Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of stability constants of strong metal–ligand complexes using anion or cation exchange chromatography and atomic spectrometry detection

A method for the determination of stability constants of polyaminocarboxylic acid ligands with trivalent metal ions based on the addition of a second metal ion as a competitor has been developed. However, the absolute values of these stability constants are too high for an accurate direct determination. A competition approach delivers the ratio of the stability constants of the two competing metal ions. The different species were separated via anion and cation ion exchange chromatography and their metal content was quantitatively detected online utilizing inductively coupled plasma atomic emission spectrometry. The model ligand used was N,N,N′,N′-ethylenediaminetetraacetic acid as a most prominent polyaminocarboxylic acid ligand. The trivalent ions of chromium, iron, gallium, indium, and scandium were successfully investigated using this approach.
Order‐constrained linear optimization

Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order-constrained linear least-squares (OCLO) optimization algorithm that maximizes the linear least-squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out-of-sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat-tailed distributions), OCLO shows less bias and variance, and substantially better out-of-sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data.
Second-Generation Poverty Reduction Strategies: New Opportunities and Emerging Issues

The poverty reduction strategy paper (PRSP) approach has taken centre stage in development assistance over the past five years. International endorsement of the approach has led to important gains in government focus on poverty reduction, civil society participation and donor behaviour. Yet PRSPs also potentially offer a much greater contribution to aid effectiveness, good governance and poverty reduction in developing countries. With a second generation of PRSPs now set to appear, this article examines the technical and political challenges that need addressing to realise their potential over time. Emerging issues include how to respond to political transitions, alternative design approaches, how to assess financing needs, and ways of enhancing the predictability of aid.
On the Value of Purpose-Orientation and Focus on Locals in Recommending Leisure Activities

Recommender systems are omnipresent today, especially on the Web, and the quality of their recommendations is crucial for user satisfaction. Unlike most works on the topic, in this article we do not focus on the algorithmic side of the problem (i.e., searching for the algorithm that better learns from the collected user feedback) and instead study the importance of the data in input to the algorithms, identifying the information that should be collected from users to build better recommendations. We study restaurant recommendations for locals and show that fine-tuned data and state-of-the-art algorithms can outperform the leading recommendation service, TripAdvisor. The findings make a case for better-thought and purpose-tailored data collection techniques.
Cargo sorting into multivesicular bodies in vitro

Genetic studies have identified a number of proteins required for the internalization of biosynthetic and endocytic cargo proteins transported to the multivesicular body (MVB). We have developed a cell-free reaction that recapitulates the internalization of a yeast biosynthetic membrane cargo protein, carboxypeptidase S (CPS), into the interior of an endosome. A recombinant form of CPS containing a biotinylation site from an Escherichia coli protein is accumulated in a vps27 yeast mutant blocked in the MVB internalization event. Endosomes isolated from the vps27 mutant are exposed to E. coli biotin ligase, which acts on only those CPS molecules with a cytosol-exposed N-terminal domain. Internalization of biotin-tagged CPS is measured by the detection of trypsin-inaccessible, membrane-protected species. Biotinylated CPS internalization requires ATP and functional forms of Vps27p and Vps4p and depends on the availability of an exposed lysine residue critical for CPS ubiquitylation.
Pegfilgrastim for primary prophylaxis of febrile neutropenia in breast cancer patients undergoing TAC chemotherapy

Purpose Primary prophylaxis with granulocyte colony-stimulating factor can effectively prevent febrile neutropenia (FN) during breast cancer treatment. The aims of this study were to evaluate the incidence of FN and the ANC profile in patients undergoing chemotherapy and pegfilgrastim primary prophylaxis. Methods Patients receiving 6 cycles of adjuvant docetaxel, doxorubicin, and cyclophosphamide (TAC) chemotherapy were included in this study. Pegfilgrastim was administered with analgesics 24 hours after treatment. Laboratory tests were performed on day 0 (before chemotherapy) and ANC was measured daily starting day 5 until it were restored to 1,000/mm3. Bone pain was checked via the numeral rating scale (NRS). Results A total of 61 patients and 366 cycles were evaluated. Mean age was 49.2 ± 7.1 years. FN was seen in 5 patients (16.4%) and 12 cycles (3.3%) with pegfilgrastim. Grades 3 and 4 neutropenia was seen in 91.5% of cycles with FN. The ANC nadir was most commonly seen at day 7 and the mean ANC nadir depth was 265.7/m3. Age was negatively correlated with nadir depth (r = −0.137, P = 0.009). Severe pain higher than NRS 7 occurred in less than 20% of patients after the administration of pegfilgrastim. Conclusion Incidence of FN was low during the chemotherapy by primary prophylaxis with pegfilgrastim. The ANC nadir was seen on day 7 after chemotherapy. Bone pain with pegfilgrastim was well tolerated during TAC chemotherapy.
Determinants of farmers' trust in government agricultural agencies in Poland

The purpose of this study was to investigate the levels of farmers’ trust toward state agricultural agencies in Poland, to identify its determinants and to develop a model illustrating the relationship between trust and the different dimensions of the agencies performance. Specifically this study looks at two agencies: the Agency for restructuring and Modernisation of Agriculture and the Agricultural Market Agency. Data were obtained from responses to a set of questions asked of the nationwide sample of 200 Polish farmers in December 2006–January 2007. The farmers’ evaluations according to a range of statements about the agencies were measured using ten-point Likert scale. Partial Least Squares Path Modelling was employed to estimate the latent (theoretical) variables such as trust, image, performance, satisfaction etc. Additionally, descriptive statistics such as frequencies, percentages and means as well as correlation coefficients were applied. Evidence is presented that the trust of farmers in the institutions of state, represented here by two agricultural agencies, is statistically correlated with the image of the agencies, respondents’ expectations and their satisfaction about the way the agencies work as well as with perceived value of the services and programmes provided by the agencies. Farmer’s socioeconomic characteristics (gender, education, the period of their interaction with the agencies, farm size) did not relate to trust score with the exception of age. causality analysis showed that trust towards both agencies was significantly impacted by their image with the customers. Trust had significant impact on the overall satisfaction with the ArMA.
Complete rRNA Sequence, Arrangement of Tandem Repeated Units and Phylogeny of Nosema fumiferanae from Spruce Budworm, Choristoneura fumiferana (Clemens)

We provide molecular systematics of a microporidian species, Nosema fumiferanae, one of the most common natural enemies of spruce budworm, Choristoneura fumiferana. The uncharacterized flanking region upstream of the large subunit (LSU) rRNA and the complete rRNA cistron of N. fumiferanae was 4,769 bp long. The organization of the rRNA gene was 5′‐LSU rRNA‐ITS‐SSU rRNA‐IGS‐5S‐3′ and corresponded primarily to most insect (i.e. lepidopteran) Nosema species identified and classified to date. Phylogenetic analysis based on the complete rRNA cistron indicated that N. fumiferanae is closely related to Nosema plutellae and is correctly assigned to the “true” Nosema group. Suggestions were provided on a criterion to delineate the “true” Nosema from other microsporidian species.
Peptide-functionalized quantum dots for potential applications in the imaging and treatment of obesity

Background Obesity is a worldwide epidemic affecting millions of people. The current pharmacological treatment of obesity remains limited and ineffective due to drugs’ undesirable side effects. Hence, there is a need for novel or improved strategies for long-term therapies that will help prevent the disease progression into other chronic diseases. Nanotechnology holds the future for the treatment of obesity because of its versatility, as shown by improved drug efficiency and safety in cancer clinical trials. Nano-based drug delivery systems could potentially do the same for obesity through targeted drug delivery. This study investigated the use of peptide-functionalized quantum dots (QDs) for the imaging of prohibitin (PHB)-expressing cells in vitro and in diet-induced obese rats, which could potentially be used as nanocarriers of antiobesity drugs. Methods Cadmium (Cd)-based QDs were functionalized with an adipose homing peptide (AHP) and injected intravenously into lean and obese Wistar rats. Biodistribution of the QDs was analyzed by an IVIS® Lumina XR imaging system and inductively coupled plasma optical emission spectroscopy (ICP-OES). For in vitro studies, PHB-expressing (Caco-2 and MCF-7) and non-PHB-expressing (KMST-6 and CHO) cells were exposed to either unfunctionalized QDs (QD625) or AHP-functionalized QDs (AHP-QD625) and analyzed by fluorescence microscopy. Results AHP-QD625 accumulated significantly in PHB-expressing cells in vitro when compared with non-PHB-expressing cells. In vivo data indicated that QD625 accumulated mainly in the reticuloendothelial system (RES) organs, while the AHP-QD625 accumulated mostly in the white adipose tissues (WATs). Conclusion AHP-functionalized QDs were successfully and selectively delivered to the PHB-expressing cells in vitro (Caco-2 and MCF-7 cells) and in the WAT vasculature in vivo. This nanotechnology-based approach could potentially be used for dual targeted drug delivery and molecular imaging of adipose tissues in obese patients in real time.
Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.
ESTIMATING A SYSTEM OF RECREATION DEMAND FUNCTIONS USING A SEEMINGLY UNRELATED POISSON REGRESSION APPROACH

In this article, a seemingly unrelated Poisson regression model is presented as an alternative to using Zellner's seemingly unrelated regression model for estimating a system of recreation demand functions. The seemingly unrelated Poisson regression model provides estimates that are asymptotically more efficient than equation-by-equation Poisson estimates and circumvents the bias and inconsistency problems that result when using A. Zellner's seemingly unrelated regression model. Additionally, the seemingly unrelated Poisson regression model is applied to an empirical problem dealing with the value of recreational boating and the findings indicate that the seemingly unrelated regression model consumer surplus estimates are substantially different from those of the seemingly unrelated Poisson regression model. Copyright 1994 by MIT Press.
STaT: a three-question screen for intimate partner violence.

OBJECTIVE To develop a simple, sensitive screening tool for lifetime intimate partner violence (IPV) in women. METHODS In the emergency department (ED) of an urban teaching hospital, 75 English-speaking women between 18 and 64 years old were tested. The sensitivity and specificity of 43 dichotomous response-option, IPV screening questions were compared. The reference standard was lifetime IPV, determined by a semistructured interview, using preset criteria of specific violence acts or emotional control or both by an intimate partner. Questions with high sensitivity (>70%) were tested together against the reference standard. Answering yes to any question scored 1 point. Content area and the area under receiver-operator curve (AUROC) of the screening questions determined the final tool. RESULTS Sixty-three percent of women reported lifetime IPV. Eight of the 43 screening questions had a sensitivity of >70%. After testing possible combinations, the final three-question screen, STaT had the highest AUROC. The STaT questions are: "Have you ever been in a relationship where your partner has pushed or Slapped you? " "Have you ever been in a relationship where your partner Threatened you with violence? " and "Have you ever been in a relationship where your partner has thrown, broken or punched Things? " The sensitivity (95% confidence intervals [95% CI]) of STaT for lifetime IPV is 96% (90%, 100%), 89% (81%, 98%), and 64% (50%, 78%) for a score of >/=1, >/=2, and 3, respectively. The corresponding specificity is 75% (59%-91%), 100%, and 100%. CONCLUSIONS Three simple questions when used together can effectively identify lifetime IPV and will aid clinicians' efforts to identify abuse in women.
Learning polynomial feedforward neural networks by genetic programming and backpropagation

This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search technique relying on the genetic programming paradigm, and second, further adjustment of the best discovered network weights by an especially derived backpropagation algorithm for higher order networks with polynomial activation functions. These two stages of the PFNN learning process enable us to identify networks with good training as well as generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.
Immigration and Leisure-Time Physical Inactivity: A population-based study

Objective. To investigate the relationship between migration status and sedentary leisure-time physical activity status in the city of Malmö, Sweden. Methods. The public health survey in 1994 is a cross-sectional study. A total of 5600 individuals aged 20-80 completed a postal questionnaire. The response rate was 71%. The population was categorized according to country of birth. Multivariate analysis was performed using a logistic regression model to investigate the importance of possible confounders for the differences in sedentary leisure-time physical activity status. Results. The prevalence of a sedentary leisure-time physical activity status was 18.1% among men and 26.7% among women. The odds ratio of a sedentary leisure-time physical activity status was significantly higher among men born in Arabic-speaking countries, in All other countries, and among women born in Yugoslavia, Poland, Arabic-speaking countries, and the category 'all other countries', compared to the reference group born in Sweden. The multivariate analysis including age, sex, and education did not alter these results. Conclusion. There were significant ethnic differences in leisure-time physical activity status. This is a CVD risk factor that could be affected by intervention programs aimed at specific ethnic subgroups of the population.
Emotional Feature Extraction Based On Phoneme Information for Speech Emotion Recognition

Emotion interaction is an important issue in human robot interaction (HRI). In this paper, we focused on emotion recognition via voice. Generally emotional speech recognition used emotional speech features which are varied as emotion change. However, the emotional speech features changed by not only emotion information but also phoneme information. Therefore we should consider phoneme information when the feature is extracted. So we proposed emotional feature extraction method with considering phoneme information. At first, we evaluated several features such as pitch, energy and formant which are generally used in emotion recognition. Secondly, the features were categorized into emotion reflective features and phoneme dominant features. Finally, emotion reflective features were extracted based on same phoneme information which was classified by phoneme dominant features. This method extracted features which were more sensitive to emotion but less sensitive to phoneme.
Interchange Fees in Payment Card Systems: A Survey of the Literature

This paper surveys the recent literature about interchange fees in payment card systems. Interchange fees are used by payment platforms to allocate the total cost of a payment card transaction between the cardholder's bank, the issuer, and the merchant's bank, the acquirer. Each time a consumer pays by card, its bank receives an interchange fee from the merchants' bank. Banks argue that interchange fees are needed to encourage the use of electronic payment instruments, whereas merchants claim that they artificially inflate the cost of accepting cards. This paper sheds light on the ongoing debate that opposes banks to the regulatory institutions or the competition authorities in various countries, by reviewing the theoretical results of the literature and highlighting some unanswered issues.
The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality

Recent research has shown that machine learning systems, including state-of-the-art deep neural networks, are vulnerable to adversarial attacks. By adding to the input object an imperceptible amount of adversarial noise, it is highly likely that the classifier can be tricked into assigning the modified object to any desired class. It has also been observed that these adversarial samples generalize well across models. A complete understanding of the nature of adversarial samples has not yet emerged. Towards this goal, we present a novel theoretical result formally linking the adversarial vulnerability of learning to the intrinsic dimensionality of the data. In particular, our investigation establishes that as the local intrinsic dimensionality (LID) increases, 1-NN classifiers become increasingly prone to being subverted. We show that in expectation, a k-nearest neighbor of a test point can be transformed into its 1-nearest neighbor by adding an amount of noise that diminishes as the LID increases. We also provide an experimental validation of the impact of LID on adversarial perturbation for both synthetic and real data, and discuss the implications of our result for general classifiers.
An Analysis of Participation in Nonconsumptive Wildlife Recreation

One of the roles of recreation economics in public policy is to demonstrate the magnitude and economic value of outdoor activities which do not register their importance through market transactions. In pursuing this research, economists can now argue the economic value of harvesting wildlife stocks for recreation purposes; for example, salmon fishing (Brown et al. 1965), duck hunting (Hammack and Brown 1974), and deer hunting (Gum and Martin 1975). There has been relative neglect of the nonconsumptive uses of wildlife such as bird-watching, wildlife watching, and wildlife photography. The impact of public policy on wildlife abundance and nonconsumptive wildlife recreation is determined by the responsiveness of users to changes in publicly controlled natural resources, such as habitat and stocks of wildlife. The purpose of this paper is to contribute to an understanding of the nature and quantity of nonconsumptive wildlife recreation.
Successful preemptive surgical division of type 2-congenital extrahepatic portosystemic shunt in children.

PURPOSE A congenital extrahepatic portosystemic shunt (CEPS) is a rare abnormality. The shunts are classified into 2 types. Of these, a type 2-shunt is a side-to-side one, which may be treated by a simple shunt division. The aim of this retrospective study was to clarify the effects of a surgical shunt division on 4 children with type 2-CEPS. PATIENTS Between June 2002 and June 2008, 4 children with type 2-CEPS underwent a surgical shunt division. Various clinical factors of each patient, including shunt types, shut ratios evaluated by portal scintigraphy using (123)I-iodoamphetamine, serum levels of ammonia and total bile acids before and after surgery were evaluated. FINDINGS Two children had a conventional open surgery and the other two had a laparoscopic surgery. The serum levels of ammonia as well as total bile acids of these children decreased significantly to the normal levels within a month after the surgical shunt divisions. All the children had a better clinical course. CONCLUSIONS A shunt division, especially by laparoscopic surgery, is an effective therapy for type 2-CEPS. To the best of our knowledge by reviewing literatures, our cases are the youngest ones treated by laparoscopic shunt division.