Miracle-dz/UniFAR-FTUs huggingface dataset:
Emerging roles for histone deacetylases in age-related muscle atrophy

BACKGROUND: Skeletal muscle atrophy during aging, a process known as sarcopenia, is associated with muscle weakness, frailty, and the loss of independence in older adults. The mechanisms contributing to sarcopenia are not totally understood, but muscle fiber loss due to apoptosis, reduced stimulation of anabolic pathways, activation of catabolic pathways, denervation, and altered metabolism have been observed in muscle from old rodents and humans. OBJECTIVE: Recently, histone deacetylases (HDACs) have been implicated in muscle atrophy and dysfunction due to denervation, muscular dystrophy, and disuse, and HDACs play key roles in regulating metabolism in skeletal muscle. In this review, we will discuss the role of HDACs in muscle atrophy and the potential of HDAC inhibitors for the treatment of sarcopenia. CONCLUSIONS: Several HDAC isoforms are potential targets for intervention in sarcopenia. Inhibition of HDAC1 prevents muscle atrophy due to nutrient deprivation. HDAC3 regulates metabolism in skeletal muscle and may inhibit oxidative metabolism during aging. HDAC4 and HDAC5 have been implicated in muscle atrophy due to denervation, a process implicated in sarcopenia. HDAC inhibitors are already in use in the clinic, and there is promise in targeting HDACs for the treatment of sarcopenia.
First Do No Harm: Technology-Induced Risks to Wellbeing

Screen-media use is associated with a variety of impacts on long-term social, emotional, and physical wellbeing – both positive and negative. The negative consequences of technology use have received considerable attention in the context of child development. In this position paper we describe some of the known implications of children’s and adolescents’ use of technology and propose avenues by which the design community might support families in reflecting on their desired patterns of use and living up to these self-defined ideals.
Concept Term Expansion Approach for Monitoring Reputation of Companies on Twitter

The aim of this contribution is to easily monitor the reputa- tion of a company in the Twittersphere. We propose a strategy that orga- nizes a stream of tweets into different clusters based on the tweets topics. Furthermore, the obtained clusters are prioritized into different priority levels. A cluster with high priority represents a topic which may affect the reputation of a company, and that consequently deserves immediate attention. The evaluation results show that our method is competitive even though the method does not make use of any external knowledge resource.
Solving Go on Small Boards

This article presents a search-based approach of solving Go on small boards. A dedicated heuristic evaluation function combined with the static recognition of unconditional territory is used in an alpha-beta framework with several domain-dependent and domain-independent search enhancements. We present two variants of the GHI problem (caused by super-ko rules) with some possible solutions. Our program, MIGOS, solves all small empty square boards up to 5 5 and can be applied to any enclosed problem of similar size.
A frequency domain differential modulation scheme for asynchronous amplify-and-forward relay networks

In this paper, we investigate cyclic prefixed single carrier block transmission for asynchronous wireless relay networks. To avoid channel estimation at the receiver, a simple frequency-domain differential encoding and decoding scheme is proposed for asynchronous relaying. Specifically, a block-by-block frequency domain differential encoder is employed at the source node, while joint frequency domain differential decoding and equalization is developed at the destination to recover the information symbols. Numerical results are presented to demonstrate the bit-error-rate (BER) performance of the proposed scheme. It is shown that the proposed cyclic prefixed frequency domain differential transmission scheme is robust to asynchronous delays and Doppler frequency shifts.
A Theory of Silicon Valley

This paper presents a model of the software-focused, venture-backed entrepreneurship found in the San Francisco Bay Area, or “Silicon Valley. ” Three kinds of markets matter in the model: (1) the financial market for venture capital, (2) the labor market for engineers, and (3) the collection of product markets successful technology companies serve. The key economic actors are elite “engineers” who decide between employment and entrepreneurship. Wages clear the labor market; retained equity clears the venture capital market. The model predicts the equilibrium startup probability of success, startup valuations, engineer wages, the returns to entrepreneurship and the split of engineers between entrepreneurship and employment. What affects the equilibrium are the direct cost of doing a startup, the cost of capital, the extent of product markets, the supply of engineers, and the innovation “environment. ” In addition to presenting the theory, I also introduce a novel dataset on the career trajectories of paradigmatic elite engineers—Stanford Computer Science PhD graduates—to illustrate some of the stylized facts motivating the model.
Marginal Stability and the Metamorphosis of BPS States

We discuss the restructuring of the BPS spectrum which occurs on certain submanifolds of the moduli/parameter space – the curves of the marginal stability (CMS) – using quasiclassical methods. We argue that in general a ‘composite’ BPS soliton swells in coordinate space as one approaches the CMS and that, as a bound state of two ‘primary’ solitons, its dynamics in this region is determined by non-relativistic supersymmetric quantum mechanics. Near the CMS the bound state has a wave function which is highly spread out. Precisely on the CMS the bound state level reaches the continuum, the composite state delocalizes in coordinate space, and restructuring of the spectrum can occur. We present a detailed analysis of this behavior in a two-dimensional N = 2 Wess-Zumino model with two chiral ﬁelds, and then discuss how it arises in the context of ‘composite’ dyons near weak coupling CMS curves in N =2 supersymmetric gauge theories. We also consider cases where some states become massless on the CMS.
Construction of a coherency preserving dynamic data dissemination network

In this paper, we discuss various techniques for the efficient organization of a coherency preserving dynamic data dissemination network. The network consists of sources of dynamically changing data, repositories to serve this data, and clients. Given the coherency properties of the data available at various repositories, we suggest methods to intelligently choose a repository to serve a new client request. The goal is to support as many clients as possible, from the given network. Secondly, we propose strategies to decide what data should reside on the repositories, given the data coherency needs of the clients. We model the problem of selection of repositories for serving each of the clients as a linear optimization problem, and derive its objective function and constraints. In view of the complexity and infeasibility of using this solution in practical scenarios, we also suggest a heuristic solution. Experimental evaluation, using real world data, demonstrates that the fidelity achieved by clients using the heuristic algorithm is close to that achieved using linear optimization. To improve the fidelity further through better load sharing between repositories, we propose an adaptive algorithm to adjust the resource provisions of repositories according to their recent response times. It is often advantageous to reorganize the data at the repositories according to the needs of clients. To this end, we propose two strategies based on reducing the communication and computational overheads. We evaluate and compare the two strategies, analytically, using the expected response time for an update at repositories, and by simulation, using the loss of fidelity at clients, as our performance measure. The results suggest that a considerable improvement infidelity can be achieved by judicious reorganization.
Expression of cGMP-Specific Phosphodiesterase 9A mRNA in the Rat Brain

cGMP has been implicated in the regulation of many essential functions in the brain, such as synaptic plasticity, phototransduction, olfaction, and behavioral state. Cyclic nucleotide phosphodiesterase (PDE) hydrolysis of cGMP is the major mechanism underlying the clearance of cGMP and is likely to be important in any process that depends on intracellular cGMP. PDE9A has the highest affinity for cGMP of any PDE, and here we studied the localization of this enzyme in the rat brain using in situ hybridization. PDE9A mRNA is widely distributed throughout the brain with varying regional expression. The pattern of PDE9A mRNA expression closely resembles that of soluble guanylyl cyclase (sGC) in the rat brain, suggesting a possible functional association or coupling of these two enzymes in the regulation of cGMP levels. Most of the brain areas expressing PDE9A mRNA also contain neuronal nitric oxide synthase (NOS), the enzymatic source of NO and the principal activator of sGC. PDE9A is the only cGMP-specific PDE with significant expression in the forebrain, and as such is likely to play an important role in NO–cGMP signaling.
Window frames as areas for information visualization

We describe how the frames of document windows can be used as areas for visualizing information about a document. A number of design examples are presented as illustrations of how users can be supported with contextual information about a document in a way that does not interfere with the visual presentation of the document itself. A ChangeIndicator is used as an example of how information can be mapped to basic design variables of a frame, such as its color. More complex visualizations can be achieved by mapping information about parts of documents to parts or details of the frame. This is illustrated with a Readability-Viewer. Finally, the ScrollSearcher is presented as an example of how the results of processes and functions can be visualized in the frames of a document.
A New Increasing Translation Invariant Morphological Method for Financial Time Series Forecasting

This paper presents a new method, referred to as Increasing Translation Invariant Morphological (ITIM), to overcome the random walk dilemma for financial time series forecasting. It consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) and a Modified Genetic Algorithm (MGA), which searches for the minimum number of time lags for a fine tuned time series representation, as well as by the initial weights, architecture and number of modules of the MMNN. Each element of the MGA population is trained via Back Propagation (BP) algorithm to further improve the parameters supplied by the MGA. The proposed method, after forecasting model adjustment, performs a behavioral statistical test and a phase fix procedure to adjust time phase distortions that appear in financial time series. An experimental analysis is conducted with the proposed method using two real world time series and five well-known performance measurements, demonstrating consistent better performance of this kind of morphological system.
Long-term affect sensitive and socially interactive companions

This paper presents some of the requirements for the design of long-term affect sensitive and socially interactive companions. Research conducted in the EU project LIREC (LIving with Robots and intEractive Companions) envisage some capabilities including affect sensitivity, memory and learning, cognitive and expressive behaviour, personalisation and embodiment, highlighting the key issues that research on artificial companions should address. 1
A PERFORMANCE STUDY OF ADAPTIVE FEC MECHANISM FOR VIDEO TRANSMISSION OVER 802 . 11 WLANS

Forward Error Correction has been implemented with Automatic Repeat reQuest to overcome packet losses and avoid network congestion in various wireless network conditions. The number of FEC packets is needed to be generated adaptively due to various network states and burst error condition. There are a number of proposed Adaptive FEC mechanisms that can generate FEC packets adaptively for wireless network. However, this current Adaptive FEC, namely Mend FEC mechanism has major drawbacks including injecting excessive number of FEC packets into the network and consequently reducing recovery performance. This paper proposed an enhancement on Mend FEC mechanism, which is called Enhanced Adaptive FEC (EnAFEC) mechanism. The enhancement is aimed to improve recovery performance of the Mend FEC mechanism by generating FEC packets dynamically based on varying wireless network conditions. The proposed enhancement was implemented in simulation environment using the NS2 network simulation. The results show that EnAFEC mechanism outperformed the other Adaptive FEC mechanism in terms of recovery efficiency. This paper also highlights that even minimal amount of FEC packets can recover high packet losses and achieve good video quality at the receiver.
Automatic Information Extraction in Semi-Structured Offic ial Journals Blind Review

Information extraction systems are used to extract only relevant text information in digital repositories. The current work proposes an automatic system to extract information in semi-structured official journals. In our approach, given an input document, a Machine Learning (ML) algorithm classifies the document’s fragments into class labels which correspond to the data fields to be extracted. The implemented system deployed different features sets and algorithms used in the classification of the fragments. The system was evaluated through experiments on a sample containing 22770 lines of the Pernambuco’s Official Journal. The performed experiments revealed, in general, good results in terms of precision, which ranged from 70.14% to 98.63% depending on the feature set and algorithm used in the classification of the fragments.
Targeting Angiogenesis and Tumor Microenvironment in Metastatic Colorectal Cancer: Role of Aflibercept

In the last decades, we have progressively observed an improvement in therapeutic options for metastatic colorectal cancer (mCRC) treatment with a progressive prolongation of survival. mCRC prognosis still remains poor with low percentage of 5-year survival. Targeted agents have improved results obtained with standard chemotherapy. Angiogenesis plays a crucial role in colorectal cancer growth, proliferation, and metastasization and it has been investigated as a potential target for mCRC treatment. Accordingly, novel antiangiogenic targeted agents bevacizumab, regorafenib, and aflibercept have been approved for mCRC treatment as the result of several phase III randomized trials. The development of a tumor permissive microenvironment via the aberrant expression by tumor cells of paracrine factors alters the tumor-stroma interactions inducing an expansion of proangiogenic signals. Recently, the VELOUR study showed that addition of aflibercept to FOLFIRI regimen as a second-line therapy for mCRC improved significantly OS, PFS, and RR. This molecule represents a valid second-line therapeutic option and its peculiar ability to interfere with placental growth factor (PlGF)/vascular endothelial growth factor receptor 1 (VEGFR1) axis makes it effective in targeting angiogenesis, inflammatory cells and in overcoming resistances to anti-angiogenic first-line treatment. Here, we discuss about Aflibercept peculiar ability to interfere with tumor microenvironment and angiogenic pathway.
RDRVision-Learning vision recognition with Ripple Down Rules

Most approaches to robot vision require domain knowledge to be programmed. Such programs are expensive to create and usually fail to adapt to new or changing environment. In this paper, we investigate methods for learning domain knowledge in object recognition tasks. We use Ripple Down Rules, a rule-based representation that can be learned incrementally. The method is demonstrated in the vision tasks required in the RoboCup 4-legged robot soccer competition. Current limitations as well as future improvements are discussed.
A full bio-inspired bipedal gait locomotion system

Modeling the biped gait locomotion is an attractive subject in various fields of study. Thus far, several types of models are proposed in the literature to generate the stable and continuous walking cycles. These methods have a vast application in controlling the limbs of humanoid and bipedal robots. However, these approaches are not suitable for describing the human locomotion. This is for their dissimilarities to the human neuromuscular systems. Due to this deficiency, in this paper a new model is presented for bipedal gait that completely inspires by human neural controlling system. The major challenges of this model have been generation of rhythmic motion and coordination between the legs. The biological researches proved that these issues are accomplished in human central nervous system (CNS) by the central pattern generator (CPG) together with the neural reflexes. Thus, a two level half center CPG is used for producing the periodic movements in the leg. Further, for keeping the coordination, a Ia reflex reversal is employed that biologists confirm that this pathway regularly exist in the human CNS. The simulations showed that this plan well generates the stable gait patterns in addition to have coincidence to the human neuromuscular system.
Flocking of networked mechanical systems on directed topologies: a new perspective

In this paper, we investigate the flocking problem for multiple uncertain mechanical systems interacting on directed topologies. We propose an integral-sliding adaptive control to realise the objective of flocking, which gives rise to a cascade closed-loop system, and also propose a new notion – iBIBO (integral-bounded-input bounded-output) stability to characterise a new input–output property of a special class of dynamical systems, which is then used for the stability analysis of the closed-loop system. With the proposed iBIBO analysis tool and Lyapunov-like approach, we show that the proposed adaptive controller ensures the position and velocity consensus of the mechanical systems if and only if the damping is greater than certain lower bound for a given stiffness, and that the velocities of the mechanical systems converge to the weighted average of their initial values and the positions of the mechanical systems converge to the initial-states-dependent ramp trajectory. The performance of the proposed adaptive control scheme is shown by numerical simulation results.
Orthogonal Meta-Modeling

This article introduces meta-modeling hierarchies additional to the conventional meta-modeling hierarchy in a model-driven architecture. Additional hierarchies are introduced orthogonal to the conventional meta-modeling hierarchy for an appropriate correlation of information on combined hierarchies. In particular, orthogonal meta-modeling enables the grouping of models on the same conventional meta-modeling layer based on additional semantic dependencies. For the enhancement of conventional meta-modeling this paper discusses the creation of orthogonal meta-modeling hierarchies, the specification of semantic dependencies in meta-modeling hierarchies, semantic instances as well as the inheritance of semantic dependencies in meta-modeling hierarchies in general. Furthermore, the paper outlines the impact of orthogonal semantic meta-modeling on automated model transformation.
Calculating maps between n-categories

We give an explicit way of calculating the set of homotopy classes of morphisms from a Tamsamani n-category A to another one B. This calculation uses a Reedy-cofibrant cosimplicial resolution of A, using a new notion of ``free cofibration'' of n-precats. The free cofibrations of n-precats seem to be the analogue for n-categories of the Bousfield-Kan cofibrations in the theory of diagrams.
Oversearching and Layered Search in Empirical Learning

When learning classifiers, more extensive search for rules is shown to lead to lower predictive accuracy on many of the leal-world domains investigated. This counter-intuitive re suit is particularly relevant to recent system the search methods that use risk-free pruning to achieve the same outcome as exhaustive search. We propose an iterated search method that commences with greedy search extending its scope at each Iteration until a stopping criterion is satisfied. This layered search is often found to produce theories that are more accurate than those obtained with either greedy search or moderately, extensive beam search.
Structural Change in a Multi-Sector Model of Growth

We study a multi-sector model of growth with differences in TFP growth rates across sectors and derive sufficient conditions for the coexistence of structural change, characterized by sectoral labor reallocation, and balanced aggregate growth. The conditions are weak restrictions on the utility and production functions commonly applied by macroeconomists. Per capita output grows at the rate of labor-augmenting technological progress in the capital-producing sector and employment moves to low-growth sectors. In the limit all employment converges to two sectors, the slowest-growing consumption-goods sector and the capital-goods sector.
Esc2 promotes Mus81 complex-activity via its SUMO-like and DNA binding domains

Replication across damaged DNA templates is accompanied by transient formation of sister chromatid junctions (SCJs). Cells lacking Esc2, an adaptor protein containing no known enzymatic domains, are defective in the metabolism of these SCJs. However, how Esc2 is involved in the metabolism of SCJs remains elusive. Here we show interaction between Esc2 and a structure-specific endonuclease Mus81-Mms4 (the Mus81 complex), their involvement in the metabolism of SCJs, and the effects Esc2 has on the enzymatic activity of the Mus81 complex. We found that Esc2 specifically interacts with the Mus81 complex via its SUMO-like domains, stimulates enzymatic activity of the Mus81 complex in vitro, and is involved in the Mus81 complex-dependent resolution of SCJs in vivo. Collectively, our data point to the possibility that the involvement of Esc2 in the metabolism of SCJs is, in part, via modulation of the activity of the Mus81 complex.
Vowel Intelligibility in Chinese

Conventional wisdom states that, since the average amplitude of vowel articulation significantly exceeds that for consonants, an assessment of spoken intelligibility in obscuring noise should primarily be limited by consonant confusion. Furthermore, in both English and Chinese, consonant discrimination is considered to be more important to overall intelligibility than that of vowels. In the unbounded case, the assumption that vowel confusion is less important than consonant confusion may well be true; however, at least two situations exist where the influence of vowel confusion may be greater. The first is where vocabulary-specific restrictions confine the structure of a particular spoken word to alternatives differing primarily in their vowel. The second is the prevalence of non-additive white Gaussian noise (AWGN) interference, particularly impulsive noise which obscures only the vowel portion of a word, and similarly is present as a nonlinear effect of many time-sliced processing algorithms. This paper explores the issue of vowel intelligibility for spoken Chinese, where the confusion characteristics are complicated through the influence of lexical tone carried by the vowel in consonant-vowel-consonant (CVC) structure utterances. Experimental evidence from multilistener intelligibility testing are presented to build toward an understanding of the characteristics of Mandarin Chinese vowel confusion in the presence of AWGN. Results are also isolated by carrier word consonants and in terms of the lexical tone overlaid upon tested vowels. In particular, several factors relating to issues such as vowel length, tone combination and the crucial influence of the /a/ (IPA ) phone are revealed.