geometrical model both with regards to the geometric resolution required and the large computational cost of performing such simulations. Wall roughness is therefore typically estimated as a global parameter by some empirical relation and added mathematically into numerical simulations. It is here examined if combining partially resolved geometric wall roughness with partially modelled roughness can increase the quality of simulations in a quantitative manner. A high-resolution laser scanning of an excavated tunnel was used to create a geometry for Computational Fluid Dynamics (CFD) simulations and a scale model of the geometry (1:10) was constructed for experimental validation. A section of the side wall from the tunnel with dimensions 40x2.5 m was selected for the numerical and experimental models. The height of the roughness elements for this wall segment varied between 0-0.8 m. This rough wall was then deployed as a side wall in a rectangular channel where the other walls were considered smooth. Simulations were performed with the commercial software CFX13 from Ansys Inc. A k − turbulence model with standard wall functions was applied and the geometry of the rough surface was resolved to different degrees by numerical grids with varying element sizes. A numerical roughness is then added to the wall with partially resolved geometrical roughness to give a total roughness corresponding to the actual tunnel wall. Particle Image Velocimetry (PIV) measurements were made on the scale model to validate the simulations. By combining the partially resolved surface with a numerical wall roughness it is possible to obtain a model that is more accurate than a model with only numerical roughness but with a much lower computational cost than a model that fully resolves the discrete roughness elements of the surface.