Miracle-dz/UniFAR-FTUs huggingface dataset:
In vitro and in vivo testing of a wireless multichannel stimulating telemetry microsystem

An inductively powered 64-site current microstimulating system, Interestim-2B, with a modular architecture and minimal number of off-chip components has been developed for neural prosthesis applications. Interestim-2B can generate any arbitrary current waveform and supports a variety of monopolar and bipolar stimulation protocols. A common analog line provides access to each site potential, and exhausts residual stimulus charges. In situ site impedance measurement capability helps indicate the defective sites in chronic stimulations. This paper also summarizes some of the in vitro and in vivo experimental results using a 16-site implant.
Notch filters for port-Hamiltonian systems

In this paper a standard notch filter is modeled in the port-Hamiltonian framework. By having such a port-Hamiltonian description it is proven that the notch filter is a passive system. The notch filter can then be interconnected with another (nonlinear) port-Hamiltonian system, while preserving the overall passivity property. By doing so we can combine a frequency-based control method, the notch filter, with the nonlinear control methodology of passivity-based control.
Optimal rate for irregular LDPC codes in binary erasure channel

In this paper, we introduce a new practical and general method for solving the main problem of designing the capacity approaching, optimal rate, irregular low-density parity-check (LDPC) code ensemble over binary erasure channel (BEC). Compared to some new researches, which are based on application of asymptotic analysis tools out of optimization process, the proposed method is much simpler, faster, accurate and practical. Because of not using any relaxation or any approximate solution like previous works, the found answer with this method is optimal. We can construct optimal variable node degree distribution for any given binary erasure rate, e, and any check node degree distribution. The presented method is implemented and it works well in practice. The time complexity of this method is of polynomial order. As a result, we obtain some degree distribution which their rates are close to the capacity.
Application of Code Average Method for GSM coverage prediction in different environment

In order to estimate the signal strength accurately for mobile or wireless systems, it is important to estimate characteristics of system's propagation through a medium. Propagation analysis provides a good initial estimate of the signal characteristics. Since site measurements are costly, path loss prediction models have been developed as a low cost technique to predict at certain area. In this paper, well-known propagation path loss model Okumura-Hata and free space path loss model were used as basis to analyze the coverage of GSM system in different propagation environment (free space, rural, suburban, urban and dense urban). The Code Average Method (CAM) was used to optimize the model to predict the path loss of propagation according to type of environment. Using CAM approach with grid system, it produced an effect in the path loss coverage form. Finally the simulation and analysis results show the effect of CAM in the path loss coverage prediction for different type of environment.
MCL-1 Is a Key Determinant of Breast Cancer Cell Survival: Validation of MCL-1 Dependency Utilizing a Highly Selective Small Molecule Inhibitor

Hyperexpression of antiapoptotic BCL-2 family proteins allows cells to survive despite the receipt of signals that would ordinarily induce their deletion, a facet frequently exploited by tumors. Tumors addicted to the BCL-2 family proteins for survival are now being targeted therapeutically. For example, navitoclax, a BCL-2/BCL-XL/BCL-W inhibitor, is currently in phase I/II clinical trials in numerous malignancies. However, the related family member, MCL-1, limits the efficacy of navitoclax and other chemotherapeutic agents. In the present study, we identify breast cancer cell lines that depend upon MCL-1 for survival and subsequently determine the mechanism of apoptosis mediated by the MCL-1 selective inhibitor A-1210477. We demonstrate that apoptosis resulting from a loss in MCL-1 function requires expression of the proapoptotic protein BAK. However, expression of BCL-XL can limit apoptosis resulting from loss in MCL-1 function through sequestration of free BIM. Finally, we demonstrate substantial synergy between navitoclax and MCL-1 siRNA, the direct MCL-1 inhibitor A-1210477, or the indirect MCL-1 inhibitor flavopiridol, highlighting the therapeutic potential for inhibiting BCL-XL and MCL-1 in breast cancer. Mol Cancer Ther; 14(8); 1837–47. ©2015 AACR.
Differential Regulation of NF-κB by Elongation Factors Is Determined by Core Promoter Type

ABSTRACT NF-κB transcription factors activate genes important for immune response, inflammation, and cell survival. P-TEFb and DSIF, which are positive and negative transcription elongation factors, respectively, both regulate NF-κB-induced transcription, but the mechanism underlying their recruitment to NF-κB target genes is unknown. We show here that upon induction of NF-κB, a subset of target genes is regulated differentially by either P-TEFb or DSIF. The regulation of these genes and their occupancy by these elongation factors are dependent on the NF-κB enhancer and the core promoter type. Converting a TATA-less promoter to a TATA promoter switches the regulation of NF-κB from DSIF to P-TEFb. Accumulation or displacement of DSIF and P-TEFb is dictated by the formation of distinct initiation complexes (TFIID dependent or independent) on the two types of core promoter. The underlying mechanism for the dissociation of DSIF from TATA promoters upon NF-κB activation involves the phosphorylation of RNA polymerase II by P-TEFb. The results highlight a regulatory link between the initiation and the elongation phases of the transcription reaction and broaden our comprehension of the NF-κB pathway.
Translating Electronic Health Record Notes from English to Spanish: A Preliminary Study

The Centers for Medicare & Medicaid Services Incentive Programs promote meaningful use of electronic health records (EHRs), which, among many benefits, allow patients to receive electronic copies of their EHRs and thereby empower them to take a more active role in their health. In the United States, however, 17% population is Hispanic, of which 50% has limited English language skills. To help this population take advantage of their EHRs, we are developing English-Spanish machine translation (MT) systems for EHRs. In this study, we first built an English-Spanish parallel corpus and trained NoteAidSpanish, a statistical MT (SMT) system. Google Translator and Microsoft Bing Translator are two baseline MT systems. In addition, we evaluated hybrid MT systems that first replace medical jargon in EHR notes with lay terms and then translate the notes with SMT systems. Evaluation on a small set of EHR notes, our results show that Google Translator outperformed NoteAidSpanish. The hybrid SMT systems first map medical jargon to lay language. This step improved the translation. A fully implemented hybrid MT system is available at http://www.clinicalnotesaid. org. The English-Spanish parallel-aligned MedlinePlus corpus is available upon request.
A satellite based system for managing crisis scenarios: The E-SPONDER perspective

This paper presents the work that is currently performed in the E-SPONDER (FP7–242411) project. E-SPONDER is a project co-funded by the European Commission under its Security Program. E-SPONDER is a suite of real-time data-centric technologies that will provide information and communication support to first responders which act during normal and abnormal events (crises). It will investigate the use of interoperable communications that will facilitate the uninterruptible link between the first responders at the operations theatre and the crisis managers at the Emergency Operations Center. To achieve that, a novel architecture is introduced that brings satellite communications in the spotlight of effective first response work. The paper demonstrates the way satellite communications will be integrated to the E-SPONDER system in order to ensure always-on communications and data exchange.
Reappraisal of the Therapeutic Role of Celecoxib in Cholangiocarcinoma

Cholangiocarcinoma (CCA), a lethal disease, affects many thousands worldwide yearly. Surgical resection provides the best chance for a cure; however, only one-third of CCA patients present with a resectable tumour at the time of diagnosis. Currently, no effective chemotherapy is available for advanced CCA. Cyclooxygenase-2 (COX-2) is a potential oncogene expressing in human CCA tissues and represents a candidate target for treatment; however, COX-2 inhibitors increase the risk of negative cardiovascular events as application for chemoprevention aim. Here, we re-evaluated the effectiveness and safety of celecoxib, one widely used COX-2 inhibitor, in treating CCA. We demonstrated that celecoxib exhibited an anti-proliferative effect on CGCCA cells via cell cycle arrest at G2 phase and apoptosis induction. Treatment for 5 weeks high dose celecoxib (160 mg/kg) significantly repressed thioacetamide-induced CCA tumour growth in rats as monitored by animal positron emission tomography through apoptosis induction. No obviously observable side effects were noted during the therapeutic period. As retrospectively reviewing 78 intrahepatic mass-forming CCA patients, their survival was strongly and negatively associated with a positive resection margin and high COX-2 expression. Based on our result, we concluded that short-term high dose celecoxib may be a promising therapeutic regimen for CCA. Yet its clinical application still needs more studies to prove its safety.
Organizational learning and organizational communication starting to merger breakdown

Why should a project about an organizational merger be interesting? What do the Petit Price and the fox teach us about the mergers? Antoine de Saint-Exupery tells us a story about to become friends. Knowing the Petit Prince the fox breaks its life style; it learns a new verbal and emotional language and consequently re organizes tis life In this paper, I argue that reading a merger event as a breakdown inside the organization may increase our knowledge about two important areas of sociological studies: organizational learning and organizational communication. I will show with my case study that organizational communication and organizational learning are intertwined. The case around which the argument is made is the development of organizational project realized by the organization, born by horizontal merger, in order to overcome post – merger crisis.
Junonia coenia Densovirus-Based Vectors for Stable Transgene Expression in Sf9 Cells: Influence of the Densovirus Sequences on Genomic Integration

ABSTRACT The invertebrate parvovirus Junonia coenia densovirus (JcDNV) shares similarities with terminal hairpins and nonstructural (NS) protein activities of adeno-associated virus (AAV) despite their evolutionary divergence (B. Dumas, M. Jourdan, A. M. Pascaud, and M. Bergoin, Virology, 191:202-222, 1992, and C. Ding, M. Urabe, M. Bergoin, and R. M. Kotin, J. Virol. 76:338-345, 2002). We demonstrate here that persistent transgene expression in insect cells results from stable integration of transfected JcDNV-derived vectors into the host genome. To assess the integrative properties of JcDNV vectors, the green fluorescent protein (GFP) gfp marker gene was fused in frame into the major open reading frame (ORF1) of the viral sequence under the control of the P9 capsid protein promoter. In addition, the influence of the nonstructural proteins on the posttransfection maintenance of the vectors was examined by interruption of one or all three NS ORFs. Following transfection of Sf9 cells with each of the JcDNV constructs, clones showing persistent GFP expression were isolated. Structural analyses revealed that the majority of the JcDNV plasmid sequence was integrated into the genome of the fluorescent clones. Integration was observed whether or not NS proteins were expressed. However, the presence of NS genes in the constructs greatly influenced the number of integrated copies and their distribution in the host genome. Disruption of NS genes expression resulted in integration of head-to-tail concatemers at multiple sites within the genome. Further analyses demonstrated that the cis JcDNV 5′ inverted terminal repeat region was the primary site of recombination. Sequence analyses of integration junctions showed rearrangements of both flanking and internal sequences for most integrations. These findings demonstrate that JcDNV vectors integrate into insect cells in a manner similar to AAV plasmids in mammalian cells.
Numerical Simulations of MREIT Conductivity Imaging for Brain Tumor Detection

Magnetic resonance electrical impedance tomography (MREIT) is a new modality capable of imaging the electrical properties of human body using MRI phase information in conjunction with external current injection. Recent in vivo animal and human MREIT studies have revealed unique conductivity contrasts related to different physiological and pathological conditions of tissues or organs. When performing in vivo brain imaging, small imaging currents must be injected so as not to stimulate peripheral nerves in the skin, while delivery of imaging currents to the brain is relatively small due to the skull's low conductivity. As a result, injected imaging currents may induce small phase signals and the overall low phase SNR in brain tissues. In this study, we present numerical simulation results of the use of head MREIT for brain tumor detection. We used a realistic three-dimensional head model to compute signal levels produced as a consequence of a predicted doubling of conductivity occurring within simulated tumorous brain tissues. We determined the feasibility of measuring these changes in a time acceptable to human subjects by adding realistic noise levels measured from a candidate 3 T system. We also reconstructed conductivity contrast images, showing that such conductivity differences can be both detected and imaged.
How to study determinants related to medication adherence in newly diagnosed polyarthritis patients for the development of a prediction instrument

Introduction For patients with a chronic disease, the appropriate use of medication is the key to manage their illness. Adherence to medication is therefore important. Adherence can be divided into three parts: the initiation part, the execution phase, and the discontinuation part. Little is known about the determinants of the initiation part. For this reason, we describe the conduct of a stepwise procedure to study determinants of medication initiation for patients with a chronic disease. Methods/design The stepwise procedure comprises of eliciting a list of all potential determinants via literature review, interviewing patients, and consulting an expert panel. This is followed by embedding the determinants in a theoretical framework, developing a questionnaire, and choosing adherence measurement methods. The consecutive steps that we conducted for the development of a tool for the prediction of adherence in our study sample of early arthritis patients are described. Discussion Although we used a thorough procedure, there are still some pitfalls to take into account, such as the choice of theoretical framework. A strength of this study is that we use multiple adherence measurement methods and that we also take clinical outcomes into account.
A Generalized Performance Study of DS-CDMA Uplink/Downlink Receivers in NakagamiWideband Fading Channel

SUMMARY In this paper, we consider a mobile system consisting of a single isolated circular cell with K independent users simultaneously sharing the channel using binary DS-CDMA to establish a full duplex channel with the base station. Both coherent and differential detection RAKE receivers with Maximal Ratio Combining (MRC) techniques are considered. The performance of two uplink/downlink receivers in Nakagami wideband fading channel is studied. Our approach relies on the use of total instantaneous interference power calculations instead of the use of average power approximations. We analyzed and derived new exact formulae for bit error probabilities for the considered system, and presented a set of numerical results both for the exact formulae and Gaussian approximation. The performance comparisons suggest that the exact formulae provide superior performance to Gaussian approximation especially at low number of users and either high fading parameters of the desired user or low fading param
Modeling and validation of the real-time Mach scheduler

Real-time scheduling theory is designed to provide a priori verification that all real-time tasks meet their timing requirements. However, this body of theory generally assumes that resources are instantaneously pre-emptable and ignores the costs of systems services. In previous work [1, 2] we provided a theoretical foundation for including the costs of the operating system scheduler in the real-time scheduling framework. In this paper, we apply that theory to the Real-Time (RT) Mach scheduler. We describe a methodology for measuring the components of the RT Mach scheduler in user space. We analyze the predicted performance of different real-time task sets on the target system using the scheduling model and the measured characteristics. We then verify the model experimentally by measuring the performance of the real-time task sets, consisting of RT Mach threads, on the target system, The experimental measurements verify the analytical model to within a small percentage of error. Thus, using the model we have successfully predicted the performance of real-time task sets using system services, and developed consistent methodologies to accomplish that prediction.
A Fast Decision Tree Learning Algorithm

There is growing interest in scaling up the widely-used decision-tree learning algorithms to very large data sets. Although numerous diverse techniques have been proposed, a fast tree-growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential. In this paper, we present a novel, fast decision-tree learning algorithm that is based on a conditional independence assumption. The new algorithm has a time complexity of O(m ċ n), where m is the size of the training data and n is the number of attributes. This is a significant asymptotic improvement over the time complexity O(m ċ n2) of the standard decision-tree learning algorithm C4.5, with an additional space increase of only O(n). Experiments show that our algorithm performs competitively with C4.5 in accuracy on a large number of UCI benchmark data sets, and performs even better and significantly faster than C4.5 on a large number of text classification data sets. The time complexity of our algorithm is as low as naive Bayes'. Indeed, it is as fast as naive Bayes but outperforms naive Bayes in accuracy according to our experiments. Our algorithm is a core tree-growing algorithm that can be combined with other scaling-up techniques to achieve further speedup.
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)

One of the central problems in neuroscience is reconstructing synaptic connectivity in neural circuits. Synapses onto a neuron can be probed by sequentially stimulating potentially pre-synaptic neurons while monitoring the membrane voltage of the post-synaptic neuron. Reconstructing a large neural circuit using such a "brute force" approach is rather time-consuming and inefficient because the connectivity in neural circuits is sparse. Instead, we propose to measure a post-synaptic neuron's voltage while stimulating sequentially random subsets of multiple potentially pre-synaptic neurons. To reconstruct these synaptic connections from the recorded voltage we apply a decoding algorithm recently developed for compressive sensing. Compared to the brute force approach, our method promises significant time savings that grow with the size of the circuit. We use computer simulations to find optimal stimulation parameters and explore the feasibility of our reconstruction method under realistic experimental conditions including noise and non-linear synaptic integration. Multi-neuronal stimulation allows reconstructing synaptic connectivity just from the spiking activity of post-synaptic neurons, even when sub-threshold voltage is unavailable. By using calcium indicators, voltage-sensitive dyes, or multi-electrode arrays one could monitor activity of multiple post-synaptic neurons simultaneously, thus mapping their synaptic inputs in parallel, potentially reconstructing a complete neural circuit.
Image-Space Texture-Based Output-Coherent Surface Flow Visualization

Image-space line integral convolution (LIC) is a popular scheme for visualizing surface vector fields due to its simplicity and high efficiency. To avoid inconsistencies or color blur during the user interactions, existing approaches employ surface parameterization or 3D volume texture schemes. However, they often require expensive computation or memory cost, and cannot achieve consistent results in terms of both the granularity and color distribution on different scales. This paper introduces a novel image-space surface flow visualization approach that preserves the coherence during user interactions. To make the noise texture under different viewpoints coherent, we propose to precompute a sequence of mipmap noise textures in a coarse-to-fine manner for consistent transition, and map the textures onto each triangle with randomly assigned and constant texture coordinates. Further, a standard image-space LIC is performed to generate the flow texture. The proposed approach is simple and GPU-friendly, and can be easily combined with various texture-based flow visualization techniques. By leveraging viewpoint-dependent backward tracing and mipmap noise phase, our method can be incorporated with the image-based flow visualization (IBFV) technique for coherent visualization of unsteady flows. We demonstrate consistent and highly efficient flow visualization on a variety of data sets.
Removal Of Random-Valued Impulse Noise using Adaptive Centre Weighted Median Filters And Detail Preservation Method

This paper proposes a two-stage iterative method for removing random-valued impulse noise. In the first phase, we use the adaptive center-weighted median filter to identify pixels which are likely to be corrupted by noise (noise candidates). In the second phase, these noise candidates are restored using a detail-preserving regularization method which allows edges and noise-free pixels to be preserved. In the this paper these two phases are applied alternatively. Simulation
Automatic Text Categorization by Unsupervised Learning

The goal of text categorization is to classify documents into a certain number of predefined categories. The previous works in this area have used a large number of labeled training documents for supervised learning. One problem is that it is difficult to create the labeled training documents. While it is easy to collect the unlabeled documents, it is not so easy to manually categorize them for creating training documents. In this paper, we propose an unsupervised learning method to overcome these difficulties. The proposed method divides the documents into sentences, and categorizes each sentence using keyword lists of each category and sentence similarity measure. And then, it uses the categorized sentences for training. The proposed method shows a similar degree of performance, compared with the traditional supervised learning methods. Therefore, this method can be used in areas where low-cost text categorization is needed. It also can be used for creating training documents.