Miracle-dz/UniFAR-FTUs huggingface dataset:
Requirement of Retinoic Acid Receptor β for Genipin Derivative-Induced Optic Nerve Regeneration in Adult Rat Retina

Like other CNS neurons, mature retinal ganglion cells (RGCs) are unable to regenerate their axons after nerve injury due to a diminished intrinsic regenerative capacity. One of the reasons why they lose the capacity for axon regeneration seems to be associated with a dramatic shift in RGCs’ program of gene expression by epigenetic modulation. We recently reported that (1R)-isoPropyloxygenipin (IPRG001), a genipin derivative, has both neuroprotective and neurite outgrowth activities in murine RGC-5 retinal precursor cells. These effects were both mediated by nitric oxide (NO)/S-nitrosylation signaling. Neuritogenic activity was mediated by S-nitrosylation of histone deacetylase-2 (HDAC2), which subsequently induced retinoic acid receptor β (RARβ) expression via chromatin remodeling in vitro. RARβ plays important roles of neural growth and differentiation in development. However, the role of RARβ expression during adult rat optic nerve regeneration is not clear. In the present study, we extended this hypothesis to examine optic nerve regeneration by IPRG001 in adult rat RGCs in vivo. We found a correlation between RARβ expression and neurite outgrowth with age in the developing rat retina. Moreover, we found that IPRG001 significantly induced RARβ expression in adult rat RGCs through the S-nitrosylation of HDAC2 processing mechanism. Concomitant with RARβ expression, adult rat RGCs displayed a regenerative capacity for optic axons in vivo by IPRG001 treatment. These neuritogenic effects of IPRG001 were specifically suppressed by siRNA for RARβ. Thus, the dual neuroprotective and neuritogenic actions of genipin via S-nitrosylation might offer a powerful therapeutic tool for the treatment of RGC degenerative disorders.
Tumor profiling of gastric and esophageal carcinoma reveal different treatment options

Background: NCCN states that chemotherapies for advanced esophageal and gastric cancers may be used interchangeably. Biomarkers from gastroesophageal cancer patients were interrogated to identify actionable alterations with therapeutic implications. Methods: 666 gastric and 640 esophageal cancer cases referred to Caris Life Sciences between 2009 thru 2013 were evaluated. Specific testing was performed, which included a combination of sequencing (Sanger, NGS) and protein expression (IHC). Results: In the complete cohort (n = 1306), 30 of 45 genes tested harbored mutations; highest rates were seen in TP53 (54%), APC (10%), SMAD4 (5.9%), KRAS (5.9%), and PIK3CA (5.1%). IHC of TOP2A was high in 76% of cases, TOPO1 in 51% and SPARC in 25%; low IHC of ERCC1 was seen in 65%, RRM1 in 62%, TS in 61% and MGMT in 45%, indicating potential benefit from epirubicin, irinotecan, nab-paclitaxel, platinum-based agents, gemcitabine, 5FU/capecitabine and temozolomide, respectively. In the HER2+ cohort (n = 88), 50% of patients demonstrated possible benefit from a combination of trastuzumab with 5FU/capecitabine based on concurrent low TS, 53% with irinotecan (high TOPO1), 63% with cisplatin (low ERCC1) and 55% with gemcitabine (low RRM1). Subgroup analysis by tumor origin demonstrated significant differences in actionable biomarker profiles with HER2 (13% vs. 4.6%), SPARC (34% vs. 15%), TOP2A (86% vs. 67%), and TOPO1 (55% vs. 46%) in esophageal and gastric adenocarcinoma cases respectively (P < 0.05). Conclusion: A comprehensive multiplatform biomarker analysis suggested significant biomarker differences between gastric and esophageal cancers. These results can assist in the development of future clinical trials.
Localization and object recognition for mobile robots

lthough new techniques that enable robots with advanced cognitive capabilities are being developed, still few work is being devoted to a difficult problem in which all this techniques rely: fast and robust perception methods to detect semantically relevant elements in unstructured environments, Indeed, being able to identify the robot's location and what objects lie around constitute the foundations on which almost all high-level reasoning processes conducted by a robot will build up. In order to help reduce this gap, this work addresses the problems of vision-based global localization and object recognition. The first contributions presented are a new technique to construct signatures of places to be used as nodes of a topological map from constellations of features detected in panoramic images, and a homing method to travel between such nodes that does not rely in artificial landmarks. Both methods were tested with several datasets showing very good results. General object recognition in mobile robots is of primary importance in order to enhance the representation of the environment that robots will use for their reasoning processes. Therefore, the next contributions of the thesis address this problem. After carefully reviewing recent Computer Vision literature on this topic, two state of the art object recognition methods were selected: The SIFT Object Recognition method and the Vocabulary Tree method. After evaluating both methods in challenging datasets, focusing on issues relevant to mobile robotics, it was found that, although the SIFT method was more suited for mobile robotics, both had complementary properties. To take advantage of this complementarity, the final contribution of this thesis is a Reinforcement Learning method to select online which object recognition method is best for an input image based on simple to compute image features. This method has been validated in a challenging object recognition experiment, even improving the performance of a human expert in some cases.
Limited Intersystem Information Exchange Method for Heterogeneous Networks

We consider a set of small cells deployed over a macrocell. To coexist under the same spectrum, the two systems must cooperate, so that one system can adapt to the other. In this letter, we assume that the macrocell has accurate channel information (about its channel and the cross-tier channels), which is quantized and then forwarded to the small cells. The aim is to minimize the required cross-tier information exchange rate and, at the same time, guarantee that the macrocell performance is not affected by the small cells. If the information exchange rate is adjusted as a function of the average SNR to achieve a BER target, then the required rate increases with the log of the average SNR. Therefore, we propose two adaptive approaches, where the exchange rate adapts to the instantaneous channel conditions, and show that the requirements can be exponentially reduced, i.e., only a log[log[. ]] increase with the SNR is enough.
Validity of the Third Molar Age Estimation from Different Dental Age Estimation Surveys for Malays and Chinese in Malaysia- A Pilot Study

This study aims to determine which age assessment data using the third molar development values (local or international) is suitable for estimating the age of Malays or Chinese in Malaysia. A sample of 60 panoramic images of Malays and Chinese aged between 13.58 to 21.25 years were selected. Different assessment surveys which included the studies by Yusof et al. (2015), Wilson (2005), Johan et al. (2012), Mincer et al. (1993), AlQahtani et al. (2010) and Gunst et al. (2003) were employed to estimate the age from the developing third molar on the panoramic images studied. The estimated ages were compared to the chronological age of the selected Malaysians. All the datas were then recorded on Microsoft Excel sheet. The two observers were then subjected to the Intraclass Correlation Coeffecient (ICC) inter-observer reliability test.The highest number of correspondence (65%) between the chronological and estimated age (within one year) was for the survey conducted by Wilson. With regards to ethnicities, 70% of Chinese matched the mean estimated age by Wilson while Malays showed a high correspondence for the study by Mincer et al. (63.3%). Furthermore the ICC reliability test showed strong agreement between the two observers. There were similarities between the Malay and Chinese population in the correspondence of the estimated age to the chronological age employing the different dental estimation surveys; in addition the study by Wilson and Mincer et al. yielded best matching for these Malaysians.
A mathematical approach towards hardware design

Today the hardware for embedded systems is often specified in VHDL. However, VHDL describes the system at a rather low level, which is cumbersome and may lead to design faults in large real life applications. There is a need of higher level abstraction mechanisms. In the embedded systems group of the University of Twente we are working on systematic and transformational methods to design hardware architectures, both multi core and single core. The main line in this approach is to start with a straightforward (often mathematical) specification of the problem. The next step is to find some adequate transformations on this specification, in particular to find specific optimizations, to be able to distribute the application over different cores. The result of these transformations is then translated into the functional programming language Haskell since Haskell is close to mathematics and such a translation often is straightforward. Besides, the Haskell code is executable, so one immediately has a simulation of the intended system. Next, the resulting Haskell specification is given to a compiler, called CeaSH (for CAES LAnguage for Synchronous Hardware) which translates the specification into VHDL. The resulting VHDL is synthesizable, so from there on standard VHDL-tooling can be used for synthesis. In this work we primarily focus on streaming applications: i.e. applications that can be modeled as data-flow graphs. At the moment the CeaSH system is ready in prototype form and in the presentation we will give several examples of how it can be used. In these examples it will be shown that the specification code is clear and concise. Furthermore, it is possible to use powerful abstraction mechanisms, such as polymorphism, higher order functions, pattern matching, lambda abstraction, partial application. These features allow a designer to describe circuits in a more natural and concise way than possible with the language elements found in the traditional hardware description languages. In addition we will give some examples of transformations that are possible in a mathematical specification, and which do not suffer from the problems encountered in, e.g., automatic parallelization of nested for-loops in C-programs.
Tactical big data analytics: challenges, use cases, and solutions

We discuss tactical challenges of the Big Data analytics regarding the underlying data, application space, and com- puting environment, and present a comprehensive solution framework motivated by the relevant tactical use cases. First, we summarize the unique characteristics of the Big Data problem in the Department of Defense (DoD) context and underline the main differences from the commercial Big Data problems. Then, we introduce two use cases, (i) Big Data analytics with multi-intelligence (multi-INT) sensor data and (ii) man-machine crowdsourcing using MapReduce framework. For these two use cases, we introduce Big Data analytics and cloud computing solutions in a coherent frame- work that supports tactical data, application, and computing needs.
Should Alvarado and Ohmann scores be real indicators for diagnosis of appendicitis and severity of inflammation

BACKGROUND: Acute appendicitis is one of the most common causes of abdominal pain seen in surgical clinics. Although it can be easily diagnosed, the picture may be confusing, particularly in premenopausal women and the elderly. The present study is an evaluation of 2 of the current scoring systems with respect to accurate diagnosis of the disease and indication of inflammation severity. METHODS: A total of 105 patients diagnosed with acute appendicitis were included in the study. Subsequent to Alvarado and Ohmann scoring, ultrasonography image was obtained and appendectomy was performed. A unique intraoperative severity scoring system was used to measure severity of inflammation and to compare Alvarado and Ohmann scoring system results to assess accuracy of predictive value for acute appendicitis. RESULTS: Moderate positive correlation was found between Alvarado score and Ohmann score (r=0.508; p<0.001). Rate of Alvarado score successfully predicting diagnosis of acute appendicitis based on histopathological results was statistically significant (p=0.027), while rate of Ohmann score was not statistically significant (p=0.807). Correlation between both scores and grading of inflammation performed during the operation was weak, but statistical significance was observed between Alvarado scoring system and intraoperative severity scoring (r=0.30; p=0.002). No statistical difference was observed between Ohmann scoring and intraoperative severity scoring (r=0.09; p=0.384). CONCLUSION: Alvarado score is better able to predict acute appendicitis and provide an idea of severity of inflammation. Ohmann score is more useful to provide guidance and eliminate acute appendicitis from consideration when conditions are more uncertain and obscured.
3D localization method for RFID tags by using mobile readers and virtual reference tags

Radio Frequency IDentification (RFID) is an identification technique for storing and transferring data and it is applied for identifying and tracking tags. Our study focus on RFID tags localization by using only mobile readers without the inclusion of static readers, helps to avoid pre-deploying of static readers. We are based only on connectivity information for achieving the goal of robust 3D localization method. The benefits of this work are: an optimized system cost, a scalable system with no need of further system maintenance. Simulation results will show satisfactory localization accuracy.
Ultra-high-energy cosmic rays from black hole jets of radio galaxies

The Auger Collaboration reports (Auger Collaboration 2007 Science 318 939, The Pierre Auger Collaboration 2008 Astropart. Phys. 29 188) that the arrival directions of ≳60 EeV ultra-high-energy cosmic rays (UHECRs) cluster along the supergalactic plane and correlate with active galactic nuclei (AGN) within ≈ 100 Mpc. The association of several events with the nearby radio galaxy Centaurus A supports the paradigm that UHECRs are powered by supermassive black-hole engines and accelerated to ultra-high energies in the shocks formed by variable plasma winds in the inner jets of radio galaxies. The GZK horizon length of 75 EeV UHECR protons is ≈100 Mpc, so that the Auger results are consistent with an assumed proton composition of the UHECRs. In this scenario, the sources of UHECRs are FR II radio galaxies and FR I galaxies like Cen A with scattered radiation fields that enhance UHECR neutral-beam production. Radio galaxies with jets pointed away from us can still be observed as UHECR sources due to deflection of UHECRs by magnetic fields in the radio lobes of these galaxies. A broadband ∼1 MeV–10 EeV radiation component in the spectra of blazar AGN is formed by UHECR-induced cascade radiation in the extragalactic background light. This emission is too faint to be seen from Cen A, but could be detected from more luminous blazars.
Should 2016 Criteria for Macrophage Activation Syndrome be applied in children with Kawasaki disease, as well as with systemic-onset juvenile idiopathic arthritis?

We read with deep interest the paper by Ravelli et al, 1 who developed new criteria for the classification of macrophage activation syndrome (MAS) in patients with systemic-onset juvenile idiopathic arthritis (So-JIA). The hemophagocytic lymphohistiocytosis (HLH) diagnostic guidelines2 have been successfully used in the management of primary HLH, but there have been several limitations to their use in secondary HLH or MAS complicating systemic inflammatory disorders including So-JIA.3 ,4 In practice, the new …
Semi-automatic SQL Debugging and Fixing to solve the Missing-Answers Problem

Asking questions is the driving force for scientific progress. But as important as it is to ask questions, so important is to be able to understand the obtained answers. In this way, we are able to verify the sanity of the question itself and if necessary refine it. In the same spirit, in this PhD we focus on SQL queries over re-lational databases with the aim to (1) pinpoint the reasons on the SQL query that led to missing-answers (tuples that were expected but not obtained in the query result), (2) fix the SQL query so that it best fits the user's expectations, and (3) do both efficiently so as to be of practical use.
Modified-Chitosan/siRNA Nanoparticles Downregulate Cellular CDX2 Expression and Cross the Gastric Mucus Barrier

Development of effective non-viral vectors is of crucial importance in the implementation of RNA interference in clinical routine. The localized delivery of siRNAs to the gastrointestinal mucosa is highly desired but faces specific problems such as the stability in gastric acidity conditions and the presence of the mucus barrier. CDX2 is a transcription factor critical for intestinal differentiation being involved in the initiation and maintenance of gastrointestinal diseases. Specifically, it is the trigger of gastric intestinal metaplasia which is a precursor lesion of gastric cancer. Its expression is also altered in colorectal cancer, where it may constitute a lineage-survival oncogene. Our main objective was to develop a nanoparticle-delivery system of siRNA targeting CDX2 using modified chitosan as a vector. CDX2 expression was assessed in gastric carcinoma cell lines and nanoparticles behaviour in gastrointestinal mucus was tested in mouse explants. We show that imidazole-modified chitosan and trimethylchitosan/siRNA nanoparticles are able to downregulate CDX2 expression and overpass the gastric mucus layer but not colonic mucus. This system might constitute a potential therapeutic approach to treat CDX2-dependent gastric lesions.
Carbon balance and productivity of a bio-energy culture with fast-growing poplars in Flanders

In April 2010, a large-scale short rotation coppice (SRC) plantation was established with mainly poplar (Populus spp.) on a former agricultural site (cropland and pasture) in Flanders. The twelve selected genotypes planted were assessed on establishment and production characteristics during the first two years of growth and were found highly productive, with a volume index ranging between 1.00 (± 0.68) and 1.93 (± 0.97) dm3 in growing season 1 (GS1) and between 2.75 (± 1.70) and 11.91 (± 6.33) dm3 in growing season 2 (GS2). Despite high survival rates of the cuttings after planting, competitive weeds and management operations increased tree mortality during the growing season from 3.4% up to 18.2% averaged over the entire plantation. Weed control therefore turned out to be the key factor in the establishment success. Only a minor influence of former land use was observed during GS1, which is explained by the non-limiting nutrient conditions on both former cropland and pasture, and which disappeared during GS2. These productive soils also explained the high growth rates, with an average tree height of 247 cm and 445 cm and stem diameter (at 22 cm height) of 25.21 mm and 40.73 mm after GS1 and GS2 respectively. Genotypic and parentage variations were found to be less pronounced during GS1, and increased during GS2 as expected. The maximum leaf area index and total leaf area duration were shown to be good indicators of production and growth performance. The results of this paper confirm the high potential of SRC with poplar on agricultural land for bio-energy purposes.
Multicriteria Image Thresholding Based on Multiobjective Particle Swarm Optimization

This paper deals with using the MultiObjective Particle Swarm Optimization (MOPSO) [5] metaheuristic for optimally thresholding medical images. Two criteria are optimized: the interclass variance) and the Shannon entropy. The process generates a Pareto front with various segmentations, leaving the final choice to the user. The paper will, first, describe MOPSO algorithm and then, focus on obtained results.
Integrating social, economic, and ecological values across large landscapes

Halofsky, Jessica E. ; Creutzburg, Megan K. ; Hemstrom, Miles A. , eds. 2014. Integrating social, economic, and ecological values across large landscapes. Gen.Tech. Rep. PNW-GTR-896. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 206 p. The Integrated Landscape Assessment Project (ILAP) was a multiyear effort to produce information, maps, and models to help land managers, policymakers, and others conduct midto broad-scale (e.g., watersheds to states and larger areas) prioritization of land management actions, perform landscape assessments, and estimate cumulative effects of management actions for planning and other purposes. The ILAP provided complete cross-ownership geospatial data and maps on current vegetation, potential vegetation, land ownership and management allocation classes, and other landscape attributes across Arizona, New Mexico, Oregon, and Washington. State-and-transition models, developed to cover all major upland vegetation types in the four states, integrated vegetation development, management actions, and natural disturbances to allow users to examine the midand long-term effects of alternative management and disturbance scenarios. New model linkages to wildlife habitat, economics, aboveground carbon pools, biomass, and wildfire hazard were developed and integrated through decision-support systems. Models incorporating potential effects of climate change were also developed for focus areas in Oregon and Arizona. This report includes an overview of the structure and components of ILAP along with descriptions of methods and example results for state-and-transition modeling, fuel characterization, treatment economics, wildlife habitat, community economics, and climate change. This report serves as a guide to ILAP. Complete collections of the project’s models, maps, data, and tools will be archived and available online through the Western Landscapes Explorer portal (www.westernlandscapesexplorer.info) so that scientists and managers will be able to use and build upon ILAP’s products.
A Pixel-Based Skin Segmentation in Psoriasis Images Using Committee of Machine Learning Classifiers

Skin segmentation, which involves detecting human skin areas in an image, is an important process for skin disease analysis. The aim of this paper is to identify the skin regions in a newly collected set of psoriasis images. For this purpose, we present a committee of machine learning (ML) classifiers. A psoriasis training set is first collected by using pixel values in five different color spaces. Experiments are then performed to investigate the impact of both the size of the training set and the number of features per pixel, on the performance of each skin classifier. A committee of classifiers is constructed by combining the classification results obtained from seven distinct skin classifiers using majority voting. Also, we propose a refinement method using morphological operations to improve the resulted skin map. We use a set of 100 psoriasis images for training and testing. For comparative evaluation, we consider 3283 face skin images. Finally, F-measure and accuracy are used to evaluate the performance of the classifiers. The experimental results show that the size of the training set does not greatly influence the overall performance. The results also indicate that the feature vector using pixel values in the five color spaces has higher performance than any subset of these spaces. Comparative study suggests that the proposed method performs reasonably with both psoriasis and faces skin images, with accuracy of 97.4% and 80.41% respectively.