Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding a Haystack in Haystacks - Simultaneous Identification of Concepts in Large Bio-Medical Corpora

Since nearly all information is now created digitally, large text databases have become more prevalent than ever. Automatically mining information from these databases proves to be a challenge due to slow pattern/string matching techniques. In this paper we introduce a new, fast multi-string pattern matching method called the Block Suffix Shifting (BSS) algorithm, which is based on the well known Aho-Chorasick algorithm. The advantages of our algorithm include: the ability to exploit the natural structure of text, perform significant character shifting, avoid useless backtracking jumps, efficient matching time and avoid the typical ”sub-string” false positive errors. Our algorithm is applicable to many fields with free text, such as the health care domain and the scientific document field. In this paper, we apply the BSS algorithm to health care data and mine hundreds of thousands of medical concepts from a large Electronic Medical Record (EMR) corpora simultaneously and efficiently. Experimental results show the superiority of our algorithm when compared with the top of the line multi-string matching algorithms (the Aho-Corasick and the Wu-Manber algorithm).
Combining local and non-local information with dual decomposition for named entity recognition from text

Named entity recognition (NER) is the task of segmenting and classifying occurrences of names in text. In NER, local contextual cues provide important evidence, but non-local information from the whole document could also prove useful: for example, it is useful to know that “Mary Kay Inc.” has been mentioned in a document to classify subsequent mentions of “Mary Kay” as an organization and not as a person. Previous works for NER typically model the problem as a sequence labeling problem, coupling the predictions of neighboring words with a Markov model such as conditional random fields. We propose applying the dual decomposition approach to combine a local sentential model and a non-local label consistency model for NER. The dual decomposition approach is a fusion approach which combines two models by constraining them to agree on their predictions on the test data. Empirically, we show that this approach outperforms the local sentential models on four out of five data sets.
GRK2 in Lymphocytes: Expanding the Arsenal of Heart Failure Prognostics.

Heart failure (HF) has been described as the inability of the myocardium to deliver oxygen and nutrients to a degree commensurate with the metabolic requirements of the body.1 Myocardial dysfunction induces compensatory neurohumoral mechanisms, including the sympathetic nervous system (SNS), as an attempt to preserve contractile performance. Mediators of the SNS consist predominantly of 2 catecholamines, namely epinephrine and norepinephrine (NE), released by cardiac sympathetic nerve terminals or secreted directly into the circulation by the adrenal medulla. Effects of these neurotransmitters are mediated through cell surface adrenergic receptors (ARs), members of the G protein–coupled receptor superfamily. Stimulation of the β-AR promotes a conformational change to activate the heterotrimeric G protein Gα and Gβγ subunits, promoting positive inotropic and chronotropic effects culminating in improved myocardial function.2 Article, see p 1116 This functionally beneficial pathway refers exclusively, however, to acute receptor activation; sustained β-AR stimulation, as occurs in most cardiovascular disease, is characterized by molecular modifications, leading to reduced receptor sensitivity and membrane expression. Receptor desensitization and downregulation are regulatory processes thought to moderate persistent agonist stimulation to prevent catecholamine-induced toxicity. The desensitization process is initiated in large part by agonist-dependent phosphorylation of the receptor’s cytoplasmic tail by G protein–coupled receptor kinase 2 (GRK2), a serine/threonine kinase. GRK2 is a cytosolic enzyme that localizes to the plasma membrane after recruitment by active Gβγ subunits. This is followed by recruitment of β-arrestin to the phosphorylated receptor, which prevents recoupling of the dissociated cognate G protein and targets the receptor for internalization and eventual degradation. It is now appreciated that chronic SNS activity and subsequent GRK2-mediated receptor downregulation result in a loss of responsiveness to catecholamines and contribute to further contractile dysfunction and increased patient mortality.2,3 Pivotal studies have indicated that properties of β-AR signaling seem …
How to Make Bias and Variance Errors Insensitive to System and Model Complexity in Identification

Solutions to optimal input design problems for system identification are sometimes believed to be sensitive to the underlying assumptions. For example, a wide class of problems can be solved with sinusoidal inputs with the same number of excitation frequencies (over the frequency range ) as the number of model parameters. The order of the true system is in many cases unknown and, hence, so is the required number of frequencies in the input. In this contribution we characterize when and how the input spectrum can be chosen so that the (asymptotic) variance error of a scalar function of the model parameters becomes independent of the order of the true system. A connection between these robust designs and the solutions of certain optimal input design problems is also made. Furthermore, we show that there are circumstances when using this type of input allows some model properties to be estimated consistently even when the model order is lower than the order of the true system. The results are derived under the assumptions of causal linear time invariant systems operating in open loop and excited by an input signal having a rational spectral factor with all poles and zeros strictly inside the unit circle.
Efficient Checkpointing of Live Virtual Machines

The ability to save the state of a running virtual machine (VM) for later restoration is an important tool for home, server, and virtual desktop cloud (VDC) environments in order to achieve optimal and balanced hardware utilization. With guest memory sizes of four to eight gigabytes being the norm the time- and space-overhead of storing VM checkpoints still prevents an effective use of the technique. This work presents a method for fast and space-efficient checkpointing of VMs. Based on the observation that operating systems cache disk blocks in memory, the proposed technique transparently intercepts I/O operations and maintains an up-to-date mapping of memory pages and disk blocks containing identical data. At a checkpoint, those memory pages are excluded from the checkpoint image leading to a significant reduction of both the time and space required to take a checkpoint of a running VM. The broad applicability and good performance of the proposed method is demonstrated by an extensive set of experiments. We have implemented the technique for para-virtualized (PV), PVHVM, and fully-virtualized (HVM) guests in the Xen hypervisor. In comparison with an unmodified Xen hypervisor, experiments with Linux and Windows guests, on average, achieve a 86, 76, 53, and 47 percent reduction in the stored data and a 73, 62, 47, and 38 percent shorter time required to take a checkpoint for PV, PVHVM, HVM Linux, and HVM Windows guests, respectively.
Analysis of damaged region of carbon implanted alumina

Implantation was performed on surface‐polished and thermal‐treated alumina discs with 75 keV monocharged carbon ions at doses of 1 × 1017 and 5 × 1017 ions cm−2. The alumina targets were kept at room temperature. The structural modifications induced during ion irradiation were studied by the scanning and scanning transmission electron microscopes. Alumina is readily amorphized at room temperature with carbon ions. The width of the ion‐beam induced disordered area increases with ion dose. As it was established by selected area electron diffraction and electron energy loss spectra, low implanted specimens still show subsurface crystalline areas whose diffraction patterns could be assigned to a deformed structure of alumina. This deformed belt is shown beneath the amorphous region on highly implanted samples. Electron energy loss spectroscopy allows to follow the carbon line profile from surface into the bulk material, pointing the maximum concentration of implanted ion on the lower half of the amorphous region. Structural changes of Al2O3 caused by carbon ion irradiation were corroborated with X‐ray photoelectron spectroscopy. The X‐ray photoelectron spectroscopy experiments showed that the oxygen concentration in the vicinity of the implanted alumina surface was reduced suggesting that this element was selectively sputtered by carbon irradiation. As a function of depth, peaks referring to Al–O bindings diminish while contributions of reduced aluminium and metal carbides were found, reaching the corresponding maxima at a depth of about 130 nm.
COPS: Detecting Co-Occurrence and Spatial Arrangement of Transcription Factor Binding Motifs in Genome-Wide Datasets

In multi-cellular organisms, spatiotemporal activity of cis-regulatory DNA elements depends on their occupancy by different transcription factors (TFs). In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with cis-regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficient for dissecting complex regulatory networks. Thus, potent computational tools detecting statistically significant and biologically relevant TF-motif co-occurrences in genome-wide datasets are essential for analyzing context-dependent transcriptional regulation. We have developed COPS (Co-Occurrence Pattern Search), a new bioinformatics tool based on a combination of association rules and Markov chain models, which detects co-occurring TF binding sites (BSs) on genomic regions of interest. COPS scans DNA sequences for frequent motif patterns using a Frequent-Pattern tree based data mining approach, which allows efficient performance of the software with respect to both data structure and implementation speed, in particular when mining large datasets. Since transcriptional gene regulation very often relies on the formation of regulatory protein complexes mediated by closely adjoining TF binding sites on CRMs, COPS additionally detects preferred short distance between co-occurring TF motifs. The performance of our software with respect to biological significance was evaluated using three published datasets containing genomic regions that are independently bound by several TFs involved in a defined biological process. In sum, COPS is a fast, efficient and user-friendly tool mining statistically and biologically significant TFBS co-occurrences and therefore allows the identification of TFs that combinatorially regulate gene expression.
A Unified Approach Towards Green Resource Allocation in Relay-Assisted Multiuser Networks

The rapid growth in energy consumption due to strong demands of wireless multimedia services, has become a major concern from an environmental perspective. In this paper, we investigate a novel energy-efficient resource allocation scheme for relay-assisted multiuser networks to maximize the energy efficiency (EE) of the network by jointly optimizing the subcarrier pairing permutation formed in one-to-many/many-to-one manner, subcarrier allocation, as well as the power allocation all together. By analyzing the properties of the complex mixed-integer nonlinear programming problem, which is generally very difficult to solve in its original form, we transform the problem into an equivalent convex problem by relaxing the integer variables using the concept of subcarrier time sharing, and by applying a successive convex approximation approach. Based on the dual decomposition method, we derive an optimal solution to the joint optimization problem. Numerical results are provided to validate the theoretical findings and to demonstrate the effectiveness of the proposed algorithms.
Automatic analysis of DNA microarray images using mathematical morphology

MOTIVATION DNA microarrays are an experimental technology which consists in arrays of thousands of discrete DNA sequences that are printed on glass microscope slides. Image analysis is an important aspect of microarray experiments. The aim of this step is to reduce an image of spots into a table with a measure of the intensity for each spot. Efficient, accurate and automatic analysis of DNA spot images is essential in order to use this technology in laboratory routines. RESULTS We present an automatic non-supervised set of algorithms for a fast and accurate spot data extraction from DNA microarrays using morphological operators which are robust to both intensity variation and artefacts. The approach can be summarised as follows. Initially, a gridding algorithm yields the automatic segmentation of the microarray image into spot quadrants which are later individually analysed. Then the analysis of the spot quadrant images is achieved in five steps. First, a pre-quantification, the spot size distribution law is calculated. Second, the background noise extraction is performed using a morphological filtering by area. Third, an orthogonal grid provides the first approach to the spot locus. Fourth, the spot segmentation or spot boundaries definition is carried out using the watershed transformation. And fifth, the outline of detected spots allows the signal quantification or spot intensities extraction; in this respect, a noise model has been investigated. The performance of the algorithm has been compared with two packages: ScanAlyze and Genepix, showing its robustness and precision.
Accelerated aging with electrical overstress and prognostics for power MOSFETs

Power electronics play an increasingly important role in energy applications as part of their power converter circuits. Understanding the behavior of these devices, especially their failure modes as they age with nominal usage or sudden fault development is critical in ensuring efficiency. In this paper, a prognostics based health management of power MOSFETs undergoing accelerated aging through electrical overstress at the gate area is presented. Details of the accelerated aging methodology, modeling of the degradation process of the device and prognostics algorithm for prediction of the future state of health of the device are presented. Experiments with multiple devices demonstrate the performance of the model and the prognostics algorithm as well as the scope of application.
A Bayesian Network Approach to Operon Prediction

MOTIVATION In order to understand transcription regulation in a given prokaryotic genome, it is critical to identify operons, the fundamental units of transcription, in such species. While there are a growing number of organisms whose sequence and gene coordinates are known, by and large their operons are not known. RESULTS We present a probabilistic approach to predicting operons using Bayesian networks. Our approach exploits diverse evidence sources such as sequence and expression data. We evaluate our approach on the Escherichia coli K-12 genome where our results indicate we are able to identify over 78% of its operons at a 10% false positive rate. Also, empirical evaluation using a reduced set of data sources suggests that our approach may have significant value for organisms that do not have as rich of evidence sources as E.coli. AVAILABILITY Our E.coli K-12 operon predictions are available at http://www.biostat. wisc.edu/gene-regulation.
Detection of Temporal Delays in Visual-Haptic Interfaces

This paper addresses the question of how large the temporal delay between a visual and a haptic stimulus may be such that the stimuli are still perceived as being synchronous. Participants had to judge whether the moment at which a graphical object collided with a virtual wall occurred simultaneously with the moment at which a force was felt through a force feedback joystick. Participants either moved the joystick to drive the object (active touch) or held the joystick in a steady position while the object moved by itself (passive touch). Participants were found to be very sensitive to visual-haptic time delays. Sensitivity was higher for passive touch than for active touch. The minimum delay at which participants judged the stimuli as asynchronous was on average 45 ms. The delay at which the proportion of synchronous judgments reached a maximum was on average close to zero. The results indicate that the temporal accuracy of visual-haptic interfaces has to meet stringent requirements in order to optimize the overall realism that users experience. Actual or potential applications of this research include teleoperation, medical training, computer-aided-design, and scientific visualization.
Computing term similarity by large probabilistic isA knowledge

Computing semantic similarity between two terms is essential for a variety of text analytics and understanding applications. However, existing approaches are more suitable for semantic similarity between words rather than the more general multi-word expressions (MWEs), and they do not scale very well. Therefore, we propose a lightweight and effective approach for semantic similarity using a large scale semantic network automatically acquired from billions of web documents. Given two terms, we map them into the concept space, and compare their similarity there. Furthermore, we introduce a clustering approach to orthogonalize the concept space in order to improve the accuracy of the similarity measure. Extensive studies demonstrate that our approach can accurately compute the semantic similarity between terms with MWEs and ambiguity, and significantly outperforms 12 competing methods.
Dual Dependence of Cryobiogical Properties of Sf21 Cell Membrane on the Temperature and the Concentration of the Cryoprotectant

The Sf21 cell line is extensively used for virus research and producing heterologous recombinant proteins. To develop optimal strategies for minimizing cell injury due to intracellular ice formation and excessive volume shrinkage during cryopreservation, the fundamental transport properties including the osmotic inactive volume (Vb), the hydraulic conductivity (Lp), and the glycerol permeability (Ps) of Sf21 cell membrane at 25, 15, 5 and −2°C were characterized using a micro-perfusion chamber. The effects of temperature on the hydraulic conductivity and the glycerol permeability of Sf21 cell membrane, reflected by the activation energies, were quantitatively investigated. It was found that the hydraulic conductivity decreases along with the increase of the final CPA concentration at a given temperature, and quantitative analysis indicates that the hydraulic conductivity has a significant linear attenuation along with the increase of the concentration of glycerol. Therefore, we incorporate the concentration dependence of the hydraulic conductivity into the classic Arrhenius relationship by replacing the constant reference value of the hydraulic conductivity at the reference temperature with a function that is linearly dependent on the CPA concentration. Consequently, the prediction of the Arrhenius relationship is improved, and the novel Arrhenius relationship could be very important to the development of optimal strategies for cell cryopreservation.
A clinical case study of the use of ecological momentary assessment in obsessive compulsive disorder

Accurate assessment of obsessions and compulsions is a crucial step in treatment planning for Obsessive-Compulsive Disorder (OCD). In this clinical case study, we sought to determine if the use of Ecological Momentary Assessment (EMA) could provide additional symptom information beyond that captured during standard assessment of OCD. We studied three adults diagnosed with OCD and compared the number and types of obsessions and compulsions captured using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) compared to EMA. Following completion of the Y-BOCS interview, participants then recorded their OCD symptoms into a digital voice recorder across a 12-h period in reply to randomly sent mobile phone SMS prompts. The EMA approach yielded a lower number of symptoms of obsessions and compulsions than the Y-BOCS but produced additional types of obsessions and compulsions not previously identified by the Y-BOCS. We conclude that the EMA-OCD procedure may represent a worthy addition to the suite of assessment tools used when working with clients who have OCD. Further research with larger samples is required to strengthen this conclusion.
photosynthesis by regulating antioxidant potential in tomato seedlings

The effect of cadmium (Cd) on growth, photosynthesis, biochemical attributes and antioxidant enzymes activities in tomato (Solanum lycopersicum L.) seedlings was studied and the role of kinetin (KN) to ameliorate Cd toxicity was examined. Tomato seedlings were subjected to two doses of Cd viz. 3 and 9 mg Cd kg P1 P sand. Cadmium at both the doses significantly declined the growth and photosynthesis, total chlorophyll, protein and carbohydrate contents in its concentration dependent manner. The oxidative stress markers such as superoxide radical (SOR), hydrogen peroxide (H
Automated Worm Fingerprinting

Network worms are a clear and growing threat to the security of today's Internet-connected hosts and networks. The combination of the Internet's unrestricted connectivity and widespread software homogeneity allows network pathogens to exploit tremendous parallelism in their propagation. In fact, modern worms can spread so quickly, and so widely, that no human-mediated reaction can hope to contain an outbreak. In this paper, we propose an automated approach for quickly detecting previously unknown worms and viruses based on two key behavioral characteristics - a common exploit sequence together with a range of unique sources generating infections and destinations being targeted. More importantly, our approach - called "content sifting" - automatically generates precise signatures that can then be used to filter or moderate the spread of the worm elsewhere in the network. Using a combination of existing and novel algorithms we have developed a scalable content sifting implementation with low memory and CPU requirements. Over months of active use at UCSD, our Earlybird prototype system has automatically detected and generated signatures for all pathogens known to be active on our network as well as for several new worms and viruses which were unknown at the time our system identified them. Our initial experience suggests that, for a wide range of network pathogens, it may be practical to construct fully automated defenses - even against so-called "zero-day" epidemics.