Miracle-dz/UniFAR-FTUs huggingface dataset:
Systemic nicotine‐induced dopamine release in the rat nucleus accumbens is regulated by nicotinic receptors in the ventral tegmental area

Stimulation of the mesolimbic dopamine (DA) system is considered of major importance for the rewarding and dependence producing properties of nicotine (NIC). To identify the site of this stimulatory action, simultaneous microdialysis was performed in the ventral tegmental area (VTA) and the ipsilateral nucleus accumbens (NAC) of awake rats. Extracellular concentrations of DA and its metabolites were measured in the NAC. NIC (0.5 mg/kg, s.c.) increased DA and its metabolites by ∼50%. Concomitant infusion of the nicotinic receptor antagonist mecamylamine (MEC, 100 μ) through the VTA probe, starting 40 min before NIC injection, antagonized the NIC induced increases of DA and its metabolites. In contrast, similar MEC pretreatment (40 or 140 min) in the NAC did not affect DA or metabolite responses to systemic NIC. Infusion of NIC (1,000 μ) in the NAC or the VTA increased DA release by 49% and 48%, respectively, whereas only the VTA infusion increased metabolite concentrations by ‐25%. MEC infusion (1–1,000 μ) in the VTA did not affect DA or its metabolites, whereas the 1,000 μ concentration infused in the NAC increased DA by 77%. These results suggest that nicotinic receptors in the somatodendritic region may be of greater importance than those located in the terminal area for the stimulatory action of systemic NIC on the mesolimbic DA system. Furthermore, our findings support the notion that the mesolimbic dopaminergic system is phasically rather than tonically regulated by nicotinic receptor activation within the VTA. © 1994 Wiley‐Liss, Inc.
Unknown malcode detection via text categorization and the imbalance problem

Todaypsilas signature-based anti-viruses are very accurate, but are limited in detecting new malicious code. Currently, dozens of new malicious codes are created every day, and this number is expected to increase in the coming years. Recently, classification algorithms were used successfully for the detection of unknown malicious code. These studies used a test collection with a limited size where the same malicious-benign-file ratio in both the training and test sets, which does not reflect real-life conditions. In this paper we present a methodology for the detection of unknown malicious code, based on text categorization concepts. We performed an extensive evaluation using a test collection that contains more than 30,000 malicious and benign files, in which we investigated the imbalance problem. In real-life scenarios, the malicious file content is expected to be low, about 10% of the total files. For practical purposes, it is unclear as to what the corresponding percentage in the training set should be. Our results indicate that greater than 95% accuracy can be achieved through the use of a training set that contains below 20% malicious file content.
Are Restaurants Really Supersizing America?

While many researchers and policymakers infer from correlations between eating out and body weight that restaurants are a leading cause of obesity, a basic identification problem challenges these conclusions. We exploit the placement of Interstate highways in rural areas to obtain exogenous variation in the effective price of restaurants and examine the impact on body mass. We find no causal link between restaurant consumption and obesity. Analysis of food-intake micro-data suggests that consumers offset calories from restaurant meals by eating less at other times. We conclude that regulation targeting restaurants is unlikely to reduce obesity but could decrease consumer welfare.
MaxSAT by Improved Instance-Specific Algorithm Configuration

Our objective is to boost the state-of-the-art performance in MaxSATsolving. To this end, we employ the instance-specific algorithmconfigurator ISAC, and improve it with the latest inportfolio technology. Experimental results on SAT show that thiscombination marks a significant step forward in our ability to tunealgorithms instance-specifically. We then apply the new methodology toa number of MaxSAT problem domains and show that the resulting solversconsistently outperform the best existing solvers on the respectiveproblem families. In fact, the solvers presented here were independentlyevaluated at the 2013 MaxSAT Evaluation where they won six of the elevencategories.
`Like Immigrants'

This article explores the father-son-computer triangle in an attempt to shed light on the role of the machine in the articulation of male identity in particular, and family relationships in general. The article outlines a framework for the investigation of families and domestic communication technologies, arguing that the study of identity construction through the medium must be accompanied by a study of the relationships around the medium; and that men and boys need to be (re)incorporated into the work on the human-machine problematic. Drawing on an analysis of the discourse of three families that were observed and interviewed in the course of one year, the article proposes that the notion of computer expertise and the sense of dependence are key for the construction of fatherhood and masculinity vis a vis the home computer, and points to the metaphor of immigrants' language acquisition, which was offered by one of the fathers, as capturing the complexity of contemporary paternal emasculation.
On the Size of Systems of Sets Every t of Which Have an SDR, with an Application to the Worst-Case Ratio of Heuristics for Packing Problems

Let $E_1 ,\cdots,E_m $ be subsets of a set V of size n, such that each element of V is in at most k of the $E_i $ and such that each collection of t sets from $E_1 ,\cdots ,E_m $ has a system of distinct representatives (SDR). It is shown that $m/n\leqq (k(k - 1)^r - k)/(2(k - 1)^r - k)$ if $t = 2r - 1$, and $m/n \leqq (k(k - 1)^r - 2)/(2(k - 1)^r - 2)$ if $t = 2r$. Moreover it is shown that these upper bounds are the best possible. From these results the “worst-case ratio” of certain heuristics for the problem of finding a maximum collection of pairwise disjoint sets among a given collection of sets of size k is derived.
Smoking Patterns among Primary School Students in Turkey

Cigarette smoking continues to be a threat to global health. The number of cigarettes smoked per person tends to increase each year, and the age of starting seems to be dropping. The research related to cigarette smoking conducted among young people generally studied high school or university students. However, studies have shown that students usually start smoking during the primary school period out of curiosity or imitation. The purpose of the present study was to find the prevalence of cigarette smoking among primary school students and the reasons for starting smoking, and to determine the characteristics of cigarette smoking of their parents. This study was conducted among 17 primary schools chosen according to their socioeconomic situations in different municipality districts in Turkey, with 9,408 students participating. Data were obtained by questionnaire. The mean age to start smoking was 11.7 ± 1.6; 82.9% of the students who took part in this study had never smoked before, 13.4% had tried smoking at least once, and 3.7% had been smoking regularly. The biggest reason for smoking was just curiosity or imitation. It was determined that a risk factor for students to start smoking was parents who smoke. The 17% smoking rate among primary school students was high in our opinion and prevention studies initiated. In addition, the effects of cigarette-smoking parents on students who start smoking should also be considered.
Recovering the dynamics of root growth and development using novel image acquisition and analysis methods

Roots are highly responsive to environmental signals encountered in the rhizosphere, such as nutrients, mechanical resistance and gravity. As a result, root growth and development is very plastic. If this complex and vital process is to be understood, methods and tools are required to capture the dynamics of root responses. Tools are needed which are high-throughput, supporting large-scale experimental work, and provide accurate, high-resolution, quantitative data. We describe and demonstrate the efficacy of the high-throughput and high-resolution root imaging systems recently developed within the Centre for Plant Integrative Biology (CPIB). This toolset includes (i) robotic imaging hardware to generate time-lapse datasets from standard cameras under infrared illumination and (ii) automated image analysis methods and software to extract quantitative information about root growth and development both from these images and via high-resolution light microscopy. These methods are demonstrated using data gathered during an experimental study of the gravitropic response of Arabidopsis thaliana.
Preliminary evaluation of hemostasis in neonatal foals using a viscoelastic coagulation and platelet function analyzer.

OBJECTIVES To compare coagulation and platelet function parameters measured using a viscoelastic analyzer in 3 groups: foals presenting to a neonatal intensive care unit with presumed sepsis, normal foals, and adult horses. DESIGN Preliminary prospective trial. SETTING Veterinary teaching hospital. ANIMALS Ten clinically healthy foals, 13 clinically healthy adult horses, and 17 foals sequentially admitted for suspected sepsis. Intervention- A single citrated (3.8%) blood sample collected at admission was submitted for coagulation evaluation using a viscoelastic analyzer. MEASUREMENTS AND MAIN RESULTS Time to initial clot formation (ACT), clot rate (CR), platelet function, and time to peak parameters were collected from the signature generated with the associated software. Peak clot strength was collected manually from signature tracings. Signalment, presenting complaint, blood culture results, clinical progression, and outcome were collected from the medical record. Kruskal-Wallis testing was used to determine differences in coagulation parameters between groups, as well as to identify any associations between coagulation variables, foal variables, and outcome. Normal foals were more likely to have increased platelet function (P=0.04) compared with normal adult horses. Prolonged ACT (P=0.004) and decreased CR (P=0.03) were associated with foals with positive blood culture. There was a trend toward prolonged ACT and increased likelihood of death (P=0.06). CONCLUSIONS Healthy foals differ in values measured by the viscoelastic coagulation and platelet function analyzer compared with healthy adult horses. ACT and CR abnormalities were more likely to be observed in foals with positive blood cultures. The viscoelastic coagulation and platelet function analyzer may be useful in identifying early hemostasic and platelet dysfunction in critically ill foals, particularly those that are septic.
Environmental effects on neonatal mortality of beef calves.

Calving records from 1969 to 1989 from the Roman L. Hruska U.S. Meat Animal Research Center were used to investigate how climatic conditions, in addition to dystocia, age of dam, size of calf, and sex affect calf survival from birth to 1 wk of age. Data were analyzed separately for cows calving with (n = 11,094) or without (n = 72,187) dystocia. Neonatal mortality was described by a logit model and parameters were estimated by maximum-likelihood procedures. Calves born to cows with dystocia were five times as likely to die neonatally than calves born without assistance. Of all calves that died, 43.6% were born with difficulty. Of these calves, survival was lowest for those that were small relative to their genetic group, sex, and age of dam. Large calves had markedly increased mortality only when born to 2-yr-old dams. Average ambient temperature and precipitation on day of calving affected survival nonlinearly and the magnitude of the effect depended on age of dam, sex and size of calf, and dystocia incidence. Calves born to 2-yr-old cows were more susceptible to severe weather conditions than calves born to older cows. The negative effect of precipitation on survival increased with decreasing temperature.
An Updated Review on Phytochemical Properties of “Golden Dewdrop” Duranta erecta

Duranta erecta (family: Verbenaceae) commonly referred to golden dewdrop, pigeon berry, angel whisper, or skyflower is one of the traditional medicinal plants. It has been shown to possess antimicrobial, antioxidant, and insecticide properties. Its phytoconstituents such as alkaloids, flavonoids, glycosides, phenolics, saponins, steroids, tannins, and terpenoids are reported as the basis of its efficacious therapeutic properties. The other important constituents which contribute to the remedial properties are durantol, pectolinaringenin, repennoside, repenins, and scutellarein. Published information on the phytochemical property of D. erecta was gathered by the use of different database platforms, including Google Scholar, ScienceDirect, PubMed, SciFinder, and Scopus, that provided an up-to-date review on its importance.
Between consent and dissent : democracy and peace in the Israeli mind

Chapter 1 Introduction Chapter 2 Indices of Democracy and Nationalism Chapter 3 Democracy-A Matter of Demography? Chapter 4 Ideological Labels and Commitment to Democracy Chapter 5 Religiousity and Political Attitudes Chapter 6 Democratic Commitment in Wartime Chapter 7 "What Are you Actually Saying? "-Summarizing Interview
A Safe Change Oriented Process for Safety Critical Systems

The development of aviation engine control soft ware is a prime example of a process in which the overriding concern is safety In building such a com plex system change is inevitable This paper discusses the aims of CONVERSE an EPSRC funded project to investigate change management in safety critical soft ware development The aim is to reduce the costs which occur due to change while allowing rapid re collection of the safety evidence required for such sys
Recognizing emotions in speech using short-term and long-term features

The acoustic characteristics of speech are influenced by speakers’ emotional status. In this study, we attempted to recognize the emotional status of individual speakers by using speech features that were extracted from short-time analysis frames as well as speech features that represented entire utterances. Principal component analysis was used to analyze the importance of individual features in representing emotional categories. Three classification methods including vector quantization, artificial neural networks and Gaussian mixture density model were used. Classifications using short-term features only, long-term features only and both short-term and long-term features were conducted. The best recognition performance of 62% accuracy was achieved by using the Gaussian mixture density method with both short-term and longterm features.
A Universal Strategy for Proteomic Studies of SUMO and Other Ubiquitin-like Modifiers*S

Post-translational modification by the conjugation of small ubiquitin-like modifiers is an essential mechanism to affect protein function. Currently, only a limited number of substrates are known for most of these modifiers, thus limiting our knowledge of their role and relevance for cellular physiology. Here, we report the development of a universal strategy for proteomic studies of ubiquitin-like modifiers. This strategy involves the development of stable transfected cell lines expressing a double-tagged modifier under the control of a tightly negatively regulated promoter, the induction of the expression and conjugation of the tagged modifier to cellular proteins, the tandem affinity purification of the pool of proteins covalently modified by the tagged modifier, and the identification of the modified proteins by LC and MS. By applying this methodology to the proteomic analysis of SUMO-1 and SUMO-3, we determined that SUMO-1 and SUMO-3 are stable proteins exhibiting half-lives of over 20 h, demonstrated that sumoylation with both SUMO-1 and SUMO-3 is greatly stimulated by MG-132 and heat shock treatment, demonstrated the preferential usage of either SUMO-1 or SUMO-3 for some known SUMO substrates, and identified 122 putative SUMO substrates of which only 27 appeared to be modified by both SUMO-1 and SUMO-3. This limited overlapping in the subset of proteins modified by SUMO-1 and SUMO-3 supports that the SUMO paralogues are likely to be functionally distinct. Three of the novel putative SUMO substrates identified, namely the polypyrimidine tract-binding protein-associated splicing factor PSF, the structural microtubular component α-tubulin, and the GTP-binding nuclear protein Ran, were confirmed as authentic SUMO substrates. The application of this universal strategy to the identification of the pool of cellular substrates modified by other ubiquitin-like modifiers will dramatically increase our knowledge of the biological role of the different ubiquitin-like conjugations systems in the cell.
Semantics, Representations and Grammars for Deep Learning

Deep learning is currently the subject of intensive study. However, fundamental concepts such as representations are not formally defined -- researchers "know them when they see them" -- and there is no common language for describing and analyzing algorithms. This essay proposes an abstract framework that identifies the essential features of current practice and may provide a foundation for future developments. The backbone of almost all deep learning algorithms is backpropagation, which is simply a gradient computation distributed over a neural network. The main ingredients of the framework are thus, unsurprisingly: (i) game theory, to formalize distributed optimization; and (ii) communication protocols, to track the flow of zeroth and first-order information. The framework allows natural definitions of semantics (as the meaning encoded in functions), representations (as functions whose semantics is chosen to optimized a criterion) and grammars (as communication protocols equipped with first-order convergence guarantees). Much of the essay is spent discussing examples taken from the literature. The ultimate aim is to develop a graphical language for describing the structure of deep learning algorithms that backgrounds the details of the optimization procedure and foregrounds how the components interact. Inspiration is taken from probabilistic graphical models and factor graphs, which capture the essential structural features of multivariate distributions.
Integrated pathway/genome databases and their role in drug discovery

1 Integrated pathway/genome databases describe the genes and genome of an organism, as well as its predicted pathways, reactions, enzymes and metabolites. In conjunction with visualiza-tion and analysis software, these databases provide a framework for improved understanding of microbial physiology and for antimicrobial drug discovery. W e describe pathway-based analyses of the genomes of a number of medically relevant microorganisms, and a novel software tool that provides visualization of gene expression data on a diagram showing the whole metabolic network of the microorganism. The method of inferring the function of a DNA or protein sequence by analogy to the functions of other similar sequences has had a profound impact on our ability to identify the functions of sequenced genes. Similar reasoning can be applied by analogy to biological pathways to identify the presence of known metabolic pathways in the annotated genome of an organism. Just as we can predict the function of an unknown sequence S by searching a reference database (DB) of sequences for those that are similar to S , w e can also predict pathways from a sequenced genome by analogy to a reference DB of pathways. A n umber of groups have d e v eloped techniques for predicting the metabolic pathways of an organism from its genome and for producing integrated pathway/genome databases that model the resulting predictions 1]. The techniques used range from manual analysis to automated computational analysis and the resulting databases vary according to both the types of information they contain and the software tools they make a vailable for the querying, visualization and analysis of that information. Such projects include the KEGG project 2,3], ? the WIT project 4], ? and a project at Pangea Systems that has produced the collection of microbial pathway/genome DBs described in this article. We will describe the information contained in these DBs, the methods used to create them, and some of the ways they may be exploited to support antimicrobial drug discovery. Pangea Systems has developed a collection of integrated metabolic pathway/genome DBs for a set of medically relevant microorganisms (Table 1). These DBs consist of a set of microorganism-speciic pathway/genome datasets, and a software environment for querying , analyzing and visualizing these datasets. Each dataset combines information about the genome and the metabolic network of one microorganism. The metabolic network is described in terms of four biological object types: (1) the pathways that compose …
A Genetic Algorithm for Automated Refactoring of Component-Based Software

Nowadays a software undergoes modifications done by different people to quickly fulfill new requirements, but its underlying design is not adjusted properly after each update. This leads to the emergence of bad smells. Refactoring provides a de facto behavior-preserving means to eliminate these anomalies. However, manually determining and performing useful refactorings is known as an NP-Complete problem as stated by Harman et al. because seemingly useful refactorings can improve some aspect of a software while making another aspect worse. Therefore it has been proposed to view object-oriented automated refactoring as a search based technique. However the review of the litterature shows that automated refactoring of component-based software has not been investigated yet. Recently a catalogue of component-relevant bad smells has been proposed in the literature but there is a lack of component-relevant refactorings. In this paper we propose a catalogue of component-relevant refactoring as well as detections rules for component-relevant bad smells. Then we rely on these two ingredients to propose a genetic algorithm for automated refactoring of component-based software systems.
Estimation of Design Parameters of Single-Phase Distribution Transformers From Terminal Measurements

In this paper, a novel method is proposed for the estimation of the design parameters of single-phase distribution transformers using data acquired from terminal measurements along with nameplate data and external tank dimensions. Transformer parameters that can be measured from terminals, for example, leakage inductance, saturation inductance, and winding resistance, are considered first. These parameters are analytically correlated with the geometrical information of the transformer structure. A system of nonlinear equations is derived accordingly. Winding dimensions and the number of turns are computed with acceptable engineering accuracy. The core dimensions (including its cross-sectional area) are calculated using winding information. The obtained data can be utilized to develop white- or gray-box models to be used in the investigation of the thermal and/or electromagnetic behavior of power transformers as presented in several research studies published to date. Examples on laboratory and utility-grade transformers are shown for illustration and validation of the proposed design parameter estimation method.