Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite element formulation of coupled grain-boundary and surface diffusion with grain-boundary migration

Finite element formulations are developed to model microstructure evolution by a combination of grain–boundary diffusion, grain–boundary migration and free surface diffusion. The formulations are based on a unified variational principle which allows fully coupled processes to be analysed. For example, a process can be analysed which involves grain–boundary diffusion along a curved and migrating grain–boundary network, coupled with surface diffusion along internal and/or external free surfaces which intersect with the grain–boundary network. The numerical solution provides the velocities of each individual grain and the velocities of grain–boundaries and migrating surfaces. The finite element formulations, when combined with a time integration algorithm, form a numerical technique which can be used to simulate microstructural evolution in polycrystalline materials. The technique can be applied to a wide range of physical problems including: sintering of powder compacts; grain–growth; diffusive void growth and crack propagation; superplastic deformation and the morphological evolution of electronic thin films. Various numerical examples are presented to demonstrate the effectiveness of the numerical technique.
Aquarius and the Aquarius/SAC-D mission

Aquarius is a combination L-band radiometer and scatterometer designed to map the salinity field at the ocean surface from space. It will be flown on the Aquarius/SAC-D mission, a partnership between the USA space agency (NASA) and Argentine space agency (CONAE). The mission is composed of two parts: (a) The Aquarius instrument being developed as part of NASA's Earth System Science Pathfinder (ESSP) program; and (b) SAC-D the fourth spacecraft service platform in the CONAE Satelite de Aplicaciones Cientificas (SAC) program. The primary focus of the mission is to monitor the seasonal and interannual variations of the salinity field in the open ocean. The mission also meets the needs of the Argentine space program for monitoring the environment and for hazard detection and includes several instruments related to these goals.
A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features

The naive Bayes is one of the useful classification techniques in data mining and machine learning. Although naive Bayes learners are efficient, they suffer from the weak assumption of conditional independence between the attributes. Many algorithms have been proposed to improve the effectiveness of naive Bayes classifier by inserting discriminant approaches into its generative structure. Combining generative and discriminative viewpoints is done in many algorithms e.g. by use of attribute weighting, instance weighting or ensemble method. In this paper, a new ensemble of Gaussian naive Bayes classifiers is proposed based on the mixture of Gaussian distributions formed on less conditional dependent features extracted by local PCA. A semi-AdaBoost approach is used for dynamic adaptation of distributions considering misclassified instances. The proposed method has been evaluated and compared with the related work on 12 UCI machine learning datasets and achievements show significant improvement on the performance.
Reusability of a corpus and a treebank to enrich verb subcategorisation in a dictionary

This paper deals with the reusability of a corpus and a treebank to enrich verb subcategorisation in a static resource, a dictionary. Two experiments have been performed to propose: a) new subcategorisation information for verb entries included in the dictionary, and b) new verb entries. For the verb subcategorisation enrichment, inconsistencies between the information obtained from the corpus and the dictionary were found by means of a tool called Saroi. The same tool is used to propose new entries. A verb is proposed for its inclusion in the dictionary if it is found in the corpus but not in the dictionary, and it also appears in the treebank.
The influence of reduced light intensity on the response of benthic diatoms to herbicide exposure

Herbicide pollution events in aquatic ecosystems often coincide with increased turbidity and reduced light intensity. It is therefore important to determine whether reduced light intensity can influence herbicide toxicity, especially to primary producers such as benthic diatoms. Benthic diatoms collected from 4 rivers were exposed to herbicides in 48 h rapid toxicity tests under high light (100 µmol m−2 s−1) and low light (20 µmol m−2 s−1) intensities. The effects of 2 herbicides (atrazine and glyphosate) were assessed on 26 freshwater benthic diatom taxa. There was no significant interaction of light and herbicide effects at the community level or on the majority (22 of 26) of benthic diatom taxa. This indicates that low light levels will likely have only a minor influence on the response of benthic diatoms to herbicides. Environ Toxicol Chem 2016;35:2252–2260. © 2016 SETAC
Monoclonal antibody‐based therapeutics, targeting the epidermal growth factor receptor family: from herceptin to Pan HER

Monoclonal antibody‐based of cancer therapy has been considered as one of the most successful therapeutic strategies for both haematologic malignancies and solid tumours in the last two decades. Epidermal growth factor receptor (EGFR) family signalling pathways play a key role in the regulation of cell proliferation, survival and differentiation. Hence, anti‐EGFR family mAbs is one of the most promising approaches in cancer therapy.
An intervention detection framework for collaborative projects

Capstone projects are increasingly being used in Higher Education to emulate real world work environments and to provide valuable experiential learning. There these projects are run within a live commercial environment and with the close cooperation of a client mentor, they have proven to provide enhanced benefits to students. In such cases a vast amount of planning and cajoling may have gone into securing the cooperation of live clients and tutors are therefore eager to ensure that the projects they have provided are successfully executed in order to secure the cooperation of the client organisation in future years. With this in mind, this paper describes the proposal for a framework to monitor and detect dysfunctional behaviour within project groups using CSCL (Computer Supported Collaborative Learning), in order to allow timely interventions to be made to correct the collaboration process. The proposed intervention detection framework comprises of four dimensions: 1) individual temporal dynamic, 2) group dynamic, 3) sentiment analysis of discussions, and 4) analysis of content volume.
Innate and adaptive type 2 immunity in lung allergic inflammation

Allergic inflammation is a type 2 immune disorder classically characterized by high levels of immunoglobulin E (IgE) and the development of Th2 cells. Asthma is a pulmonary allergic inflammatory disease resulting in bronchial hyper‐reactivity. Atopic asthma is defined by IgE antibody‐mediated mast cell degranulation, while in non‐atopic asthma there is no allergen‐specific IgE and more involvement of innate immune cells, such as basophils, group 2 innate lymphoid cells (ILC2), and eosinophils. Recently, protease allergens were shown to cause asthmatic responses in the absence of Th2 cells, suggesting that an innate cell network (IL‐33/TSLP‐basophil‐ILC2‐IL‐5/IL‐13 axis) can facilitate the sensitization phase of type 2 inflammatory responses. Recent evidence also indicates that in the chronic phase, these innate immune cells directly or indirectly contribute to the adaptive Th2 cell responses. In this review, we discuss the role of Th2 cytokines (IL‐4 and IL‐13) and innate immune cells (mast cells, basophils, ILC2s, and dendritic cells) in the cross‐talk between innate and adaptive inflammatory responses.
Comparison of semantic vectors with reduced precision using the cosine similarity measure

This paper presents an analysis of an impact of a precision reduction on a performance of the cosine similarity measure in a document comparison task. The precision reduction of semantic vectors allows for a substantial computing performance improvement at the expense of a negligible decline of a comparison quality. In order to take an advantage of the precision reduction in terms of a lower number of bits, a dedicated hardware platforms are essential. Consequently, we proposed an FPGA-based hardware solution and examined its performance. In order to validate the adopted method of the precision reduction we also created the quality assessment setup. This allowed us to determine that it is possible to decrease a vector precision down to 8 bits and still maintain 0.99 correlation of the regular and reduced results. This is feasible for a wide range of data set sizes.
An online fuzzy model for classification of data streams with drift

In this paper, an adaptive fuzzy classifier for online rule learning from real-time data streams is proposed. These kinds of data have some limitations which make them different from batch datasets and therefore the process of learning is confronted with many challenges. Since concept drift is one of the most important challenge among them, different techniques as well as our proposed method focus on solving this issue. Our method sequentially updates the constructed model such that the structure and parameters always remains compatible with any new characteristics of data. For having low computational time of modifying the model, we propose a simple updating formula based on minimizing the classification accuracy in each step through gradient descent. The proposed method achieves results that are better than other fuzzy and non-fuzzy methods.
Effect of fiber length on Young's modulus of paper

Effect of fiber length on the elastic modulus of the random sheet was investigated. According to the finite-fiber-length model, the elastic property of the sheet in a lower sheet density range obviously depends on length of the composed fibers. However, a plot of the experimental values of the elastic modulus of the sheets made of fibers with different average length against sheet density did not support the tendency predicted by the model. Fiber length was found not to be a crucial factor affecting the elastic modulus. The shape of fibers in a sheet is neither vertically nor horizontally straight, because they are tightly packed in the sheet with many fibers. The results obtained in this study may suggest limitations of the finite-fiber-length model in application to an actual paper sheet.
Representation, Analysis, and Recognition of 3D Humans

Computer Vision and Multimedia solutions are now offering an increasing number of applications ready for use by end users in everyday life. Many of these applications are centered for detection, representation, and analysis of face and body. Methods based on 2D images and videos are the most widespread, but there is a recent trend that successfully extends the study to 3D human data as acquired by a new generation of 3D acquisition devices. Based on these premises, in this survey, we provide an overview on the newly designed techniques that exploit 3D human data and also prospect the most promising current and future research directions. In particular, we first propose a taxonomy of the representation methods, distinguishing between spatial and temporal modeling of the data. Then, we focus on the analysis and recognition of 3D humans from 3D static and dynamic data, considering many applications for body and face.
Lower Patient Ratings of Physician Communication Are Associated With Unmet Need for Symptom Management in Patients With Lung and Colorectal Cancer.

PURPOSE Little is known about factors associated with unmet needs for symptom management in patients with cancer. METHODS Patients with a new diagnosis of lung and colorectal cancer from the diverse nationally representative Cancer Care Outcomes Research and Surveillance cohort completed a survey approximately 5 months after diagnosis (N = 5,422). We estimated the prevalence of unmet need for symptom management, defined as patients who report that they wanted help for at least one common symptom (pain, fatigue, depression, nausea/vomiting, cough, dyspnea, diarrhea) during the 4 weeks before the survey but did not receive it. We identified patient factors associated with unmet need by using logistic regression with random effects to account for clustering within study sites. RESULTS Overall, 15% (791 of 5,422) of patients had at least one unmet need for symptom management. Adjusting for sociodemographic and clinical factors, African American race, being uninsured or poor, having early-stage lung cancer, and the presence of moderate to severe symptoms were associated with unmet need (all P < .05). Furthermore, patients who rated their physician's communication score < 80 (on a 0 to 100 scale) had adjusted rates of an unmet need for symptom management that were more than twice as high as patients who rated their physicians with a perfect communication score (23.1% v 10.0%; P < .001). CONCLUSION A significant minority of patients with newly diagnosed lung and colorectal cancer report unmet needs for symptom management. Interventions to improve symptom management should consider the importance of physician communication to the patient's experience of disease.
Synthesis And Characterization Of Nickel Cobalt Ferrite Nanoparticles Via Heat Treatment Method

Material synthesis is a scientific art, considering all parameters and conditions in practical view and applying them in an experiment. Properties of synthesized material can be tuned further by technically playing with concerned parameters individually. In this work, a one to one relation between the formation of cubic spinel ferrite structure with stable phase and morphology has been established, and corresponding changes in the magnetic properties are investigated with temperature. Heat treatment method is adopted for the preparation of nanoparticles of cobalt doped nickel ferrite Ni1-xCoxFe2O4 (x=0.5) and characterized by XRD, FESEM, TGA and VSM. Initially, as synthesized powder sample shows amorphous and weak ferromagnetic nature (Ms = 0.04 emu/g, Hc = 92.42 Oe), but heat treatment at 400oC exhibits signature of phase formation with irregular particle shape and ferromagnetic (Ms = 6.62emu/g, Hc = 460 Oe) behaviour. On further heating upto 600oC, the nanoparticles of Ni1-xCoxFe2O4 (x=0.5) is formed with stable cubic spinel crystal structure (lattice constant a=8.35452Å) and enhanced magnetic properties (Ms=8.24emu/g, Hc=1955Oe). Due to higher coercivity obtained of Ni1-xCoxFe2O4 (x=0.5) nanoparticles, it may be useful material in the production of data storage devices, permanent magnet, parts of electronic circuits and also in stealth technology. Copyright © 2016 VBRI Press
Sentence matrix normalization using most likely n-grams vector

Word embeddings is one of the interesting natural language processing filed and has been shown to be a great asset for a large variety of NLP tasks. N-gram language models are also an important text processing methods and are based on statistics of how likely words are to follow each other. A big problem in all text processing approaches based on word vectors are different size of sentences matrices. In most of suggested approaches, the average value of columns is calculated and a one dimensional vector including N elements is created. But the approach has some disadvantages such as its resistance against word ordering, ignoring sentence length feature and their vector sentences are very close to each other. We introduce an efficient and very simple rich statistical model of Word2Vec approach and n-grams language model to assess unique size sentence matrices. The unique size resulting matrix does not depend on the language and its semantic concepts. Our results demonstrate that certain models capture complementary aspects of coherence evaluation, text summarization, automatic essay scoring, detecting fake and copied texts, text topic comparison and thus can be combined to improve performance.
Role of the Endocannabinoid System in Anxiety and Stress-Related Disorders

Post-traumatic stress disorder (PTSD) is an anxiety disorder that may develop after exposure to a life-threatening traumatic experience and according to the DSM-IV diagnostic criteria, involves characteristic features such as persistent experiencing of trauma, avoidance, numbing, and hyperarousal (DSM-IV, 1994). Although many individuals with PTSD recover during the first couple of years following traumatic exposure, up to 30–40% remain chronically symptomatic (Kessler et al., 1995). Furthermore, individuals with chronic PTSD were shown to maintain conditioned fear responses to traumatic stimuli even 40–50 years after the trauma (Orr et al., 1993). The largest body of treatment literature exists for antidepressants, particularly selective serotonin reuptake inhibitors (SSRIs). Extensive clinical experience with these agents and their efficacy for disorders that are commonly comorbid with PTSD means that their use will likely continue until better treatment options are found (Davidson, 2000; Ravindran & Stein, 2009) even though their efficacy and that of other pharmacological treatments is generally limited. The high prevalence, chronicity, and resistance to treatment underscore the importance of the development of effective therapeutic strategies for PTSD. The endocannabinoid (eCB) system has arisen as part of the complex circuitry that regulates anxiety and as a crucial mediator of emotional learning (Marsicano et al., 2002; Viveros et al., 2005; Laviolette & Grace, 2006a,2006b; Gorzalka et al., 2008; Ganon-Elazar & Akirav, 2009; Lutz, 2009; Hill & Gorzalka, 2009; Parolaro et al., 2010). The idea that the eCB system is involved in the control of anxiety-like behaviour is rooted in the fact that Cannabis sativa is used recreationally, mainly for its euphoric effects. However, this picture is confounded by the fact that cannabis abusers sometimes experience dysphoric reactions, with feelings of anxiety and panic. Additionally, it has been suggested that a temporary form of drug‐induced psychosis can occur in some cannabis users. This ‘marijuana psychosis’ is usually due to exposure to large doses of the drug (Iversen, 2003) and has similar symptoms to paranoid schizophrenia. This has led some to propose a ‘cannabinoid hypothesis of schizophrenia’, suggesting that the symptoms of schizophrenic illness might be caused by an abnormal over‐activity of endogenous cannabinoid mechanisms in the brain (Emrich et al., 1997). An interesting question in cannabis research is whether cannabis use can precipitate long‐term psychiatric illness. There are several reports suggesting that the development of cannabis dependence in young people is associated with increased rates of psychiatric symptoms, including psychosis, depression and anxiety (Andreasson et al., 1987; Mathers &
A Novel Technique for E. coli Testing of Beef and Pork Carcasses

In January 1997, the USDA-FSIS mandated Escherichia coli (EC) testing in all processing plants to monitor surface contamination of beef, pork, and poultry carcasses. A novel technique that uses membrane filtration (MF) and m-ColiBlue24 (mCB) to determine the amount of EC contamination on a carcass was developed. Sponges from pork carcasses and excision samples from beef carcasses were analyzed using mCB and compared with standard methods. Samples from 77 pork carcasses were plated both on mCB and pour plating on Violet Red Bile (VRB) agar to obtain Total Coliform (TC) counts. There was no significant difference between the counts. The mean values for mCB and VRB agar were 7.4 colony forming units (CFU)/15 cm2 and 6.1 CFU/15 cm2, respectively. The paired t-test revealed a t=0.52 and P=0.61. Excision samples from 57 spiked beef carcasses were used to compare mCB with both TC and EC PetrifilmTM. The mean TC count on mCB was 1.6 x 104 CFU/cm2 and 9.3 x 103 CFU/cm2 on TC Petrifilm. The paired t-test gave a t=2.4 and P=0.02. The mean EC count on mCB was 9.3 x 103 CFU/cm2 and 3.2 x 103 CFU/cm2 on EC Petrifilm. The t=3.5 and P<0.01. The new technique that uses mCB detected more TC and EC than both types of Petrifilm. Furthermore, the t values suggest that there is a significant treatment effect.
Humanization of rabbit monoclonal antibodies via grafting combined Kabat/IMGT/Paratome complementarity-determining regions: Rationale and examples

ABSTRACT Rabbit monoclonal antibodies (RabMAbs) can recognize diverse epitopes, including those poorly immunogenic in mice and humans. However, there have been only a few reports on RabMAb humanization, an important antibody engineering step usually done before clinical applications are investigated. To pursue a general method for humanization of RabMAbs, we analyzed the complex structures of 5 RabMAbs with their antigens currently available in the Protein Data Bank, and identified antigen-contacting residues on the rabbit Fv within the 6 Angstrom distance to its antigen. We also analyzed the supporting residues for antigen-contacting residues on the same heavy or light chain. We identified “HV4” and “LV4” in rabbit Fvs, non-complementarity-determining region (CDR) loops that are structurally close to the antigen and located in framework 3 of the heavy chain and light chain, respectively. Based on our structural and sequence analysis, we designed a humanization strategy by grafting the combined Kabat/IMGT/Paratome CDRs, which cover most antigen-contacting residues, into a human germline framework sequence. Using this strategy, we humanized 4 RabMAbs that recognize poorly immunogenic epitopes in the cancer target mesothelin. Three of the 4 humanized rabbit Fvs have similar or improved functional binding affinity for mesothelin-expressing cells. Interestingly, 4 immunotoxins composed of the humanized scFvs fused to a clinically used fragment of Pseudomonas exotoxin (PE38) showed stronger cytotoxicity against tumor cells than the immunotoxins derived from their original rabbit scFvs. Our data suggest that grafting the combined Kabat/IMGT/Paratome CDRs to a stable human germline framework can be a general approach to humanize RabMAbs.