Miracle-dz/UniFAR-FTUs huggingface dataset:
A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model

ABSTRACT The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq) data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads, and the gene annotation that defines gene features, must also be taken into account. A partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies. Summary: We combined genome-guided gene prediction and whole transcriptome assembly from RNA sequencing data to improve the chicken genome annotation. This method may be also applicable to other imperfectly annotated genomes.
Anthropogenic Warming of Earth's Climate System

We compared the temporal variability of the heat content of the world ocean, of the global atmosphere, and of components of Earth's cryosphere during the latter half of the 20th century. Each component has increased its heat content (the atmosphere and the ocean) or exhibited melting (the cryosphere). The estimated increase of observed global ocean heat content (over the depth range from 0 to 3000 meters) between the 1950s and 1990s is at least one order of magnitude larger than the increase in heat content of any other component. Simulation results using an atmosphere-ocean general circulation model that includes estimates of the radiative effects of observed temporal variations in greenhouse gases, sulfate aerosols, solar irradiance, and volcanic aerosols over the past century agree with our observation-based estimate of the increase in ocean heat content. The results we present suggest that the observed increase in ocean heat content may largely be due to the increase of anthropogenic gases in Earth's atmosphere.
A Flexible Approach to Measuring Network Security Using Attack Graphs

The previous approaches to measuring network security are most based on the hypothesis that the related source data can be known well and truly. But in practice, it is very difficult to obtain all the related accurate source data [22]. In this paper, we propose a flexible approach based on attack graphs to measuring security of crucial resources in vulnerable network, which could bring out the accurate result of measuring network security with incomplete input data. Another key improvement is presenting the backward iterative algorithm to solve the problem of cyclic attack paths in measuring security using attack graphs. At the same time, the simulation experiment demonstrates the algorithm can be applied to the large attack graphs.
Equal-gain combining diversity reception of M-ary cpsk signals in nakagami fading

An expression for the average symbol error rate of M-ary coherent phase-shift-keyed (CPSK) signals for equal-gain combining (EGC) diversity reception in slow Nakagami fading channels is derived using the characteristic function method. The new expression is presented in the form of a one-fold integral. This expression adapts readily for numerical computation of error rates with no restriction on the number of channels or the value of the fading severity index. Numerical results for the error rates are presented for 2- and 4-channel diversity reception of binary, quaternary and octonary CPSK signals.
Intestinal Dysbiosis and Lowered Serum Lipopolysaccharide-Binding Protein in Parkinson’s Disease

Background The intestine is one of the first affected organs in Parkinson’s disease (PD). PD subjects show abnormal staining for Escherichia coli and α-synuclein in the colon. Methods We recruited 52 PD patients and 36 healthy cohabitants. We measured serum markers and quantified the numbers of 19 fecal bacterial groups/genera/species by quantitative RT-PCR of 16S or 23S rRNA. Although the six most predominant bacterial groups/genera/species covered on average 71.3% of total intestinal bacteria, our analysis was not comprehensive compared to metagenome analysis or 16S rRNA amplicon sequencing. Results In PD, the number of Lactobacillus was higher, while the sum of analyzed bacteria, Clostridium coccoides group, and Bacteroides fragilis group were lower than controls. Additionally, the sum of putative hydrogen-producing bacteria was lower in PD. A linear regression model to predict disease durations demonstrated that C. coccoides group and Lactobacillus gasseri subgroup had the largest negative and positive coefficients, respectively. As a linear regression model to predict stool frequencies showed that these bacteria were not associated with constipation, changes in these bacteria were unlikely to represent worsening of constipation in the course of progression of PD. In PD, the serum lipopolysaccharide (LPS)-binding protein levels were lower than controls, while the levels of serum diamine oxidase, a marker for intestinal mucosal integrity, remained unchanged in PD. Conclusions The permeability to LPS is likely to be increased without compromising the integrity of intestinal mucosa in PD. The increased intestinal permeability in PD may make the patients susceptible to intestinal dysbiosis. Conversely, intestinal dysbiosis may lead to the increased intestinal permeability. One or both of the two mechanisms may be operational in development and progression of PD.
Kinetic spanning trees for minimum-power routing in MANETs

A distributed kinetic spanning tree algorithm is proposed for routing in wireless mobile ad hoc networks. Assuming a piecewise linear motion model for the nodes, the sequence of shortest-path spanning trees is determined, valid until the time of the next node trajectory change. By computing the sequence of trees using one execution of the distributed routing algorithm, in contrast to computing the tree for a single time instant, the number of routing messages is substantially reduced. Moreover, the total power required to route through the trees as a function of time is also lower.
Application of quadratic linearization to control of Permanent Magnet synchronous motor

Control strategies developed for Permanent Magnet synchronous motor do not explicitly consider the dynamics of the system. Considering a dynamic model of the PM machine allows powerful control-theoretic techniques such as, linearization to be applied to the system. Existing feedback linearization techniques suffer from the limitations of singularity. In this paper, input and state transformations are derived for a 4 dimensional PM machine model for linearization of PMSM model. The proposed technique does not introduce singularities in the system. A Simulink model is developed for the IPM motor. Tuning transformations have been derived for the given machine by comparing the output of linearized machine with that of the normal form. The simulation including tuning transformation is also done and better results are obtained for linearization.
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials. Beyond the benefit of visualizing the high-dimensional, noisy spiking activity in a compact form, such trajectories can offer insight into the dynamics of the neural circuitry underlying the recorded activity. Current methods for extracting neural trajectories involve a two-stage process: the spike trains are first smoothed over time, then a static dimensionality-reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way and that account for spiking variability, which may vary both across neurons and across time. We then present a novel method for extracting neural trajectories-Gaussian-process factor analysis (GPFA)-which unifies the smoothing and dimensionality-reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that the proposed extensions improved the predictive ability of the two-stage methods. The predictive ability was further improved by going to GPFA. From the extracted trajectories, we directly observed a convergence in neural state during motor planning, an effect that was shown indirectly by previous studies. We then show how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject's behavior on a single-trial basis. Finally, to assess how well the proposed methods characterize neural population activity when the underlying time course is known, we performed simulations that revealed that GPFA performed tens of percent better than the best two-stage method.
A real world implementation of answer extraction

We describe ExtrAns, an answer extraction system. Answer extraction (AE) aims at retrieving those exact passages of a document that directly answer a given user question. AE is more ambitious than information retrieval and information extraction in that the retrieval results are phrases, not entire documents, and in that the queries may be arbitrarily specific. It is less ambitious than full-fledged question answering in that the answers are not generated from a knowledge base but looked up in the text of documents. The current version of ExtrAns is able to parse unedited Unix "man pages", and derive the logical form of their sentences. User queries are also translated into logical forms. A theorem prover then retrieves the relevant phrases, which are presented through selective highlighting in their context.
Path-Based Failure and Evolution Management

We present a new approach to managing failures and evolution in large, complex distributed systems using runtime paths. We use the paths that requests follow as they move through the system as our core abstraction, and our "macro" approach focuses on component interactions rather than the details of the components themselves. Paths record component performance and interactions, are user- and request-centric, and occur in sufficient volume to enable statistical analysis, all in a way that is easily reusable across applications. Automated statistical analysis of multiple paths allows for the detection and diagnosis of complex failures and the assessment of evolution issues. In particular, our approach enables significantly stronger capabilities in failure detection, failure diagnosis, impact analysis, and understanding system evolution. We explore these capabilities with three real implementations, two of which service millions of requests per day. Our contributions include the approach; the maintainable, extensible, and reusable architecture; the various statistical analysis engines; and the discussion of our experience with a high-volume production service over several years.
GSMDPs for Multi-Robot Sequential Decision-Making

Markov Decision Processes (MDPs) provide an extensive theoretical background for problems of decision-making under uncertainty. In order to maintain computational tractability, however, real-world problems are typically discretized in states and actions as well as in time. Assuming synchronous state transitions and actions at fixed rates may result in models which are not strictly Markovian, or where agents are forced to idle between actions, losing their ability to react to sudden changes in the environment. In this work, we explore the application of Generalized Semi-Markov Decision Processes (GSMDPs) to a realistic multi-robot scenario. A case study will be presented in the domain of cooperative robotics, where real-time reactivity must be preserved, and synchronous discrete-time approaches are therefore sub-optimal. This case study is tested on a team of real robots, and also in realistic simulation. By allowing asynchronous events to be modeled over continuous time, the GSMDP approach is shown to provide greater solution quality than its discrete-time counterparts, while still being approximately solvable by existing methods.
Unsupervised Satellite Image Segmentation by Combining SA Based Fuzzy Clustering with Support Vector Machine

Fuzzy clustering is an important tool for unsupervised pixel classification in remotely sensed satellite images. In this article, a Simulated Annealing (SA) based fuzzy clustering method is developed and combined with popular Support vector Machine (SVM) classifier to fine tune the clustering produced by SA for obtaining an improved clustering performance. The performance of the proposed technique has been compared with that of some other well-known algorithms for an IRS satellite image of the city of Kolkata and its superiority has been demonstrated quantitatively and visually.
DETERMINATION OF ATP IN BIOLOGICAL MATERIAL BY A BIOLUMINESCENCE METHOD USING A SCINTILLATION COUNTER

A simple, rapid and inexpensive method for the ATP determination in biological material has been described. The method is based on the measurement o f flashes emitted by the firefly Iuciferin-luciferase system in a liquid scintillation counter. The method permits the routine analyses o f fresh as well frozen samples. High sensitivity (10~12 M ATP) makes suitable for the ATP analyses in small samples.
Advances in audio source separation and multisource audio content retrieval

Audio source separation aims to extract the signals of individual sound sources from a given recording. In this paper, we review three recent advances which improve the robustness of source separation in real-world challenging scenarios and enable its use for multisource content retrieval tasks, such as automatic speech recognition (ASR) or acoustic event detection (AED) in noisy environments. We present a Flexible Audio Source Separation Toolkit (FASST) and discuss its advantages compared to earlier approaches such as independent component analysis (ICA) and sparse component analysis (SCA). We explain how cues as diverse as harmonicity, spectral envelope, temporal ﬁne structure or spatial location can be jointly exploited by this toolkit. We subsequently present the uncertainty decoding (UD) framework for the integration of audio source separation and audio content retrieval. We show how the uncertainty about the separated source signals can be accurately estimated and propagated to the features. Finally, we explain how this uncertainty can be eﬃciently exploited by a classiﬁer, both at the training and the decoding stage. We illustrate the resulting performance improvements in terms of speech separation quality and speaker recognition accuracy.
B^2RTDP: An Efficient Solution for Bounded-Parameter Markov Decision Process

Bounded-parameter Markov decision process (BMDP) can be used to model sequential decision problems, where the transitions probabilities are not completely know and are given by intervals. One of the criteria used to solve that kind of problems is the maxim in, i.e., the best action on the worst scenario. The algorithms to solve BMDPs that use this approach include interval value iteration and an extension of real time dynamic programming (Robust-LRTDP). In this paper, we introduce a new algorithm, named B2RTDP, also based on real time dynamic programming that makes a different choice of the next state to be visited using upper and lower bounds of the optimal value function. The empirical evaluation of the algorithm shows that it converges faster than the state-of-the-art algorithms that solve BMDPs.
A two-layer decentralized approach to the optimal energy management of a building district with a shared thermal storage

We propose a decentralized scheme for the energy management of a district composed of multiple buildings. Each building is equipped with its own chiller, whereas a single thermal storage is shared among all buildings. The buildings aim at jointly minimizing the electric energy costs while guaranteeing comfort conditions for their occupants, and they can act on their own temperature set-point and the usage of the common storage to this purpose. The problem can be formulated as a constrained optimization program where the buildings decision variables are coupled via some global constraints, due to the shared storage, and the cost function, since the electric energy price depends on the district demand. To distribute the computational load and reduce the amount of transmitted information, we propose a two-layer solution where a central entity takes care of the global constraints by updating some dual variables (outer layer), whereas the primal variables, i.e., zone temperature set-point and storage usage, are optimized locally by the buildings through nested iterations where the price is recomputed based on the updated information on the district demand provided by the central entity (inner layer).
Stable load control with load prediction in multipath packet forwarding

With the invention of multiprotocol label switching (MPLS), complex traffic engineering and multipath packet forwarding has become a reality. To use multipath packet forwarding, efficient load control between paths is necessary. Furthermore, dynamic load control is needed to optimize load mapping between paths depending on the network state. Unfortunately, distributed dynamic load control algorithms, if not carefully designed, can make the load mapping unstable, thus making many packets dropped due to unbalanced mapping. We propose a dynamic load control scheme which makes the network efficient while keeping load mapping stable.
Neurobiology of Disease Castration Restores Function and Neurofilament Alterations of Aged Symptomatic Males in a Transgenic Mouse Model of Spinal and Bulbar Muscular Atrophy

Transgenic models of neurodegenerative disease have proved uniquely powerful for delineating pathways of neuronal dysfunction and cell death. We have developed a transgenic model of the polyglutamine disease spinal and bulbar muscular atrophy (SBMA), an adultonset, slowly progressive motor neuron disease caused by polyglutamine expansion in the androgen receptor (AR). Mice bearing a human AR with 112 glutamines reproduce many aspects of SBMA, including slowly progressive, gender-specific motor deficits, and neuronal intranuclear inclusions. Despite substantial motor deficits in male AR112Q mice, no motor neuron loss was observed, indicating that neuronal dysfunction, rather than neuronal death, is central to disease. Moreover, reduced levels of unphosphorylated neurofilament heavy chain (NF-H) were observed in motor neurons, suggesting a role for NF-H in SBMA neuronal dysfunction. The elimination of androgens by surgical castration of severely affected, aged 112Q male mice partially restored motor function as well as NF-H levels. These data suggest that hormone-based therapies designed to treat SBMA patients, even with advanced disease, are likely to be effective.
Non-Gaussian signatures in the temperature fluctuation observed by the Wilkinson Microwave Anisotropy Probe

We present results from a test for the Gaussianity of the whole sky subdegree-scale cosmic microwave background (CMB) temperature anisotropy measured by the Wilkinson Microwave Anisotropy Probe (WMAP). We calculate the genus from the foreground-subtracted and Kp0-masked WMAP maps and measure the genus shift parameters defined at negative and positive threshold levels (Δv - and Δv + ) and the asymmetry parameter (Ag) to quantify the deviation from the Gaussian relation. At WMAP Q, V and W bands, the genus and genus-related statistics imply that the observed CMB sky is consistent with a Gaussian random phase field. However, from the genus measurement on the Galactic northern and southern hemispheres, we have found two non-Gaussian signatures at the W-band resolution (0.35 scale), i.e. the large difference of genus amplitudes between the north and the south and the positive genus asymmetry in the south, which are statistically significant at 2. 6σ and 2. 4σ levels, respectively. The large genus amplitude difference also appears in the WMAP Q- and V-band maps, deviating from the Gaussian prediction with a significance level of about 2σ. The probability that the genus curves show such a large genus amplitude difference exceeding the observed values at all Q, V and W bands in a Gaussian sky is only 1.4 per cent. Such non-Gaussian features are reduced as the higher Galactic cut is applied, but their dependence on the Galactic cut is weak. We discuss possible sources that can induce non-Gaussian features, such as the Galactic foregrounds, the integrated Sachs-Wolfe and the Sunyaev-Zel'dovich effects, and the reionization-induced low l-mode non-Gaussianity that are aligned along the Galactic plane. We conclude that CMB data with higher signal-to-noise ratio and an accurate foreground model are needed to understand the non-Gaussian signatures.
Parallel Real-Time Optimization: Beyond Speedup

Traditionally, interest in parallel computation centered around the speedup provided by parallel algorithms over their sequential counterparts. In this paper, we ask a different type of question: Can parallel computers, due to their speed, do more than simply speed up the solution to a problem? We show that for real-time optimization problems, a parallel computer can obtain a solution that is better than that obtained by a sequential one. Specifically, a sequential and a parallel algorithm are exhibited for the problem of computing the best-possible approximation to the minimum-weight spanning tree of a connected, undirected and weighted graph whose vertices and edges are not all available at the outset, but instead arrive in real time. While the parallel algorithm succeeds in computing the exact minimum-weight spanning tree, the sequential algorithm can only manage to obtain an approximate solution. In the worst case, the ratio of the weight of the solution obtained sequentially to that of the solution computed in parallel can be arbitrarily large.
A Novel Algorithm for Load Balancing in Distributed Systems

This paper proposes a novel algorithm for load balancing in the parallel and distributed systems. Firstly, almost all jobs are organized into the standard job combinations, each of which consists of one to four jobs. Then they are assigned to the machines according to the assignment algorithm for job combinations, which is a special integer partition algorithm. The maximum error between the optimal algorithm and our algorithm is estimated. The experimental results show that our algorithm performs efficiently.