Miracle-dz/UniFAR-FTUs huggingface dataset:
Intra‐ and inter‐laboratory reproducibility of the rat blood Pig‐a gene mutation assay

The in vivo Pig‐a assay is being used in safety studies to evaluate the potential of chemicals to induce somatic cell gene mutations. Ongoing work is aimed at developing an Organization for Economic Cooperation and Development (OECD) test guideline to support routine use for regulatory purposes (OECD project number 4.93). Among the requirements for OECD approval are demonstrations of assay reliability, including reproducibility within and among laboratories. Experiments reported herein address the reproducibility of the rat blood Pig‐a assay using the reference mutagens chlorambucil and melphalan. These agents were evaluated for their ability to induce Pig‐a mutant erythrocytes in three separate studies conducted across two laboratories. Each of the studies utilized a common treatment schedule: 28 consecutive days of exposure via oral gavage. Whereas one laboratory studied Crl:CD(SD) rats, the other laboratory used Wistar Han rats. One or two days after cessation of treatment blood samples were collected for mutant reticulocyte and mutant erythrocyte measurements that were accomplished with the same analytical technique whereby samples were depleted of wildtype erythrocytes via immunomagnetic separation followed by flow cytometric enumeration of mutant phenotype cells (MutaFlow®). Dunnett's test results showed similar qualitative outcomes within and between laboratories, that is, each chemical and each study demonstrated statistically significant, dose‐related increases in mutant reticulocyte and erythrocyte frequencies. Benchmark dose analysis (PROAST software) provided a means to quantitatively analyze the results, and the relatively tight, overlapping benchmark dose confidence intervals observed for each of the two chemicals indicate that within and between laboratory reproducibility of the Pig‐a assay are high, adding further support for the development of an OECD test guideline.
Estimating the United States Dollar Index Returns’ Value at Risk: Empirical Evidence from RiskMetrics and Simultaneous Bootstrap Quantile Regression Methods

Two methods, namely simultaneous bootstrap quantile regression and RiskMetrics, are backtesting and compared to establish which one is a better Value at Risk (VaR) estimate for the United States dollar index returns. Using daily closing prices and the nearby contract settlement prices from 20 November 1985 to 15 February 2017, the results of this empirical research point out that at 5% of the significance level, RiskMetrics with IGARCH (1, 1) underestimates VaR for the next trading day. From the backtest findings, the number of violations in the RiskMetrics method is more than in simultaneous bootstrap quantile regression even after controlling for marginal effects of the index futures returns and volatilities in both spot and futures markets.
Introduction to the handbook on fear and anxiety

Any volume that seeks to analyze two concepts – here fear and anxiety – needs to start by differentiating them. This volume will bring up this distinction in a number of contexts, and it will become clear that different authors may have somewhat different conceptions of what the distinction may be (e.g., chapter by McNaughton and Zangrossi). For current purposes, and because the editors have a robust position on this topic, we will start with this distinction: fear is the motivation associated with a number of behaviors that normally occur on exposure to clearly threatening stimuli. Anxiety is the motivation associated with behaviors that occur to potential, signaled, or ambiguous threat. Both anxiety and fear are often measured through the intensity or persistence of the behaviors with which they are associated, and may further be assessed by their ability to be conditioned to stimuli associated with these threats. These characterizations make it clear that fear and anxiety may intergrade or overlap, just as the stimuli that elicit them represent extremes of continua of clarity and immediacy of threat, such that a particular threat might appear at various points along these continua. From an ethological perspective, both fear and anxiety are highly adaptive responses. Both are persistent and intense under appropriate conditions, in all vertebrate species in which they have been examined. However, the behaviors associated with fear and anxiety are timeand labor intensive; they may have to be, in order to be successful in meeting the array of dangers that every living organism faces. Failures of intensity or persistence are some of, but certainly not all, the ways that fear and anxiety systems may be insufficient. The simple fact that each of us is alive to read these words indicates that every one of our direct ancestors, human or prehuman, displayed fear and anxiety patterns that were at least adequate to keep them alive through successful reproduction. It is not a negligible legacy. The problem with all such intense and persistent reactions is that they take effort and time. The evolutionary history of all species has included a world of threatening events. Left unchecked, the motivations and behavioral expression of fear and anxiety might easily consume a disproportionate portion of the energy and time budgets of individual animals, to the detriment of other crucial behaviors like obtaining food, sex, reproduction, and self-care. The major mechanisms limiting fear and anxiety, such as habituation and extinction, and behaviors facilitating these limitation processes, for example, risk assessment, are Corresponding authors. E-mail: blanchar@hawaii.edu
Evaluation of Predictors of Suicidal Re-Attempt in Pediatric Patients in the Emergency Department

Background: Suicidal attempt in children is a serious public health problem. A proper identification of features of suicide-related behavior may help physicians to develop an accurate approach. The aim of this study was to clarify the characteristics of children with poisoning due to suicidal attempt and to determine the risk factors of suicidal re-attempt in the Emergency Department (ED) via a simple questionnaire. Methods: We collected medical data of patients under 18 years who were admitted to our ED with intoxication due to suicidal attempt, retrospectively. General characteristics of patients were evaluated. Patients were divided into 2 groups as 1) High risk: patients with repetitive suicide attempt; 2) Low risk: patients with first time suicidal attempt. Results: A total of 57 patients were included in this study. The mean age was 15.91 ± 0.97. Majority of the patients were female (73.7%). Analgesics were the most frequent abused drugs with a ratio of 51.1%. It is determined that the most important variables affecting the risk of suicidal re-attempt are “idea about the suicide” and “purpose”. It was determined that patients with an idea of repetitive suicide (I will try again) and whose purpose was to die (I wish I have died) were in the most risky group with a history of previous suicidal attempt. Conclusion: This study suggests that answers of the pediatric patients to some question have a potential to predict the high risk patients. The risk of suicidal re-attempt may be predicted by the answers given to these questions: 1) What is your idea about suicide? 2) What was your purpose?
Electroencephalography (EEG) based Brain Computer Interface

: Brain Computer Interface (BCI) generate a direct technique to speak with the outside world. Many sufferers are not able to speak. As an example:- the affected person who're suffered with the numerous diseases like put up stroke - the process of wondering, remembering & recognizing may be tough. Be-purpose of spinal cord accidents or mind stem stroke the patient loss the monitoring electricity. EEG primarily based mind laptop interface (BCI) characteristic is beneficial to scale the mind motion & convert them right into a preparation for monitoring. In this paper our goal is to have a look at about numerous applications of EEG based signal of the special disorder like spinal wire damage, submit stroke and ALS (amyotrophic lateral sclerosis) etc.
A STUDY ON ANTIMICROBIAL PROPERTIES OF HERBAL NANOPARTICLES OF SELECTED MANGROVE PLANTS

Homogenous plant powder at nanoscale is the need of the hour for existing and newly emerging biomedical applications, and novel drug delivery with less side effects. Several methods are used for the synthesis of nanoparticles (NPs) such as physical, chemical, enzymatic and biological. Ball milling is one of the physical methods used for synthesis of homogeneous nanoparticles. The herbal nanoparticles were prepared from shade dried selected mangrove plant leaves i.e. Avicennia marina, Rhizophora apiculata, and Excoecaria agallocha of Krishna estuary by employing ball milling technique. The XRD analysis revealed that the obtained nanoparticles ranged between 14.38 to 28.70 nm. The nano size of the powdered leaf material was also confirmed by Transverse Electron Microscopy (TEM) and UV-VIS spectrophotometry. The FTIR analysis and EDS confirmed the presence of various functional groups and mineral elements present in the herbal nanoparticles. The nanoparticles with less size formed from R. apiculata showed maximum antibacterial and antifungal activity with a zone of inhibition of 26 mm on Bacillus subtilis. The present study confirms that smaller nanoparticles are found to exhibit maximum zone of inhibition when compared with larger particles.
Review on Shape Classification Techniques

: Nowadays, all the images are generated in digital form. There is growing interest in finding images from the databases. The images should be represented or described in some features, so that they can be find out. The important visual feature of an image is shape. Using shape as a feature to search the images is grabbing much attention. There are many shape description and representation techniques used all over the world. In this paper, we review such techniques. The procedure of each technique is examined and discussed.
Exploring the Role of Albanian International Migration on Education

In an attempt to shed some light in the process of development through migration, the purpose of this paper is to explore the effects of international migration on education in Albania. In doing so, it will examine the role of international migration on school enrolments, educational attainment, and household’s educational expenditures for individuals between the ages of 6 and 22. The analysis is conducted using the 2005 Albanian Living Standard Measurement Study survey, to examine the impact of migration on education across regions, gender, and age-groups (14 to 17 year old). Data availability allow us to also separate the impact of international migration by country of migration, and by gender of migrants. Across model specification, our findings suggest an overall negative impact of migration on education in Albania, with larger negative impacts for the rural areas and females.
Semantics-guided Exploration of Latent Spaces for Shape Synthesis

We introduce an approach to incorporate user guidance into shape synthesis approaches based on deep networks. Synthesis networks such as auto-encoders are trained to encode shapes into latent vectors, effectively learning a latent shape space that can be sampled for generating new shapes. Our main idea is to allow users to start an exploratory process of the shape space with the use of high-level semantic keywords. Specifically, the user inputs a set of keywords that describe the general attributes of the shape to be generated, e.g., “four legs” for a chair. Next, we map the keywords to a subspace of the latent space that captures the shapes possessing the specified attributes. The user then explores only the subspace to search for shapes that satisfy the design goal, in a process similar to using a parametric shape model. Our exploratory approach allows users to model shapes at a high-level without the need of advanced artistic skills, in contrast to existing methods that allow to guide the synthesis with sketching or partial modeling of a shape. Our technical contribution to enable this exploration-based approach is the introduction of a label regression neural network coupled with a shape synthesis neural network. The label regression network takes the user-provided keywords and maps them to the corresponding subspace of the latent space, where the subspace is modeled as a set of distributions for the dimensions of the latent space. We show that our method allows users to efficiently explore the shape space and generate a variety of shapes with selected high-level attributes.
Vision-based Indoor Localization Algorithm using Improved ResNet

The output of the residual network fluctuates greatly with the change of the weight parameters, which greatly affects the performance of the residual network. For dealing with this problem, an improved residual network is proposed. Based on the classical residual network, batch normalization, adaptive -dropout random deactivation function and a new loss function are added into the proposed model. Batch normalization is applied to avoid vanishing/exploding gradients. -dropout is applied to increase the stability of the model, which we select different dropout method adaptively by adjusting parameter. The new loss function is composed by cross entropy loss function and center loss function to enhance the inter class dispersion and intra class aggregation. The proposed model is applied to the indoor positioning of mobile robot in the factory environment. The experimental results show that the algorithm can achieve high indoor positioning accuracy under the premise of small training dataset. In the real-time positioning experiment, the accuracy can reach 95.37.
on biogenically driven new particle formation in Svalbard ( SVALBAEROSOL ) 168

1 Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki, Finland 2 Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany 3 Institute of Polar Sciences, National Research Council of Italy, Via Fosso del Cavaliere 100, 00133 Rome, Italy 4 Institute of Polar Sciences, National Research Council of Italy, Via P. Gobetti 101, 40129 Bologna, Italy 5 Stockholm University, Svante Arrhenius väg 8, SE-11418 Stockholm, Sweden
Drugs Induced Alzheimer’s Disease in Animal Model

Alzheimer’s disease (AD) can be described by characteristics like dementia, mental and cognitive dysfunctions, and memory impairment. Nowadays, with progresses of science, attempts to treat many diseases have increased. Laboratory animals help to discover new ways of treating disease. AD induced by chemical drugs in animal models can be useful in better understanding the mechanisms of disease and treatment of AD. In recent decades, many researchers have reported transgenic rat models of AD but this modeling has a great problem and does not contain all kinds of AD. There are two types of AD, including familial (5% of all AD) and sporadic, but the transgenic model does not show the complete model of AD, especially in sporadic form of AD, which is 95% of AD cases. We decided to describe another modeling of AD using chemical drugs such as colchicine, scopolamine, okadaic acid, streptozotocin, and trimethyltin. [GMJ.2017;6(3):185-96] DOI:10.22086/gmj.v6i3. 820
Towards Building User Affect Models from Tweets: a comparative study with various datasets, features and algorithms

— In this paper, we use machine learning techniques to try to ﬁnd the best possible solution, including datasets, emotion lexicons, features, and algorithms, for the automatic detection of emotions in tweets. For that purpose, we consider as emotional categories, the six basic emotions from Paul Ekman ( anger , disgust , fear , happiness , sadness , and surprise ). For the experiments, we rely on ﬁve datasets, of which three are regularly found in the literature (news headlines, fairy tales, and blogs sentences), another is directly related with the topic under study (emotional tweets), and the other was composed by us (emotive tweets), speciﬁcally for this study and will be primarily used as an evaluation dataset. In addition, we also count with three different emotion lexicons: WordNet-Affect, Roget’s Thesaurus, and NRC Emotion Lexicon. From this collection of data, we compare the performance of two different classiﬁcation algorithms: Naive Bayes and Support Vector Machines (SVM). The experiments evidence the existence of two alternatives that are suitable for the purpose of this study. We evaluate and discuss the results from the point of view of building user affect models, based on the emotional information that can be collected from the tweets.
Preschoolers search longer when there is more information to be gained

What drives human search in situations with sparse rewards? We let preschoolers (age 24-52 months) play a game in which they had to search for an animal and measured their persistence in the absence of explicit rewards. Crucially, we either told children which animal they were searching for (known animal condition), so that they gained no additional information when finding it, or we told them that it could be one out of eight different animals (unknown animal condition), so that they gained additional information when finding it. Although none of the children could actually find an animal, no matter how long they searched, our results indicate that the unknown animal group was more persistent in their search. These results enrich our understanding of children’s motivation in scenarios with sparse rewards and inform research on artificially intelligent agents facing similar environments.
MONITORING OF SUSPICIOUS DISCUSSIONS ON ONLINE FORUMS USING DATA MINING

: With the increasing years, the internet has changed the lives of so many people for better or worse. As internet technology is progressing, many illegal activities have also increased exponentially. The Internet is an unacknowledged path for illegal activities such as hacking, trafficking, betting, fraud and scams etc. The cyber-crime branches are looking for provisions to detect these forums for illegal feedbacks, comments or reviews and download questionable postings as verification for their investigation. Our proposed system will monitor for suspicious postings, collect it from few discussion forums, implement techniques of data mining and extract meaningful data. In this concern, we focus on Data Mining and Sentimental Analysis to enhance the techniques and to extract the features of the text to represent them.
TEACHING CO-DESIGN IN INDUSTRIAL DESIGN CASE STUDIES OF EXSISTING PRACTICES

Co-design is a process that allows designers to develop products with greater insight to user needs through the participation of users in the design process. During this process what users say, make, and do is investigated using common research methods in combination with newer generative and exploratory approaches created for this purpose. Despite the prevalence of the co-design process, a lack of studies into the education of designers on co-design have been implemented, leaving a gap of information that needs to be filled in order for co-design to become integrated into design education and practice. The purpose of this study is to understand the current state of co-design education in the U.S. and to assimilate popular teaching techniques, by surveying teaching methods of co-design within Industrial Design programs at U.S. Universities with reputations as leaders in the field. A snowball sampling was performed with schools leaders in co-design. Schools were contacted and given a survey, interviewed with selected participants and assessed on their materials and practices on co-design. Various qualitative data analysis was performed with the surveys, interviews and materials. The conclusion includes a summary of key findings on the teaching of co-design. The significance of this project is to further research into teaching methods of co-design as well as providing a common framework for design educators to follow in higher level learning institutions
Estimation of Chlorophyll From Aeromatic Medicinal Plants Used As Potent Therapeuritic Agents In Anaemic Patients

_____ Abstract: Chlorophyll is a green pigment, which is structurally similar to porphyrin pigments such as heme and it is produced through the same metabolic pathway in plants. Chlorophyll benefits the body in a unique and distinctive ways. It is helps to cleans harmful toxins from the body and it is also used to fight infection. Chlorophyll antiviral effects make it a strong skin protector. A recommended and regular intake of chlorophyll can keep the circulatory and digestive system much healthier. The present study, the chlorophyll was extracted from the different medicinal and aromatic plants, and characterized by UV-visible spectroscopy. These plant juice extract contains rich chlorophyll (whose structure is similar to hemoglobin except the central moiety), Amino acids, Minerals (like iron), Vitamins (like B12) and active enzymes are present. These juices’ were used as therapeutic agents for treatment of various problems including Anemia, Thalassemia, etc. In our study we treated anemia with intake of three different juices for three different groups and found a significant results in treating anemia.
Impacts of monoculture and mixed vegetation on green roof hydrological function Impacts de monocultures et de végétations variées sur la rétention hydraulique et les débits maximaux de ruissellement

A dense vegetation cover is usually desired to fulfil aesthetical expectations of green roofs, and therefore stress tolerant Sedum vegetation has traditionally been favoured for extensive green roofs. However, Sedum species’ low water use and low root biomass could prove suboptimal for the hydrological function of green roofs compared to vegetation with different water use strategies. This study evaluated the hydrological performance of four different vegetation mixtures based on Grime’s C-S-R life strategies and compared their performance to a Sedum monoculture and bare substrate. Runoff from seven rainfall events (3.4–8.4 mm) was recorded during one autumn season when temperatures were getting lower (6–13 ̊C). The results showed no relationship between vegetation cover and retention, and the roofs planted with a mixture of stress tolerant species showed the greatest overall retention. Green roofs planted with the Sedum monoculture had the greatest vegetation cover but the lowest mean retention.