Miracle-dz/UniFAR-FTUs huggingface dataset:
(Z)-2-(5-Fluoro-2-oxoindolin-3-yl­idene)-N-methyl­hydrazinecarbo­thio­amide

In the title compound, C10H9FN4OS, which is approximately planar with a maximum deviation of 0.0881 (10) A from the mean plane of the non-H atoms, an intra­molecular N—H⋯O hydrogen bond generates an S(6) ring. In the crystal, mol­ecules are linked via N—H⋯O hydrogen bonds, forming a helical chain along the a axis. The chains are linked by N—H⋯S, C—H⋯S and C—H⋯F hydrogen bonds and C—H⋯ π inter­actions into a three-dimensional network.
Role of leukocytes, complement system and endothelium in rat renal ischaemia-reperfusion injury

Introduction: Renal Ischaemia-Reperfusion injury (IRI) is a complex mechanism involving the interplay between endothelium, leukocytes and the complement system. To evaluate the role of these three key mediators, a rat model was used to evaluate changes seen in renal IRI. Two interventional agents: Anti-Thymocyte Globulin (ATG) and Soluble Complement Receptor-1 (sCR1) were used to modulate leukocyte and complement response in this IRI model with a view to assess, and define IRI mechanism. Methods: The Ischaemia-Reperfusion (IR) model involved unilateral left renal ischemia (n=10) for 40 minutes, followed by 48 hours of reperfusion. ATG (n=8), ATG Isotype (n=8) and sCR1(n=8) were given IV prior to the laparotomy followed by IR model. The sham group (n=6) served as controls. Blood CD3 lymphocyte counts and CH50 complement assay were used to check efficacy of ATG and sCR1 respectively. The kidneys retrieved at 48 hours were analysed for histology, immunohistochemistry and RT-qPCR studies. Results: The IR group showed significant injury compared to the sham group. ATG treatment offered significant histological protection mainly via decreased leukocyte infiltrate and endothelial protection compared to the IR and Isotype controls. CH50 assay showed complete ablation of complement activity at the time of reperfusion, with return to normality at 24 hours. sCR1 treatment conferred protection from IRI predominantly via suppression of the complement cascade (C3, C9), reduced leukocyte infiltrates and V endothelial protection. RT-qPCR showed down-regulation of injury molecules – KIM-1 and NGAL in both the intervention groups. Conclusion: Modulation of leukocytes and complement system using single dose ATG and sCR1 led to significant endothelial protection, resulting in amelioration of renal ischaemia-reperfusion injury. The complement system was ablated at the time of reperfusion and was reconstituted by 24 hours, thus indicating that suppression of complement system during the phase of IR provides an avenue for mitigating IRI.
Effect of Oral Saccharomyces Cerevisiae Hansen CBS 5926 Therapy on Gastrointestinal Symptoms and Nutrition in Gastrectomized Patients

Purpose: We aimed to investigate the effect of oral Saccharomyces cerevisiae Hansen CBS 5926 therapy (Saccharomyces boulardii) on gastrointestinal symptoms and nutrition in gastrectomized patients. Materials and Methods: Ninety patients who underwent a gastrectomy due to early gastric cancer were randomly divided into case and control (placebo) groups and were given medicine or placebos for 12 weeks. The gastrointestinal symptom rating and the nutritional status were evaluated by using double blind methods at 4, 16, and 20 weeks after discharge. Results: The ratio of males to females was 2.3 : 1, and the mean age was years. The numbers of subtotal gastrectomies and total gastrectomies were 69 and 21, respectively. Fourteen (15.6%) patients dropped, of whom 5 belonged to the case group. Of the remaining 76 patients, those in the case group had higher levels of serum albumin (P=0.046). For patients who underwent subtotal gastrectomy (n=57), the case group had higher level of serum albumin (P=0.049). Conclusion: This double blind study showed that oral Saccharomyces boulardii therapy could improve the postoperative nutrition of gastrectomized patients because gastrectomized patients with oral Saccharomyces boulardii therapy had higher levels of serum albumin than the control group.
Experimental Forecasts of the Indian Ocean Dipole Using a Coupled OAGCM

The Indian Ocean Dipole (IOD) has profound socioeconomic impacts on not only the countries surrounding the Indian Ocean but also various parts of the world. A forecast system is developed based on a relatively high-resolution coupled ocean–atmosphere GCM with only sea surface temperature (SST) information assimilated. Retrospective ensemble forecasts of the IOD index for the past two decades show skillful scores with up to a 3–4-month lead and a winter prediction barrier associated with its intrinsic strong seasonal phase locking. Prediction skills of the SST anomalies in both the eastern and western Indian Ocean are higher than those of the IOD index; this is because of the influences of ENSO, which is highly predictable. The model predicts the extreme positive IOD event in 1994 at a 2–3-season lead. The strong 1997 cold signal in the eastern pole, however, is not well predicted owing to errors in model initial subsurface conditions. The real-time forecast system with more ensembles successfully predicted the weak negative IOD event in the 2005 boreal fall and La Niña condition in the 2005/06 winter. Recent experimental real-time forecasts showed that a positive IOD event would appear in the 2006 summer and fall accompanied by a possible weak El Niño condition in the equatorial Pacific.
Data-driven audit with anomaly detection algorithms an explorative study about the application of unsupervised machine learning to detect exceptions in transaction level audit data

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
TheEffectiveness of Nursing Educational Intervention OnImproving Fatigue Levels Among Egyptian Patients with Chronic Hepatitis C Virus Undergoing Antiviral Therapy

Background:Fatigue is a common side effect of antiviral therapy that affects patients’ overall wellbeing. The aim of this study was to assess the effectiveness of a nursing intervention for reducing fatigue levels among Egyptian patients infected with hepatitis C virus and enrolled in an antiviral therapy program with dailySofosbuvir (SOF) 400mg and ribavirin (RBV)1000-1200mg. Design:Aquasi-experimental research design was conducted on a convenience sample of 100 patients complaining of fatigue. Measurements: Afatigue severity scale (FSS) measurement was used to assess fatigue levels in pre and post program implementation. Results: Among all subjects (n=100), the results indicated that 62 percent of participants reported having severe base line fatigue scores. The day after the intervention, 49 percent reported severe fatigue levels,15 and 14 percent reported severe fatigue levels at one and two months after the intervention respectively. Repeated measures (ANOVA) revealed significant correlations between fatigue levels and times of intervention as (F= 114.437& p=≤.001) with means and standard deviations as (M=36.00 &+SD=7.767), (M=32.02 &+SD=7.623), (M=21.13&+SD=9.168) & (M=15.82 &+SD=10.642) respectively. Conclusion: Patients` fatigue levels were high at the baseline. However, the nursing intervention was effective in reducing fatigue levels.
A Multinomial Probit Model with Latent Factors

We develop a parametrization of the multinomial probit model that yields greater insight into the underlying decision-making process, by decomposing the error terms of the utilities into latent factors and noise. The latent factors are identified without a measurement system, and they can be meaningfully linked to an economic model. We provide sufficient conditions that make this structure identified and interpretable. For inference, we design a Markov chain Monte Carlo sampler based on marginal data augmentation. A simulation exercise shows the good numerical performance of our sampler and reveals the practical importance of alternative identification restrictions. Our approach can generally be applied to any setting where researchers can specify a structure on a few drivers of unobserved heterogeneity a priori. One such example is the choice among combinations of two options, which we explore with real data on education and occupation pairs. JEL Classification: C11; C25; C35.
Learning Embeddings from Scientific Corpora using Lexical, Grammatical and Semantic Information

Natural language processing can assist scientists to leverage the increasing amount of information contained in scientific bibliography. The current trend, based on deep learning and embeddings, uses representations at the (sub)word level that require large amounts of training data and neural architectures with millions of parameters to learn successful language models, like BERT. However, these representations may not be well suited for the scientific domain, where it is common to find complex terms, e.g. multi-word, with a domain-specific meaning in a very specific context. In this paper we propose an approach based on a linguistic analysis of the corpus using a knowledge graph to learn representations that can unambiguously capture such terms and their meaning. We learn embeddings from different linguistic annotations on the text and evaluate them through a classification task over the SciGraph taxonomy, showing that our representations outperform (sub)word-level approaches.
Governance in Peer-to-Peer Networks Is a Design Problem (short paper)

Peer-to-peer networks are gaining momentum, and its revolution is accelerating. Examples of peer-to-peer systems are Bitcoin and Corda, which are enabled by blockchain technology. Even the firmest supporters of blockchain, acknowledge the most significant challenge for successful adoption and growth of peer-to-peer networks is governance in a decentralized way. A lack of governance design poses a risk, as the decentralized network will not prevail in the long term which can even lead to a centrally orchestrated system. In this paper, we argue that the finding an appropriate governance structure is a design problem.
COMPARISON OF FUZZY LOGIC, REGRESSION AND ANN LASER KERF WIDTH MODELS

This paper focuses on development and comparison of empirical models for the prediction of kerf width obtained in CO2 laser cutting of AISI 304 stainless steel using regression analysis, artificial neural network (ANN) and fuzzy logic. Laser cutting experiment, conducted according to Taguchi’s experimental design using L27 orthogonal array, and provided a set of data for model’s development. All three models considered the laser power, cutting speed, assist gas pressure and focus position as input parameters. Statistical values of the coefficient of determination and absolute percentage error were employed to compare the three developed models by considering initial experimental as well as additional experimental (validation) data. Analysis and results indicate that all three modeling approaches can be equally effectively used for the prediction of kerf width in CO2 laser cutting. However, fuzzy logic model showed the best overall prediction results, while developed ANN model best generalization capability.
Delft University of Technology Decreased pain and improved dynamic knee instability mediate the beneficial effect of wearing a soft knee brace on activity limitations in patients with knee osteoarthritis

Citation (APA) Cudejko, T. , van der Esch, M. , van den Noort, J. C. , Rijnhart, J. J. M. , van der Leeden, M. , Roorda, L. D. , . Dekker, J. (2019). Decreased pain and improved dynamic knee instability mediate the beneficial effect of wearing a soft knee brace on activity limitations in patients with knee osteoarthritis. Arthritis Care and Research, 71(8), 1036-1043. https://doi.org/10.1002/acr.23722
Multi-colony tracking reveals potential threats to little auks wintering in the North Atlantic from marine pollution and shrinking sea-ice cover

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Multi-colony tracking reveals potential threats to little auks wintering in the North Atlantic from marine pollution and shrinking sea-ice cover Jérôme Fort, Børge Moe, Hallvard Strøm, David Grémillet, Jorg Welcker, Jannik Schultner, Kurt Jerstad, Kasper Johansen, Richard A. Phillips, Anders Mosbech
PROGNOSIS OF BREAST CANCER IN VERY YOUNG AGE (LESS THAN 30 YEARS)

Purpose: Breast cancer diagnosed at a younger age has aggressive biology being triple negative and high grade and is associated with poor prognosis. Materials and Methods: Retrospectively data of 121 patients age 30 years or younger registered during the year 2008 were reviewed. Data were extracted from the cancer registry department of the institute. Demographics studied were the age at diagnosis, gender, pregnancy or lactation association, family history of breast cancer, histopathological diagnosis, and stage of the disease, receptors, type of treatment, response, local recurrence, distant relapse, and survival. Results: A total of 121 patients with age 30 years or less were included. An only a single patient was male. The age range was from 20 to 30 years; bilateral involvement was seen in a single patient. Almost half 50.4% ( n = 61) patients had locally advanced disease at presentation. Pregnancy/lactation-associated breast cancer was seen in 29.8% ( n = 36). The most common stage was Stage III (52.1%) and Stage II (33.9%). Invasive ductal carcinoma was the most common histology 94.2% ( n = 114) of patients; triple negative was the most common molecular subtype present in 46.3% ( n = 56). Chemotherapy was received by 92.6% ( n = 112), 88.4% ( n = 107) patients received radiation therapy. Modi ed radical mastectomy was performed in 57% ( n = 69), breast conservation surgery in 35.5% ( n = 43), follow- up period was 5 years, local recurrence was observed in 12.4% ( n = 15) and cancer related deaths were 42.1% ( n = 51). Conclusions: Breast cancer in very young has very aggressive tumour biology, needs aggressive treatment with surgery, chemotherapy, radiation therapy and hormonal therapy. Key words: Breast cancer, pregnancy-associated aggressive tumour biology, triplenegative, young
On Lattice-Based Interactive Protocols with Aborts

A canonical identiﬁcation (CID) scheme is a 3-move protocol consisting of a commitment, challenge, and response. It constitutes the core design of many cryptographic constructions such as zero-knowledge proof systems and various types of signature schemes. Unlike number-theoretic constructions, CID in the lattice setting usually forces provers to abort and repeat the whole authentication process once the distribution of the computed response does not follow a target distribution independent from the secret key. This concept has been realized by means of rejection sampling, which makes sure that the secrets involved in a protocol are concealed after a certain number of repetitions. This however has a negative impact on the eﬃciency of interactive protocols because it leads to a number of communication rounds that is multiplicative in the number of aborting participants (or rejection sampling procedures). In this work we show how the CID scheme underlying many lattice-based protocols can be designed with smaller number of aborts or even without aborts. Our new technique exploits (unbalanced) binary hash trees and thus signiﬁcantly reduces the communication complexity. We show how to apply this new method within interactive zero-knowledge proofs. We also present BLAZE + : a further application of our technique to the recently proposed lattice-based blind signature scheme BLAZE (FC20). We show that BLAZE + has an improved performance and communication complexity compared to BLAZE while preserving the size of signatures.
Assessing the classification accuracy of an evolutionary neural network : the case of plant virus identification

Genetic algorithms and neural networks are exciting technologies in the fields of artificial intelligence, machine learning and data mining. In this work, we propose an evolutionary method to produce time series meta-data for the training and testing of a neural network module used in plant virus identification. The initial dataset is derived via a prototype method developed by members of our research team, which uses specially designed sensors to monitor the virus reactions. The proposed method incorporates an evolutionary technique to manipulate the time series in order to effectively manipulate the dimensionality of the input data space. Furthermore, our method is tested against some of the most commonly used classifiers in machine learning and proves its potential towards assisting an identification which, up to now, is accomplished mainly empirically.
Adaptive Response Surface based FE Model Updating for Operational Modal Analysis of RC Road Bridge

This study attempts to examine the effectiveness of meta-modelling in finite element (FE) model updating problem. Natural frequencies evaluated from eigen analysis of the FE model are approximated individually by meta-models. The updating objective function is defined for residual error between the natural frequencies identified by operational modal analysis and from FE model. A comparative numerical study is performed for updating a reinforced concrete (RC) road bridge FE model. In meta-modelling framework, linear and quadratic polynomial based adaptive response surface method (ARSM) using least square (LS) and moving least square (MLS) regression techniques are presented. Different design of experiment (DOE) schemes (viz. Koshal, D–Optimal, Full Factorial and Central Composite Designs) for generating support points and weight functions (viz. exponential and regularized) are also considered. Results show the effectiveness of MLS based ARSM that bypasses multiple FE model runs as compared to the conventional gradient based optimization.
THE RELATIONSHIP BETWEEN THE PRE-SERVICE SCIENCE TEACHERS’ SELF-EFFICACY BELIEFS ABOUT SCIENCE TEACHING AND LABORATORY WORKS

The purpose of the present study is to determine the relationship between the pre-service science teachers’ perceptions of their self-efficacies in science teaching and laboratory works. The sampling of the study consists of 70 pre-service science teachers grade 4 that have studied in Science Teacher Education Department at Muğla University. Survey method was used in the present study. The pre-service teachers’ views of their proficiency in science laboratory works were determined through 18-item “Proficiency Determination Form” developed by Böyük, Demir and Erol (2010). The reliability of the scale was found to be.89. The pre-service teachers’ perceptions of “Science Teaching Self-Efficacy (STSE)”were determined by means of 23-item “Science Teaching Efficacy Belief Instrument (STEBIB)” developed by Riggs and Enochs (1990) and adopted to Turkish by Özkan, Tekkaya and Çakıroğlu (2002). According to the results of factor analysis, science teaching self-efficacy perception consists of two dimensions as “personal science teaching efficacy beliefs” and “science teaching outcome expectancy”. Alpha reliability coefficient of each dimension was found to be 0.86 and 0.79, respectively. The results of the analyses revealed that there is a significant correlation between science teachers’ perceptions’ of their self-efficacies in science teaching and science laboratory competencies.
Readability of Twitter Tweets for Second Language Learners

Optimal language acquisition via reading requires the learners to read slightly above their current language skill level. Identifying material at the right level is the essential role of automatic readability measurement. Short message platforms such as Twitter offer the opportunity for language practice while reading about current topics and engaging in conversation in small doses, and can be filtered according to linguistic criteria to suit the learner. In this research, we explore how readable tweets are for English language learners and which factors contribute to their readability. With participants from six language groups, we collected 14,659 data points, each representing a tweet from a pool of 4100 tweets, and a judgement of perceived readability. Traditional readability measures and features failed on the data-set, but demographic data showed that judgements were largely genuine and reflected reported language skill, which is consistent with other recent studies. We report on the properties of the data set and implications for future research.
MRS in cancer

In recent years there has been an immense development of new targeted anti-cancer drugs. For practicing precision medicine, a sensitive method imaging for non-invasive, assessment of early treatment response and for assisting in developing new drugs is warranted. Magnetic Resonance Spectroscopy (MRS) is a potent technique for non-invasive in vivo investigation of tissue chemistry and cellular metabolism. Hyperpolarization by Dynamic Nuclear Polarization (DNP) is capable of creating solutions of molecules with polarized nuclear spins in a range of biological molecules and has enabled the real-time investigation of in vivo metabolism. The development of this new method has been demonstrated to enhance the nuclear polarization more than 10,000-fold, thereby significantly increasing the sensitivity of the MRS with a spatial resolution to the millimeters and a temporal resolution at the subsecond range. Furthermore, the method enables measuring kinetics of conversion of substrates into cell metabolites and can be integrated with anatomical proton magnetic resonance imaging (MRI). Many nuclei and substrates have been hyperpolarized using the DNP method. Currently, the most widely used compound is 13C-pyruvate due to favoring technicalities. Intravenous injection of the hyperpolarized 13C-pyruvate results in appearance of 13C-lactate, 13C-alanine and 13C-bicarbonate resonance peaks depending on the tissue, disease and the metabolic state probed. In cancer, the lactate level is increased due to increased glycolysis. The use of DNP enhanced 13C-pyruvate has in preclinical studies shown to be a sensitive method for detecting cancer and for assessment of early treatment response in a variety of cancers. Recently, a first-in-man 31-patient study was conducted with the primary objective to assess the safety of hyperpolarized 13C-pyruvate in healthy subjects and prostate cancer patients. The study showed an elevated 13C-lactate/13C-pyruvate ratio in regions of biopsy-proven prostate cancer compared to noncancerous tissue. However, more studies are needed in order to establish use of hyperpolarized 13C MRS imaging