Miracle-dz/UniFAR-FTUs huggingface dataset:
Delayed-onset muscle soreness: a pilot study to assess analgesic study design features

Abstract Based on a thorough review of the available literature in the delayed-onset muscle soreness (DOMS) model, we identified multiple study design characteristics that are considered to be normative in acute pain research but have not been followed in a majority of published DOMS experiments. We designed an analgesic investigation using the DOMS model that both complied with current scientifically accepted standards for the conduct of analgesic studies and demonstrated reasonable assay sensitivity. This randomized, double-blind, placebo-controlled within-subject study compared the efficacy of topical diclofenac sodium 1% with a matching placebo in reducing pain associated with DOMS. After exercise, subjects reporting DOMS received topical diclofenac sodium gel 1% (DSG 1%) applied to one leg and placebo to the other every 6 hours for 48 hours. Pain intensity was assessed at rest, upon standing, and when walking in the 48 hours after initial drug application (T0). The primary end point was the reduction in pain intensity (SPID 24) on walking. Subjects receiving DSG 1% had less pain while walking compared with those receiving placebo at 24 hours (SPID 24 = 34.9 [22.9] and 23.6 [19.4], respectively; P = 0.032). This investigation used experimental techniques that have been vetted in the field of exercise physiology and superimposed techniques that are considered to be best practice in the field of analgesic research. Over time and with the help of colleagues in both fields of study, similar investigations will validate design features that impact the assay sensitivity of analgesic end points in DOMS models. In addition, the study confirmed the analgesic efficacy of topical DSG 1% over placebo in subjects experiencing DOMS.
The Quality of Fancy Yarn, Part II, Practical Experiments and Application

In Part I of this paper the author presented and detailed the methods he suggested to account for the subject of quality of fancy yarn. These include all factors and concepts which should be used to account for the structure of various types of fancy yarn. In addition, the procedures and apparati needed to apply these methods were detailed. In Part II, however the author applied his method to deal with gimp yarn and bouclé yarn which are traditional two types of fancy yarn. A comparison between these methods and the traditional subjective method of quality assessments of fancy yarns revealed high levels of agreement. The author produced several different gimp yarns and boucle yarns and then used the Shape Factor of Fancy Yarn (ShF), the Relative Shape of Fancy Yarn (RSI) and Circularity Ratio of Fancy Profile(CR) to scientifically and objectively judge the quality of several gimp yarns and bouclé yarns. In the end, it was possible to arrange these yarns in ascending or descending order in terms of their quality, e.g. texture, structure, and fancy bulkiness.
Mining Group Nonverbal Conversational Patterns Using Probabilistic Topic Models

The automatic discovery of group conversational behavior is a relevant problem in social computing. In this paper, we present an approach to address this problem by defining a novel group descriptor called bag of group-nonverbal-patterns (NVPs) defined on brief observations of group interaction, and by using principled probabilistic topic models to discover topics. The proposed bag of group NVPs allows fusion of individual cues and facilitates the eventual comparison of groups of varying sizes. The use of topic models helps to cluster group interactions and to quantify how different they are from each other in a formal probabilistic sense. Results of behavioral topics discovered on the Augmented Multi-Party Interaction (AMI) meeting corpus are shown to be meaningful using human annotation with multiple observers. Our method facilitates “group behavior-based” retrieval of group conversational segments without the need of any previous labeling.
Development of Postinfection Epilepsy After Theiler's Virus Infection of C57BL/6 Mice

Viral infection of the central nervous system can lead to long-term neurologic defects, including increased risk for the development of epilepsy. We describe the development of the first mouse model of viral-induced epilepsy after intracerebral infection with Theiler's murine encephalomyelitis virus. Mice were monitored with long-term video-electroencephalogram at multiple time points after infection. Most miceexhibited short-term symptomatic seizures within 3 to 7days of infection. This was followed by a distinct latent period in which no seizures were observed. Prolonged video-electroencephalogram recordings at 2, 4, and 7 months after the initial infection revealed that asignificant proportion of the mice developed profound, spontaneous epileptic seizures. Neuropathologic examination revealed hippocampal sclerosis in animals with epilepsy. Theiler's murine encephalomyelitis virus-infected C57BL/6 mice represent a novel "hit-and-run" model toinvestigate mechanisms underlying viral-induced short-term symptomatic seizures, epileptogenesis, and epilepsy. Importantly, this model will also be useful to investigate novel therapies for the treatment and prevention of epilepsy.
Hijacking of Host Cellular Functions by an Intracellular Parasite, the Microsporidian Anncaliia algerae

Intracellular pathogens including bacteria, viruses and protozoa hijack host cell functions to access nutrients and to bypass cellular defenses and immune responses. These strategies have been acquired through selective pressure and allowed pathogens to reach an appropriate cellular niche for their survival and growth. To get new insights on how parasites hijack host cellular functions, we developed a SILAC (Stable Isotope Labeling by Amino Acids in Cell culture) quantitative proteomics workflow. Our study focused on deciphering the cross-talk in a host-parasite association, involving human foreskin fibroblasts (HFF) and the microsporidia Anncaliia algerae, a fungus related parasite with an obligate intracellular lifestyle and a strong host dependency. The host-parasite cross-talk was analyzed at five post-infection times 1, 6, 12 and 24 hours post-infection (hpi) and 8 days post-infection (dpi). A significant up-regulation of four interferon-induced proteins with tetratricopeptide repeats IFIT1, IFIT2, IFIT3 and MX1 was observed at 8 dpi suggesting a type 1 interferon (IFN) host response. Quantitative alteration of host proteins involved in biological functions such as signaling (STAT1, Ras) and reduction of the translation activity (EIF3) confirmed a host type 1 IFN response. Interestingly, the SILAC approach also allowed the detection of 148 A. algerae proteins during the kinetics of infection. Among these proteins many are involved in parasite proliferation, and an over-representation of putative secreted effectors proteins was observed. Finally our survey also suggests that A. algerae could use a transposable element as a lure strategy to escape the host innate immune system.
Unsupervised Methods for Language Modeling

Language models are crucial for many tasks in NLP1 and N-grams are the best way to build them. Huge effort is being invested in improving n-gram language models. By introducing external information (morphology, syntax, partitioning into documents, etc.) into the models a significant improvement can be achieved. The models can however be improved with no external information and smoothing is an excellent example of such an improvement. Thesis summarizes the state-of-the-art approaches to unsupervised language modeling with emphases on the inflectional languages, which are particularly hard to model. It is focused on methods that can discover hidden patterns that are already in a training corpora. These patterns can be very useful for enhancing the performance of language modeling, moreover they do not require additional information sources. Copies of this report are available on http://www.kiv. zcu.cz/publications/ or by surface mail on request sent to the following address: University of West Bohemia in Pilsen Department of Computer Science and Engineering Univerzitńı 8 30614 Pilsen Czech Republic Copyright c © 2012 University of West Bohemia in Pilsen, Czech Republic Natural Language Processing
Learning from failure in health care: frequent opportunities, pervasive barriers

The notion that hospitals and medical practices should learn from failures, both their own and others’, has obvious appeal. Yet, healthcare organisations that systematically and effectively learn from the failures that occur in the care delivery process, especially from small mistakes and problems rather than from consequential adverse events, are rare. This article explores pervasive barriers embedded in healthcare’s organisational systems that make shared or organisational learning from failure difficult and then recommends strategies for overcoming these barriers to learning from failure, emphasising the critical role of leadership. Firstly, leaders must create a compelling vision that motivates and communicates urgency for change; secondly, leaders must work to create an environment of psychological safety that fosters open reporting, active questioning, and frequent sharing of insights and concerns; and thirdly, case study research on one hospital’s organisational learning initiative suggests that leaders can empower and support team learning throughout their organisations as a way of identifying, analysing, and removing hazards that threaten patient safety.
Tradition versus fashion in consumer choice

Evidence is growing that in many markets consumers select not simply on the basis of the perceived attributes of products, but their preferences are modied by the behaviour of others. Economists have paid relatively little attention to such markets. The classic Bass diusion model incorporates the imitation of others as a part of the behavioural rules used by consumers in making choice. We extend the Bass model to be able to apply it to long-term case studies where substantial changes over time in the population making choices at any given point have to be taken into account. We consider evidence from the activity of hill-walking. We use as a particular illustration the Munros, a list of Scottish hills over 3,000 feet in height which many British walkers aspire to complete. The extension to the Bass model can of course be used in other situations where such population changes are important.
Feature extraction of epilepsy EEG using discrete wavelet transform

Epilepsy is one of the most common a chronic neurological disorders of the brain that affect millions of the world's populations. It is characterized by recurrent seizures, which are physical reactions to sudden, usually brief, excessive electrical discharges in a group of brain cells. Hence, seizure identification has great importance in clinical therapy of epileptic patients. Electroencephalogram (EEG) is most commonly used in epilepsy detection since it includes precious physiological information of the brain. However, it could be a challenge to detect the subtle but critical changes included in EEG signals. Feature extraction of EEG signals is core trouble on EEG-based brain mapping analysis. This paper will extract ten features from EEG signal based on discrete wavelet transform (DWT) for epilepsy detection. These numerous features will help the classifiers to achieve a good accuracy when utilize to classify EEG signal to detect epilepsy. Subsequently, the results have illustrated that DWT has been adopted to extract various features i.e., Entropy, Min, Max, Mean, Median, Standard deviation, Variance, Skewness, Energy and Relative Wave Energy (RWE).
New concepts on denoising and sharpening of video signals

This paper presents a new concept for improving video signals. Separating noise from the images simultaneously with sharpening these images is one key idea. Thereby, the different frequency channels of the video signal are processed in different ways in order to achieve the most attractive effect on the human visual system. New algorithms including luminance transition improvement (LTI), contrast adaptive peaking in the case of the luminance, as well as chrominance transition improvement (CTI) in the case of the color components are presented. The whole image improvement concept including all its components will soon be produced in a 0.18 /spl mu/m technology as the display processor part of a new chip for 100 Hz TV applications.
FUNEUS: A Neurofuzzy Approach Based on Fuzzy Adaline Neurons

Today hybrid computing is a popular framework for solving complex problems. If we have knowledge expressed in rules, we can build an Expert System, and if we have data, or can learn from stimulation (training) then we can use Artificial Neural Networks. In this paper we present the FUzzy NEUrule System (FUNEUS) which is a Neuro Fuzzy approach based on fuzzy Adaline neurons and uses Differential Evolution for optimization of membership functions. According to previous Neuro-fuzzy approaches and a well-defined hybrid system HYMES, FUNEUS is an attempt to the direction for integration of neural and fuzzy components with Differential evolution. Despite the fact that it remains difficult to compare neurofuzzy systems conceptually and evaluate their performance, early experimental results proved a promising performance and the need for further evaluation in other application domains.
Imprint Lithography using Nanocrystalline CVD Diamond Molds

Micro-/nanoimprint lithography is a high-throughput, high-resolution, and low-cost mass production fabrication process often used for creating microfluidic devices and optical components. In the imprint lithography process, a surface pattern of a stamp is replicated into a material by mechanical contact and three dimensional material displacements. These stamps are exposed to high pressures and temperatures and need to be able to withstand these circumstances in order to be durable. Diamond is potentially the ideal surface material for an imprint lithography stamp, since it has a high hardness, a high thermal conductivity coefficient, a low thermal expansion coefficient, it is chemically inert and highly wear resistant. Up until now, only molds made completely out of diamond have been used for imprint lithography. These molds were fabricated using single crystal or polished Chemical Vapor Deposited (CVD) diamond, which were then micro-structured by focused ion beam, reactive ion etching or e-beam lithography. Unfortunately, the availability of large area, single crystal diamond is very limited, and therefore extremely costly. On the other hand, diamond synthesis by chemical vapor deposition provides the possibility to deposit polycrystalline diamond films on areas up to tens of cm2. However, it is a rather slow process where typical growth rates are about 1 μm/hour, and thus production of full diamond stamps is time consuming and expensive. In this thesis, two new methods for the fabrication of CVD diamond imprint lithography molds have been developed. In the first method, a layer of 0.5 μm CVD diamond is deposited on micro-structured silicon. The second method makes use of porous silicon templates through which diamond can be grown, resulting in micro-structured diamond molds. Since polishing micro-structured diamond layers is not possible, the molds were coated with an anti-adhesion layer in order to facilitate release of the mold after imprinting. Both methods proved to be suited for imprinting into a cyclic olefin copolymer developed by TOPAS (grade 6013). With the first method, imprint dimensions of 1 μm with a depth of 350 nm were realized, and imprint dimensions of 2.5 μm with a depth of 2 μm were realized with the second fabrication method. These new approaches greatly reduce complexity of the fabrication process for durable stamps, and thereby the costs involved in creating imprint lithography stamps.
Wood furniture waste–based recycled 3-D printing filament

Abstract The Michigan furniture industry produces >150 tons/day of wood-based waste that can be upcycled into a wood–polymer composite (WPC). This study investigated the viability of using furniture waste as a feedstock for 3-D printer filament to produce furniture components. The process involves grinding and milling board scraps made of both LDF-MDF-LDF (where LDF is light-density fill and MDF is medium-density fill) and melamine-particleboard-paper impregnated with phenolic resins, premixing wood-based powder with the biopolymer polylactic acid, extruding twice through open source recyclebots to fabricate homogeneous 3-D printable WPC filament, and printing with open source fused filament fabrication–based 3-D printers. The results indicate that there is a significant opportunity for waste-based composite WPCs to be used as 3-D printing filament.
Redundancy and Molecular Evolution: The Rapid Induction of Bone Formation by the Mammalian Transforming Growth Factor-β3 Isoform

The soluble osteogenic molecular signals of the transforming growth factor-β (TGF-β) supergene family are the molecular bases of the induction of bone formation and postnatal bone tissue morphogenesis with translation into clinical contexts. The mammalian TGF-β3 isoform, a pleiotropic member of the family, controls a vast array of biological processes including the induction of bone formation. Recombinant hTGF-β3 induces substantial bone formation when implanted with either collagenous bone matrices or coral-derived macroporous bioreactors in the rectus abdominis muscle of the non-human primate Papio ursinus. In marked contrast, the three mammalian TGF-βs do not initiate the induction of bone formation in rodents and lagomorphs. The induction of bone by hTGF-β3/preloaded bioreactors is orchestrated by inducing fibrin-fibronectin rings that structurally organize tissue patterning and morphogenesis within the macroporous spaces. Induced advancing extracellular matrix rings provide the structural anchorage for hyper chromatic cells, interpreted as differentiating osteoblasts re-programmed by hTGF-β3 from invading myoblastic and/or pericytic differentiated cells. Runx2 and Osteocalcin expression are significantly up-regulated correlating to multiple invading cells differentiating into the osteoblastic phenotype. Bioreactors pre-loaded with recombinant human Noggin (hNoggin), a BMPs antagonist, show down-regulation of BMP-2 and other profiled osteogenic proteins' genes resulting in minimal bone formation. Coral-derived macroporous constructs preloaded with binary applications of hTGF-β3 and hNoggin also show down-regulation of BMP-2 with the induction of limited bone formation. The induction of bone formation by hTGF-β3 is via the BMPs pathway and it is thus blocked by hNoggin. Our systematic studies in P. ursinus with translational hTGF-β3 in large cranio-mandibulo-facial defects in humans are now requesting the re-evaluation of “Bone: formation by autoinduction” in primate models including humans.
Random Rough Subspace Based Neural Network Ensemble for Insurance Fraud Detection

In this paper, a random rough subspace based neural network ensemble method is proposed for insurance fraud detection. In this method, rough set reduction is firstly employed to generate a set of reductions which can keep the consistency of data information. Secondly, the reductions are randomly selected to construct a subset of reductions. Thirdly, each of the selected reductions is used to train a neural network classifier based on the insurance data. Finally, the trained neural network classifiers are combined using ensemble strategies. For validation, a real automobile insurance case is used to test the effectiveness and efficiency of our proposed method with two popular evaluation criteria including the percentage correctly classified (PCC) and the receive operating characteristic (ROC) curve. The experimental results show that our proposed model outperforms single classifier and other models used in comparison. The findings of this study reseal that the random rough subspace based neural network ensemble method can provide a faster and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection.
Localizes to the Inner PlasmaMembrane of PancreaticCancerCells andRegulatesCellGrowth and Disease Progression through Critical Cell-Cycle Regulatory Pathways

Pancreatic ductal adenocarcinoma (PDAC) carries the most dismal prognosis of all solid tumors and is generally strongly resistant to currently available chemoand/or radiotherapy regimens, including targeted molecular therapies. Therefore, unraveling themolecular mechanisms underlying the aggressive behavior of pancreatic cancer is a necessary prerequisite for thedevelopment of novel therapeutic approaches. We previously identified the protein placenta-specific 8 (PLAC8, onzin) in a genome-wide search for target genes associated with pancreatic tumor progression and demonstrated that PLAC8 is strongly ectopically expressed in advanced preneoplastic lesions and invasive human PDAC. However, the molecular function of PLAC8 remained unclear, and accumulating evidence suggested its role is highly dependent on cellular and physiologic context. Here, we demonstrate that in contrast to other cellular systems, PLAC8 protein localizes to the inner face of the plasma membrane in pancreatic cancer cells, where it interacts with specificmembranous structures in a temporally and spatially stable manner. Inhibition of PLAC8 expression strongly inhibited pancreatic cancer cell growth by attenuating cell-cycle progression, which was associated with transcriptional and/or posttranslational modification of the central cell-cycle regulators CDKN1A, retinoblastoma protein, and cyclin D1 (CCND1), but did not impact autophagy. Moreover, Plac8 deficiency significantly inhibited tumor formation in genetically engineered mouse models of pancreatic cancer. Together, our findings establish PLAC8 as a central mediator of tumor progression inPDACandas a promising candidate gene for diagnostic and therapeutic targeting. Cancer Res; 76(1); 96–107. 2015 AACR.
Tunnel Technique With Collagen Matrix Compared With Connective Tissue Graft for Treatment of Periodontal Recession: A Randomized Clinical Trial.

BACKGROUND The aim of this study is to compare efficacy of the tunnel technique for root coverage using collagen matrix (CM) versus connective tissue graft (CTG) for treatment of multiple recessions of Miller Classes I and II over a short period of time. METHODS Twenty-eight patients were enrolled in the study. Patients in the control group were treated with the tunnel technique using CTGs, whereas patients in the test group were treated with the tunnel technique using xenogeneic CM. Clinical recordings were obtained at baseline and after 3 and 6 months. Percentages of average recession coverage (ARC) and complete recession coverage (CRC) were evaluated 3 and 6 months after surgery. RESULTS Significant decreases were recorded in both groups of recession parameters compared with baseline measurements. Mean recession depth (0.21 versus 0.39 mm) and recession area (0.31 versus 0.53 mm2) after 6 months were significantly higher in the test group (P <0.05). Mean keratinized tissue width (KTW) increased at a similar rate in both groups (1.0 versus 0.8 mm for control and test groups, respectively). ARC after 6 months was 95% in the control group and 91% in the test group (P <0.05), and CRC was 71.4% (10/14) in the control group and 14.3% (2/14) in the test group (P <0.05). CONCLUSION Xenogeneic CM combined with tunnel technique leads to satisfactory ARC and increase in KTW similar to CTG, but yields lower unsatisfactory CRC.
Classic cryptanalysis using hidden Markov models

ABSTRACT In this article, the authors present a detailed introduction to hidden Markov models (HMM). They then apply HMMs to the problem of solving simple substitution ciphers, and they empirically determine the accuracy as a function of the ciphertext length and the number of random restarts. Application to homophonic substitutions and other classic ciphers is briefly considered.