Miracle-dz/UniFAR-FTUs huggingface dataset:
In vivo selection for metastasis promoting genes in the mouse

Here, we report the identification of a metastasis promoting factor by a forward genetic screen in mice. A retroviral cDNA library was introduced into the nonmetastatic cancer cell line 168FARN, which was then orthotopically transplanted into mouse mammary fat pads, followed by selection for cells that metastasize to the lung. The genes encoding the disulfide isomerase ERp5 and β-catenin were found to promote breast cancer invasion and metastasis. Disulfide isomerases (thiol isomerases), which catalyze disulfide bond formation, reduction, and isomerization, have not previously been implicated in cancer cell signaling and tumor metastasis. Overexpression of ERp5 promotes both in vitro migration and invasion and in vivo metastasis of breast cancer cells. These effects were shown to involve activation of ErbB2 and phosphoinositide 3-kinase (PI3K) pathways through dimerization of ErbB2. Activation of ErbB2 and PI3K subsequently stimulates RhoA and β-catenin, which mediate the migration and invasion of tumor cells. Inhibition of ErbB2 and PI3K reverses the phenotypes induced by ERp5. Finally, ERp5 was shown to be up-regulated in human surgical samples of invasive breast cancers. These data identify a link between disulfide isomerases and tumor development, and provide a mechanism that modulates ErbB2 and PI3K signaling in the promotion of cancer progression.
NetTRUST: mixed NETworks Trust infrastRUcture baSed on Threshold cryptography

The proliferation of network technologies (wired, cellular, ad-hoc, etc.) leads to many different network architectures. These different architectures cohabitate to provide services and contents to end customers. In order to secure services in such mixed networks, it is necessary to rely on a homogeneous trust model. The trust model must define trust relationships between the mixed architecture actors, provide elementary ingredients to secure top level services, and guarantee the security service availability. In this paper, we propose a trust infrastructure for mixed networks architectures. The model uses two particular certification authorities, which ensure X509v3 certificates management: the central certification authorities (CCA) are tied to the portions of the network having a pre-existent communication infrastructure (such as wired networks, cellular networks, etc.), and mobile certification authorities (MCA) which are on the ad-hoc portion of the network. The MCA servers emulate the certification authority role using a (k, n) threshold cryptography scheme, and the CCA servers delegate the role of certification to the MCA servers by using a (t,m) scheme of threshold cryptography. This solution is decentralized and partially distributed, supports the nodes mobility and the failure of, up to n-k, among n MCA servers. The simulation results and the performance evaluation prove the adequacy of this solution to mixed networks architectures.
Guilty Molecules, Guilty Minds? The Conflicting Roles of the Innate Immune Response to Traumatic Brain Injury

Traumatic brain injury (TBI) is a complex disease in the most complex organ of the body, whose victims endure lifelong debilitating physical, emotional, and psychosocial consequences. Despite advances in clinical care, there is no effective neuroprotective therapy for TBI, with almost every compound showing promise experimentally having disappointing results in the clinic. The complex and highly interrelated innate immune responses govern both the beneficial and deleterious molecular consequences of TBI and are present as an attractive therapeutic target. This paper discusses the positive, negative, and often conflicting roles of the innate immune response to TBI in both an experimental and clinical settings and highlights recent advances in the search for therapeutic candidates for the treatment of TBI.
Optimization of temporal filters for constructing robust features in speech recognition

Linear discriminant analysis (LDA) has long been used to derive data-driven temporal filters in order to improve the robustness of speech features used in speech recognition. In this paper, we proposed the use of new optimization criteria of principal component analysis (PCA) and the minimum classification error (MCE) for constructing the temporal filters. Detailed comparative performance analysis for the features obtained using the three optimization criteria, LDA, PCA, and MCE, with various types of noise and a wide range of SNR values is presented. It was found that the new criteria lead to superior performance over the original MFCC features, just as LDA-derived filters can. In addition, the newly proposed MCE-derived filters can often do better than the LDA-derived filters. Also, it is shown that further performance improvements are achievable if any of these LDA/PCA/MCE-derived filters are integrated with the conventional approach of cepstral mean and variance normalization (CMVN). The performance improvements obtained in recognition experiments are further supported by analyses conducted using two different distance measures.
Feasibility, Acceptability, and Potential Effectiveness of Dignity Therapy for People with Motor Neurone Disease

Background Motor neurone disease (MND) practice guidelines suggest developing interventions that will promote hope, meaning, and dignity to alleviate psychological distress, but very little research has been done. This study begins to address this need by exploring the use of dignity therapy with people with MND. Dignity therapy is a brief psychotherapy that promotes hope, meaning and dignity, and enhances the end of life for people with advanced cancer. The aims of this study are to assess the feasibility, acceptability, and potential effectiveness of dignity therapy for people with MND. Methods/design This cross-sectional feasibility study used a one-group pre-test post-test design with 29 people diagnosed with MND. Study participants completed the following self-report questionnaires: Herth Hope Index, FACIT-sp, Patient Dignity Inventory, ALS Assessment Questionnaire, ALS Cognitive Behavioural Screen, and a demographic and health history questionnaire. Acceptability was measured with a 25-item feedback questionnaire. Feasibility was assessed by examining the length of time taken to complete dignity therapy and how symptoms common in MND affected the intervention. Generalised linear mixed models and reliable change scores were used to analyse the data. Results There were no significant pre-test post-test changes for hopefulness, spirituality or dignity on the group level, but there were changes in hopefulness on the individual level. The results of the feedback questionnaire indicates dignity therapy is highly acceptable to people with MND, who report benefits similar to those in the international randomised controlled trial on dignity therapy, a population who primarily had end-stage cancer. Benefits include better family relationships, improved sense of self and greater acceptance. Dignity therapy with people with MND is feasible if the therapist can overcome time and communication difficulties. Conclusions Dignity therapy for people with MND is feasible and acceptable. Further research is warranted to explore its ability to diminish distress. Trial Registration www.anzctr. org.au ACTRN12611000410954
Simulation-Extrapolation Gaussian Processes for Input Noise Modeling

Input noise is common in situations when data either is coming from unreliable sensors or previous outputs are used as current inputs. Nevertheless, most regression algorithms do not model input noise, inducing thus bias in the regression. We present a method that corrects this bias by repeated regression estimations. In simulation extrapolation we perturb the inputs with additional input noise and by observing the effect of this addition on the result, we estimate what would the prediction be without the input noise. We extend the examination to a non-parametric probabilistic regression, inference using Gaussian processes. We conducted experiments on both synthetic data and in robotics, i.e., Learning the transition dynamics of a dynamical system, showing significant improvements in the accuracy of the prediction.
CVS release history data for detecting logical couplings

The dependencies and interrelations between classes and modules affect the maintainability of object-oriented systems. It is therefore important to capture weaknesses of the software architecture to make necessary corrections. We describe a method for software evolution analysis. It consists of three complementary steps, which form an integrated approach for the reasoning about software structures based on historical data: 1) the quantitative analysis uses version information for the assessment of growth and change behavior; 2) the change sequence analysis identifies common change patterns across all system parts; and 3) the relation analysis compares classes based on CVS release history data and reveals the dependencies within the evolution of particular entities. We focus on the relation analysis and discuss its results; it has been validated based on empirical data collected from a concurrent versions system (CVS) covering 28 months of a picture archiving and communication system (PACS). Our software evolution analysis approach enabled us to detect shortcomings of PACS such as architectural weaknesses, poorly designed inheritance hierarchies, or blurred interfaces of modules.
Measuring empirical computational complexity

The standard language for describing the asymptotic behavior of algorithms is theoretical computational complexity. We propose a method for describing the asymptotic behavior of programs in practice by measuring their empirical computational complexity. Our method involves running a program on workloads spanning several orders of magnitude in size, measuring their performance, and fitting these observations to a model that predicts performance as a function of workload size. Comparing these models to the programmer's expectations or to theoretical asymptotic bounds can reveal performance bugs or confirm that a program's performance scales as expected. Grouping and ranking program locations based on these models focuses attention on scalability-critical code. We describe our tool, the Trend Profiler (trend-prof), for constructing models of empirical computational complexity that predict how many times each basic block in a program runs as a linear (y = a + bx) or a powerlaw (y = axb) function of user-specified features of the program's workloads. We ran trend-prof on several large programs and report cases where a program scaled as expected, beat its worst-case theoretical complexity bound, or had a performance bug.
Extracting multi-dimensional relations: a generative model of groups of entities in a corpus

Extracting relations among different entities from various data sources has been an important topic in data mining. While many methods focus only on a single type of relations, real world entities maintain relations that contain much richer information. We propose a hierarchical Bayesian model for extracting multi-dimensional relations among entities from a text corpus. Using data from Wikipedia, we show that our model can accurately predict the relevance of an entity given the topic of the document as well as the set of entities that are already mentioned in that document.
Environmentally Induced Physical Changes in Ancient Kauri ( Agathis Australis ) Wood

The physical properties of 30,000-year-old and modern kauri ( Agathis australis ) were explored. The acidic burial environment had modified the ancient wood cell-wall structure, affecting strength properties. Degradation of labile cell-wall polysaccharides resulted in strength differences in the ancient kauri both below and above the proportional limit (PL) of the wood. Significantly lower modulus of elasticity (MOE) and work to proportional limit (WPL) values, as compared with modern kauri, indicated that the ancient material was more readily deformed. Above the PL, the modulus of rupture (MOR) of the ancient wood was significantly lower and the work to maximum load (WML) significantly higher than the corresponding values for recently felled kauri, suggesting that the ancient material did not resist stress; rather, the structure gradually "gave way" under imposed stress. Scanning electron micrographs (SEM) of the fractured ancient kauri surfaces reveal degradation of the woody cell wall.
Transient Response in PPD CMOS Image Sensors Irradiated by Gamma Rays: Variation of Dose Rates and Integration Times

The transient responses in CMOS image sensors (CISs) during radiation are one of the most important key issues to the detector design, reliability, and applicability. The radiation experiments of transient responses in CISs are presented. The CISs have a four Megapixels and pinned photodiode (PPD) pixel architecture with a standard 0.18- $\mu \text{m}$ CMOS technology. The transient responses induced by the gamma radiation at the dose rates of 0.01, 0.02, 0.05, 0.1, 0.2, and 0.3 rad(Si)/s are compared to analyze the influence of dose rates. The transient responses of the PPD CISs at different integration times ranged from 1.02 to 20.51 ms during radiation are also analyzed. The transient response characteristics of the PPD CISs induced by the gamma radiation present a transient disturbance and a random signal distribution. The experimental results show that the mean signal levels increase linearly with increasing dose rates or integration times during radiation, which indicates a method to improve the CIS detector precision. The mechanisms of the transient responses in the PPD CISs are also demonstrated.
A New Robust Adaptive-Fuzzy Control Method Applied to Quadrotor Helicopter Stabilization

A new method for adaptive-fuzzy control achieves stabilization of a quadrotor helicopter in the presence of sinusoidal wind disturbance. Techniques traditionally used in adaptive control for robust parameter updates may not be sufficient for fuzzy schemes. In particular, e-modification may result in the fuzzy-membership centers drifting to large values when persistent oscillations are present in the input. These large values can cause control signal chatter, which can be undesirable or even cause instability if they excite unmodeled dynamics. A new method for robust updates is proposed that prevents this drift in the fuzzy membership centers. In the new method, a set of alternate membership function centers guides the adaptation process in order to prevent drift. A Lyapunov-stability proof ensures the uniform ultimate boundedness of all signals. A simulation of a quadrotor helicopter demonstrates the high performance and robust stability of the new method
The relationship of psycho-physiological responses and self-reported emotions while listening to music

We combined a continuous self report technique with psycho-physiological measures. The researcher-developed software ”EMuJoy” was developed as an extension of Schubert’s method, which enabled subjects to express their emotions ”on-line” while listening to music. The dimensions used were valence (negative-positive) and arousal (high-low). Subjects controlled the software with a computer mouse. One of the mouse buttons was used to indicate chill experiences. As psycho-physiological measures, heart rate (HR), skin conductivity response (SCR) and level (SCL), skin temperature, breathing rate and an electromyogram of two facial muscles (the cygomaticus and the corrugator) were recorded. In contrast to previous studies, subjects were instructed to express their own perceived emotions and not to rate the emotional expression intended by the composer. The experiment started with a learning phase during which subjects got accustomed to the continuous rating procedure. Handling the ”EMuJoy” software was trained by rating perceived emotions while looking at ten pictures from the International Aective Picture System [4]. These pictures were selected according to the two-dimensional
Low-Resource Routing Attacks Against Anonymous Systems ; CU-CS-1025-07

Overlay mix-networks are widely used to provide low-latency anonymous communication services. It is generally accepted that, if an adversary can compromise the endpoints of a path through an anonymous mix-network, then it is possible to ascertain the identities of a requesting client and the responding server. However, theoretical analyses of anonymous mix-networks show that the likelihood of such an end-to-end attack becomes negligible as the network size increases. We show that if the mix-network attempts to optimize performance by utilizing a preferential routing scheme, then the system is highly vulnerable to attacks from non-global adversaries with only a few malicious servers. We extend this attack by exploring methods for low-resource nodes to be perceived as high-resource nodes by reporting false resource claims to centralized routing authorities. To evaluate this attack on a mature and representative system, we deployed an isolated Tor network on the PlanetLab testbed. We introduced low-resource malicious nodes that falsely gave the illusion of high-performance nodes, which allowed them to be included on a disproportionately high number of paths. Our results show that our malicious low-resource nodes are highly effective at compromising the end-to-end anonymity of the system. We present several extensions to this general attack that further improve the performance and minimize the resources required. In order to mitigate low-resource nodes from exploiting preferential routing, we present several methods to verify resource claims, including a distributed reputation system. Our attacks suggest what seems be a fundamental problem in multi-hop systems that attempt to simultaneously provide anonymity and high-performance.
Code Completion with Neural Attention and Pointer Networks

Intelligent code completion has become an essential research task to accelerate modern software development. To facilitate effective code completion for dynamically-typed programming languages, we apply neural language models by learning from large codebases, and develop a tailored attention mechanism for code completion. However, standard neural language models even with attention mechanism cannot correctly predict the out-of-vocabulary (OoV) words that restrict the code completion performance. In this paper, inspired by the prevalence of locally repeated terms in program source code, and the recently proposed pointer copy mechanism, we propose a pointer mixture network for better predicting OoV words in code completion. Based on the context, the pointer mixture network learns to either generate a within-vocabulary word through an RNN component, or regenerate an OoV word from local context through a pointer component. Experiments on two benchmarked datasets demonstrate the effectiveness of our attention mechanism and pointer mixture network on the code completion task.
Spectral unmixing based on a minimum volume simplicial enclosure model

This work describes the minimum volume enclosing simplex problem, which is known to be a mul- timodal Global Optimization problem. The problem has been used as a basis to estimate so-called endmember and abundance data in unmixing spectral data ofhyperspectralsensors. This estimation problem is a big challenge. We explore the possibility of a new estimation algorithm using the min- imum volume enclosing simplex problem. We investigate its behaviour numerically on designed instances comparing its outcomes with a maximum volume enclosed simplex approach which is used frequently in spectral unmixing.
Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm

The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.
IMAGE STRUCTURE ANALYSIS BY 3-STAGES CLUSTERING

An approach for decomposition of visual images by clustering and breaking them down into geometric figures is considered. Multilevel hierarchical clustering algorithm to form three emphasized levels of clusters such as rectangles, closed regions and integrated areas is proposed. Advantages of such decomposition in three stages are as follows: images covered by rectangles are planned to be formatted and compressed, image fragments could be taken for the preliminary pattern recognition or could easily be corrected, hierarchically constructed fragments are good material to form pattern features for searching procedures. The algorithm complexity, the proposed approach of scanning searching area to reduce it, the rolling up criteria and key parameters for its control are investigated. The results of pattern analysis by structure features for some practical problems are presented in the article.
C358A Polymorphism of the Endocannabinoid Degrading Enzyme Fatty Acid Amide Hydrolase (FAAH) Influence On Metabolic Parameters a High Protein/Low Carbohydrate versus a Standard Hypocaloric Diet

Background and aim: The C385A polymorphism of FAAH gene (rs324420C>A) has been associated with obesity. We investigate the role of this polymorphism on cardiovascular risk factors and weight loss secondary to a high protein/ low carbohydrate vs. a standard hypocaloric diets (1000 kcal/day) during 9 months. Methods: A sample of 284 subjects with obesity (body mass index (BMI) >30) was enrolled. These subjects were randomly allocated to one of two diets for a period of nine months Diet S (standard protein hypocaloric diet) vs. Diet HP (high protein-low carbohydrate hypocaloric diet). Results: After both diets and in both genotype groups (CC vs. CA+AA), body mass index (BMI), weight, fat mass, waist circumference and systolic blood pressure decreased. With the diet type HP and in non A carriers, glucose (-5.3 ± 1.2 mg/dl vs. -1.8 ± 2.1 mg/dl; p<0.05), insulin levels (-3.1 ± 1.9UI/L vs. -1.1 ± 2.0 UI/L; p<0.05), HOMA-R (-0.9 ± 0.8 units vs. -0.3 ± 1.0 units; p<0.05), total cholesterol (-11.9 ± 8.2 mg/dl vs. -0.1 ± 3.1mg/dl; p<0.05), and LDL- total cholesterol (-9.8 ± 4.2 mg/dl vs. -1.0 ± 2.1mg/dl; p<0.05) decreased. After diet S and in patients with both genotypes, total cholesterol (-6.0 ± 3.1 mg/dl vs. -10.0 ± 8.2mg/dl; ns), triglycerides (-8.1 ± 7.1 mg/dl vs. -13.1 ± 8.9 mg/dl; ns) and LDL- total cholesterol (-5.9 ± 3.0 mg/dl vs. -9.1 ± 5.8mg/dl; p<0.05) decreased. Conclusion: Non carriers of the allele A385 of FAAH showed an improvement on insulin and HOMA-R levels with a high protein hypocaloric diet after weight loss during 9 months. A standard hypocaloric diet produced a similar improvement in lipid profile in both genotypes.