Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic dynamics of genetic networks: modelling and parameter identification

MOTIVATION Identification of regulatory networks is typically based on deterministic models of gene expression. Increasing experimental evidence suggests that the gene regulation process is intrinsically random. To ensure accurate and thorough processing of the experimental data, stochasticity must be explicitly accounted for both at the modelling stage and in the design of the identification algorithms. RESULTS We propose a model of gene expression in prokaryotes where transcription is described as a probabilistic event, whereas protein synthesis and degradation are captured by first-order deterministic kinetics. Based on this model and assuming that the network of interactions is known, a method for estimating unknown parameters, such as synthesis and binding rates, from the outcomes of multiple time-course experiments is introduced. The method accounts naturally for sparse, irregularly sampled and noisy data and is applicable to gene networks of arbitrary size. The performance of the method is evaluated on a model of nutrient stress response in Escherichia coli.
Clustering Fetal Heart Rate Tracings by Compression

Fetal heart rate (FHR) monitoring is widely used regarding the detection of fetuses in danger of death or damage. Thirty one FHR tracings acquired in the antepartum period were clustered by compression in order to identify abnormal ones. A recently introduced approach based on algorithmic information theory was used. The new method can mine patterns in completely different areas, without domain-specific parameters to set, and does not require specific background knowledge. At the highest level the FHR tracings were clustered according to an unanticipated feature, namely the technology used in signal acquisition. At the lower levels all tracings with abnormal or suspicious patterns were clustered together, independently of the technology used
Layered LDPC convolutional codes for compression of correlated sources under adversarial attacks

We consider the problem of code design for compression of correlated sources under adversarial attacks. A scenario with three correlated sources is considered in which at most one source is compromised by an adversary. The theoretical minimum achievable sum-rate for this scenario was derived by Kosut and Tong. We design layered LDPC convolutional codes for this problem, assuming that one of the sources is available at the common decoder as side information. We demonstrate that layered LDPC convolutional codes constitute a sequence of nested codes where each sub-code is capacity-achieving for the binary symmetric channels used to model the correlation between sources, and therefore, can ideally achieve the theoretical minimum sum-rate. Simulated performance results for moderate block length show a small gap to the theoretical limit, and as the block length increases the gap vanishes.
Fatigue in Emergency Services Operations: Assessment of the Optimal Objective and Subjective Measures Using a Simulated Wildfire Deployment

Under controlled laboratory conditions, neurobehavioral assays such as the Psychomotor Vigilance Task (PVT) are sensitive to increasing levels of fatigue, and in general, tend to correlate with subjective ratings. However, laboratory studies specifically curtail physical activity, potentially limiting the applicability of such findings to field settings that involve physical work. In addition, laboratory studies typically involve healthy young male participants that are not always representative of a typical working population. In order to determine whether these findings extend to field-like conditions, we put 88 Australian volunteer firefighters through a multi-day firefighting simulation. Participants were required to perform real-world physical and cognitive tasks under conditions of elevated temperature and moderate sleep restriction. We aimed to examine changes in fatigue in an effort to determine the optimum objective and subjective measures. Objective and subjective tests were sensitive to fatigue outside laboratory conditions. The PVT was the most sensitive assay of objective fatigue, with the Samn-Perelli fatigue scale the most sensitive of the subjective measures. The Samn-Perilli fatigue scale correlated best with PVT performance, but explained a small amount of variance. Although the Samn-Perelli scale can be easily administered in the field, the wide range of individual variance limits its efficacy as a once-off assessment tool. Rather, fatigue measures should be applied as a component of a broader fatigue risk management system. Findings provide firefighting agencies, and other occupations involving physical work, guidance as to the most sensitive and specific measures for assessing fatigue in their personnel.
An Effective Supervised Streamed Text Classification Approach for Mining Positive and Negative Examples

data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the field of text mining. This survey paper is based on effective classification of streamed data for text mining by PNLH & one-class classification SVM for text contained audit, we consider the problem of one-class classification of text streams with respect to concept drift where a large volume of documents arrives at a high speed and with change of user interests and data distribution. In this case, only a small number of positively labelled documents is available for training. And text classification without negative examples revisit, by this we propose a labelling heuristic called PNLH to tackle this problem. PNLH aims at extracting high quality positive examples and negative examples from U and our survey can be used on top of any existing classifiers.
Dentigerous Cyst or Adenomatoid Odontogenic Tumor: Clinical Radiological and Histopathological Dilemma

Adenomatoid odontogenic tumor (AOT) is a well-recognised slow growing benign tumor derived from complex system of dental lamina or its remnants. This lesion is categorised into three variants of which the more common variant is follicular type which is often mistaken for dentigerous cyst. We present a case of AOT in a 14-year-old male who was misdiagnosed as dentigerous cyst. Clinical radiological and therapeutic characteristics of the case are commented on in detail.
Autoregressive Hidden Semi-Markov Model of Symbolic Music Performance for Score Following

A stochastic model of symbolic (MIDI) performance of polyphonic scores is presented and applied to score following. Stochastic modelling has been one of the most successful strategies in this field. We describe the performance as a hierarchical process of performer's progression in the score and the production of performed notes, and represent the process as an extension of the hidden semi-Markov model. The model is compared with a previously studied model based on hidden Markov model (HMM), and reasons are given that the present model is advantageous for score following especially for scores with trills, tremolos, and arpeggios. This is also confirmed empirically by comparing the accuracy of score following and analysing the errors. We also provide a hybrid of this model and the HMM-based model which is computationally more efficient and retains the advantages of the former model. The present model yields one of the state-of-the-art score following algorithms for symbolic performance and can possibly be applicable for other music recognition problems.
Molecular characterization of the VH region of murine autoantibodies from neonatal and adult BALB/c mice

We report on the molecular characterization of the heavy (H) chain variable (V) region of two murine autoantibodies reacting with a conventional self antigen, thyro‐globulin (Tg), originated from unimmunized/unstimulated neonatal mice (clone B10H2) and from an adult hyperimmunized mouse (clone 62), respectively. Serologically, both hybridoma antibodies express the same idiotype (Id). By cloning and sequencing we demonstrated that their VH regions are encoded by identical VDJ gene elements including the VD and DJ joints. This VH belongs to the VH 7183 gene family, the most 3′‐end proximal family in the murine genome. The D segment is unique and only 50% similar to any murine D segment. The J segment utilized is germ‐line JH4. The cloned DNA rearrangement was transfected into the J558L myeloma cells and the secreted antibody was found to express the reference Id on the H chain, hence proving that the productive VDJ rearrangement had been identified. These results show that a spontaneously arising and an antigen‐induced autoantibody use an identical VDJ gene rearrangement and that after hyperimmunization somatic mutation did not occur. The significance of this finding with respect to ontogeny of the B cell repertoire is discussed.
On the Energy and Spectral Efficiency Tradeoff in Massive MIMO-Enabled HetNets With Capacity-Constrained Backhaul Links

In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive multiple-input-multiple-output-enabled heterogenous networks while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously. With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared with other algorithms.
Inactivation of PNKP by Mutant ATXN3 Triggers Apoptosis by Activating the DNA Damage-Response Pathway in SCA3

Spinocerebellar ataxia type 3 (SCA3), also known as Machado-Joseph disease (MJD), is an untreatable autosomal dominant neurodegenerative disease, and the most common such inherited ataxia worldwide. The mutation in SCA3 is the expansion of a polymorphic CAG tri-nucleotide repeat sequence in the C-terminal coding region of the ATXN3 gene at chromosomal locus 14q32.1. The mutant ATXN3 protein encoding expanded glutamine (polyQ) sequences interacts with multiple proteins in vivo, and is deposited as aggregates in the SCA3 brain. A large body of literature suggests that the loss of function of the native ATNX3-interacting proteins that are deposited in the polyQ aggregates contributes to cellular toxicity, systemic neurodegeneration and the pathogenic mechanism in SCA3. Nonetheless, a significant understanding of the disease etiology of SCA3, the molecular mechanism by which the polyQ expansions in the mutant ATXN3 induce neurodegeneration in SCA3 has remained elusive. In the present study, we show that the essential DNA strand break repair enzyme PNKP (polynucleotide kinase 3’-phosphatase) interacts with, and is inactivated by, the mutant ATXN3, resulting in inefficient DNA repair, persistent accumulation of DNA damage/strand breaks, and subsequent chronic activation of the DNA damage-response ataxia telangiectasia-mutated (ATM) signaling pathway in SCA3. We report that persistent accumulation of DNA damage/strand breaks and chronic activation of the serine/threonine kinase ATM and the downstream p53 and protein kinase C-δ pro-apoptotic pathways trigger neuronal dysfunction and eventually neuronal death in SCA3. Either PNKP overexpression or pharmacological inhibition of ATM dramatically blocked mutant ATXN3-mediated cell death. Discovery of the mechanism by which mutant ATXN3 induces DNA damage and amplifies the pro-death signaling pathways provides a molecular basis for neurodegeneration due to PNKP inactivation in SCA3, and for the first time offers a possible approach to treatment.
An overview of a large-scale data migration

We present an overview of the migration of the data collected by the COMPASS experiment at CERN. In less than three months, almost 300 TB will be migrated at a sustained data rate approaching 100 MB/s, using a distributed system with multiple nodes and data servers. This project, which is being carried out by the Database Group of CERN's IT Division and is expected to be completed by early 2003, involves both a physical media migration, from StorageTek 9940A to StorageTek 9940B tapes, and a data format conversion, from an object database management system to a hybrid persistency mechanism based on flat files referenced in a relational database management system.
Nonconceptual Content, Richness, and Fineness of Grain

NONCONCEPTUAL CONTENT, RICHNESS, AND FINENESS OF GRAIN As I view the scene before my eyes, there is a way the world looks to me. If the world is that way, my visual experience is accurate; if not, my experience is inaccurate. My visual experience, then, has correctness conditions: it is correct or accurate in certain circumstances; incorrect or inaccurate in others. Visual experiences, like beliefs, are representations of how things are. Accordingly, visual experiences have representational content. It is often said that the representational content of visual experience is rich. Another common claim is that there is a fineness of grain to visual experience. A third view, also quite widely accepted, is that the representational content of visual experience is nonconceptual. Notwithstanding the popularity of these views, it is still not fully clear what is being claimed nor how exactly the three views are connected. The purpose of this essay is threefold. First, I want to present a clarification and partial defense of the thesis that visual experiences have nonconceptual contents. Second, I want to explain and defend the view that visual experience is representationally rich. This necessitates a discussion of several psychological experiments, the results of which bear upon the view. Finally, I want to discuss how the thesis of richness relates to the thesis of fineness of grain (held most often in connection with our experiences of shades of color); and I want to spell out carefully how both theses relate to the thesis of nonconconceptual content. The paper is divided into six sections. In Section I, I address some preliminary terminological matters and I take up the question of what it is for an
XTEN as Biological Alternative to PEGylation Allows Complete Expression of a Protease-Activatable Killin-Based Cytostatic

Increased effectiveness and reduced side effects are general goals in drug research, especially important in cancer therapy. The aim of this study was to design a long-circulating, activatable cytostatic drug that is completely producible in E. coli. Crucial for this goal was the novel unstructured polypeptide XTEN, which acts like polyethylene glycol (PEG) but has many important advantages. Most importantly, it can be produced in E. coli, is less immunogenic, and is biodegradable. We tested constructs containing a fragment of Killin as cytostatic/cytotoxic element, a cell-penetrating peptide, an MMP-2 cleavage site for specific activation, and XTEN for long blood circulation and deactivation of Killin. One of three sequence variants was efficiently expressed in E. coli. As typical for XTEN, it allowed efficient purification of the E. coli lysate by a heat step (10 min 75°C) and subsequent anion exchange chromatography using XTEN as purification tag. After 24 h XTEN-Killin reduced the number of viable cells of HT-1080 tumor cell line to 3.8 ±2.0% (p<0.001) compared to untreated controls. In contrast, liver derived non-tumor cells (BRL3A) did not show significant changes in viability. Our results demonstrate the feasibility of completely producing a complex protease-activatable, potentially long-circulating cytostatic/cytotoxic prodrug in E. coli—a concept that could lead to efficient production of highly multifunctional drugs in the future.
Solution Equilibrium Titration for High-Throughput Affinity Estimation of Unpurified Antibodies and Antibody Fragments

The generation of therapeutic antibodies with extremely high affinities down to the low picomolar range is today feasible with state-of-the art recombinant technologies. However, reliable and efficient identification of lead candidates with the desired affinity from a pool of thousands of antibody clones remains a challenge. Here, we describe a high-throughput procedure that allows reliable affinity screening of unpurified immunoglobulin G or antibody fragments. The method is based on the principle of solution equilibrium titration (SET) using highly sensitive electrochemiluminescence as a readout system. Because the binding partners are not labeled, the resulting KD represents a sound approximation of the real affinity. For screening, diluted bacterial lysates or cell culture supernatants are equilibrated with four different concentrations of a soluble target molecule, and unbound antibodies are subsequently quantified on 384-well Meso Scale Discovery (MSD) plates coated with the respective antigen. For determination of KD values from the resulting titration curves, fit models deduced from the law of mass action for 1:1 and 2:1 binding modes are applied to assess hundreds of interactions simultaneously. The accuracy of the method is demonstrated by comparing results from different screening campaigns from affinity optimization projects with results from detailed affinity characterization.
Prediction-based load shedding for burst data streams

Overload management has become very important in telecommunication networks, especially in the case of monitoring network elements that generate time-varying and burst data streams. Efficient overload management improves the quality of provided services, reduces revenue leakage, and minimizes network failure detection time. We argue that in order to improve the quality of bursty data stream processing, data stream management systems (DSMS) should be introduced into network management infrastructures and should monitor and optimize the execution of data stream queries through load-shedding techniques. We introduce a new mechanism for data prioritization and implement a new load-shedding framework, partially based on the popular TelegraphCQ DSMS. Our approach significantly improves the accuracy of data stream query results by using a prediction module to rank the values using preferential ranks. The prediction module tracks and registers all symptoms of overload, and either prevents overload before it occurs, or minimizes its effects. We present empirical evidence showing that our solution significantly improves data stream query accuracy. The effectiveness of the framework has been verified using both artificial and real data streams collected from network elements.
Overdispersed variational autoencoders

The ability to fit complex generative probabilistic models to data is a key challenge in AI. Currently, variational methods are popular, but remain difficult to train due to high variance of the sampling methods employed. We introduce the overdispersed variational autoencoder and overdispersed importance weighted autoencoder, which combine overdispersed black box variational inference with the variational autoencoder and importance weighted autoencoder respectively. We use the log likelihood lower bounds and reparametrisation trick from the variational and importance weighted autoencoders, but rather than drawing samples from the variational distribution itself, we use importance sampling to draw samples from an overdispersed (i.e. heavier-tailed) proposal in the same family as the variational distribution. We run experiments on two different datasets, and show that this technique produces a lower variance estimate of the gradients, and reaches a higher bound on the log likelihood of the observed data.
Fault-Tolerant Mobile Agent Execution

Mobile agents have attracted considerable interest in recent years. In the context of mobile agents, fault tolerance is crucial to enable the integration of mobile agent technology into today's business applications. This article identifies two important properties for fault-tolerant mobile agent execution: nonblocking and exactly-once. Nonblocking ensures that the agent execution can proceed despite a single failure of the agent or the machine, for instance. Replication is the generally adopted mechanism to prevent blocking, but may lead to multiple executions of the agent (i.e., a violation of the exactly-once property), which is undesirable with operations that have side effects. Hence, we propose that fault-tolerant mobile agent execution be modeled as a sequence of agreement problems. Our approach is nonblocking and ensures exactly-once execution. FATOMAS, our prototype fault-tolerant mobile agent system, implements our approach. Its performance evaluation illustrates the overhead of the replication mechanisms.
Data driven methods for utterance semantic tagging

The proliferation of mobile devices, along with advances in speech and natural language processing technologies, have given birth to a new wave of personal assistance applications that enable users to quickly and more naturally perform many tasks through voice on their smart devices. This paper focuses on a natural language understanding (NLU) solution for one such application. We adopted a data-driven approach, aiming to take advantage of large volume of deployment data for continued learning and system improvement. In this paper, we compare two different statistical models---a hidden Markov model and a maximum entropy Markov model---for the task of semantic slot extraction, and we present empirical results on real user data.
The Application of PMML in Healthy Housing Evaluation and Rules Discovery Decision Support System

Upon introduction of the efficiency issue of decision support system in the data mining (abbr. DM) field, a settlement scheme has been raised up, i.e. to adopt Predictive Model Markup Language (abbr. PMML) for storing DM information, conducting cross- platform exchanging DM models. It explained at length how to apply PMML to standardize the definition of healthy housing evaluation and the DM model in rules discovery support system. In the meantime, the DM procedures have been realized.