Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanism of activation of a response regulator: interaction of NtrC-P dimers induces ATPase activity

NtrC is the transcriptional activator for nitrogen-regulated promoters and, as a response regulator, belongs to the protein family of two-component systems. The activity of all response regulators is modulated by phosphorylation of the conserved N-terminal receiver domain. Phosphorylation of the dimeric NtrC has two consequences: (i) a strong increase in the cooperative binding of NtrC to two adjacent binding sites and (ii) activation of NtrC as an ATPase. Here we show that phosphorylation of NtrC is not sufficient for activation of NtrC. At low protein concentrations (50 nM), phosphorylated NtrC was only active as an ATPase upon cooperative binding to DNA. At high protein concentrations (above 50 nM), NtrC was active in the absence of DNA, and activation occurred in parallel with the formation of high-molecular-weight aggregates. We infer that activation of NtrC involves an interaction between two NtrC-P dimers and proceeds in two steps. The first step is the phosphorylation of NtrC. The second step is the interaction between two NtrC-P dimers. This interaction induces the conformational change in NtrC-P to the active conformation.
Firm Communication and Investor Response: A Framework and Discussion Integrating Social Media

Abstract I provide a general framework of firms’ financial communication process and investor response to information, moving from disclosure through dissemination to investor response and management response. I then discuss the entrance of social media into firm communications, highlighting both classic and unique aspects of social media in the communication process. I place Cade (2018) in this literature and discuss areas ripe for future research. Finally, I encourage researchers interested in social media to acknowledge and embrace the unique opportunities and challenges in this area.
Protein body formation in leaves of Nicotiana benthamiana: a concentration-dependent mechanism influenced by the presence of fusion tags.

Protein bodies (PBs) are endoplasmic reticulum (ER) derived organelles originally found in seeds whose function is to accumulate seed storage proteins. It has been shown that PB formation is not limited to seeds and green fluorescent protein (GFP) fused to either elastin-like polypeptide (ELP) or hydrophobin (HFBI) fusion tags induce the formation of PBs in leaves of N. benthamiana. In this study, we compared the ELP- and HFBI-induced PBs and showed that ELP-induced PBs are larger than HFBI-induced PBs. The size of ELP- and HFBI-induced PBs increased over time along with the accumulation levels of their fused protein. Our results show that PB formation is a concentration-dependent mechanism in which proteins accumulating at levels higher than 0.2% of total soluble protein are capable of inducing PBs in vivo. Our results show that the presence of fusion tags is not necessary for the formation of PBs, but affects the distribution pattern and size of PBs. This was confirmed by PBs induced by fluorescent proteins as well as fungal xylanases. We noticed that in the process of PB formation, secretory and ER-resident molecules are passively sequestered into the lumen of PBs. We propose to use this property of PBs as a tool to increase the accumulation levels of erythropoietin and human interleukin-10 by co-expression with PB-inducing proteins.
Single feed compact quad-band PIFA antenna for wireless communication applications

In this paper, a novel compact single feed quad-band planar inverted F-antenna (PIFA) is presented. Two techniques to reduce the physical size are illustrated. First, we insert U-shaped slits within the antenna-radiating surface. This forces the current to flow around the obstacles hence, reducing the resonant frequency. This technique reduces the size by about 30% from the original PIFA. Second, a capacitive plate is loaded between the radiating surface and the ground plane. The size is reduced more to reach 55% of the original. The relation between the capacitance load value and the antenna size reduction ratio is studied. Using different U-shaped slits at different appropriate positions, multiple (dual, tri, and quad) band capabilities are realized. The four center frequencies are chosen to lie within the GSM band, DCS band, Bluetooth (IEEE802.11a) ISM band, and WLAN (IEEE802.11b) band, respectively. Foam dielectric substrate is used for rigid structure and easy shielding purposes. The reduced size and thin substrate thickness (h<0.15/spl lambda//sub 0/) allow the design to be compatible with wireless and portable communication systems. Experimental measurements verify the design and simulation criteria.
The Effect of Individual Factors on the Medication Error.

Medication error is a major issue in healthcare industry and significant efforts have been taken in recent years to comprehend factors that influence errors in medication. Therefore, the present study aims to examine individual factors that contribute to medication errors as perceived by nurses. 255 registered nurses working in different Jordanian public hospitals have been chosen as samples to collect the study data from. They were asked to complete a questionnaire to assess the perceived individual factors, specifically, on nursing mathematical calculation skills and training as well as knowledge on medication treatment as factors contributing to medication errors. The current study found that the nurses' mathematical calculation skills, training and their knowledge on medication treatment have significant relationship with medication error. This was proven as the study framework is able to explain 45.6% of the total variance. Consequently, it is recommended that healthcare authorities and hospitals in Jordan should focus on nursing knowledge in medication treatment and the nurses' ability to perform drug calculation in order to improve the medication system in Jordan.
Dynamic balance and level of lesion in spinal cord injured patients.

This study investigated differences in the dynamic balance of patients with different levels of spinal cord injury (SCI) as well as the intraclass correlation coefficient (ICC) of the modified functional reach test (MFRT). Twenty non-standing Spinal Cord Injured patients (13 males and 7 females) with mean age of 43.15 years (S.D = 10.03 years) participated in the study. They were categorised into three groups based on the level of spinal cord lesion. Group 1 comprised of 8 subjects with C5-T1, quadriplegia, group 2 comprised of 6 subjects with T6-T8 paraplegia and group 3 comprised of 6 subjects with T10-L1 paraplegia. The dynamic balance of all subjects was assessed using the modified functional reach test. The data were summarized using descriptive statistics of mean and standard deviation with further analysis using ANOVA and Pearson product correlation coefficient. The subjects with paraplegia showed higher functional reach (dynamic balance) than the quadriplegics but there was no significant difference in the functional reach values among the three groups. The ICC for MFRT was high and ranged from 0.981 to 0.992. The result of this study therefore seems to suggest that the MFRT cannot be used to classify patients with different levels of SCI.
Feature extraction for nonintrusive load monitoring based on S-Transform

The electric energy demand is dramatically growing worldwide and demand reduction emerges as an outstanding strategy; it implies detailed information about the electricity consumption, namely load disaggregation. Typical automatic methods for load disaggregation require high hardware efforts to install one sensor per appliance, whereas Non-intrusive Load Monitoring (NILM) systems diminish the hardware efforts through signal processing and mathematical modeling. One approach to NILM systems is to model the load signatures via artificial intelligence. This paper proposes to employ S-Transform for the feature extraction stage and Support Vector Machines for the pattern recognition problem. Several experiments are presented and the results of the feature extraction with S-Transform and Wavelet Packet Transform are compared. Thus promising feature vectors based on S-Transform are presented with similar or superior performance than the approach based on Wavelet Packet Transform.
Estimating Risk-Return Relations with Analysts Price Targets

Asset pricing tests often replace ex ante return expectation with ex post realization. The large deviation between the two drastically weakens the power of these tests. This paper proposes to use analysts consensus price target for a stock as the market expectation of the stock’s future price to directly construct the stock’s expected excess return. Analyzing the expected excess return behavior both over time and across different stocks shows that classic asset pricing theory works much better on ex ante return expectations than on ex post realizations. The analysis also provides new insights on the pricing of common equity risk factors.
Towards statistical comparison and analysis of models

Model comparison is an important challenge in model-driven engineering, with many application areas such as model versioning and domain model recovery. There are numerous techniques that address this challenge in the literature, ranging from graph-based to linguistic ones. Most of these involve pairwise comparison, which might work, e.g. for model versioning with a small number of models to consider. However, they mostly ignore the case where there is a large number of models to compare, such as in common domain model/metamodel recovery from multiple models. In this paper we present a generic approach for model comparison and analysis as an exploratory first step for model recovery. We propose representing models in vector space model, and applying clustering techniques to compare and analyse a large set of models. We demonstrate our approach on a synthetic dataset of models generated via genetic algorithms.
Research on influence factor of system efficiency and power loss factor for DC power supply system of data center

This paper focus on energy efficiency comparison and analysis of different DC power distribution mode for large data center. First, several single-module DC power supply systems with different voltage level and power capacity are applied to substitute for UPS based AC distribution mode for a typical data center of high-tech park with many enterprises. Secondly, system efficiency, power supply loss factor(PLF) and power usage effectiveness(PUE) are used respectively to analyze and evaluate the energy efficiency of power supply system with IT equipment. Then a simulation model is developed with MATLAB/SIMULINK platform to compare the simulation results for different voltage level and capacity of battery supply. These results of energy saving analysis are quantified and shown with the calculation and analysis on system efficiency and power factor of PLF. Finally, the efficiency of a data center is measured for different power supply, the tested data is in agreement with the simulation.
Texture-based detection of lung pathology in chest radiographs using local binary patterns

This paper presents a method that employs texture-based feature extraction and Support Vector Machines (SVM) to classify chest abnormal radiograph patterns namely pleural effusion, pnuemothorax, cardiomegaly and hyperaeration. A similar previous attempt prototyped the classification system that achieved 97% and 87.5% accuracy for pleural effusion and pneumothorax using histogram values, while attaining 70% and 73.33% for cardiomegaly and hyperaeration using image processing schemes. In this work, we aimed to increase the performance in classifying the said lung patterns, specifically for cardiomegaly and hyperaeration. Using texture-based features, the developed system was able to achieve accuracies of 96% and 99% with sensitivities of 97% and 100% for the cardiomegaly and hyperaeration cases, respectively.
Machine Learning for Structured Finance

Machine learning and artificial intelligence have evolved beyond simple hype and have integrated themselves in business and in popular conversation as an increasing number of smart applications profoundly transform the way we work and live. This article defines machine learning in terms of potential benefits and pitfalls for a nontechnical audience, and gives examples of popular and powerful machine learning algorithms: k-means clustering, principal component analysis, and artificial neural networks. Three important philosophical challenges of machine learning are introduced: the no free lunch theorem, the curse of dimensionality, and the bias–variance trade-off.
Synergistic interaction between metformin and sulfonylureas on diclofenac-induced antinociception measured using the formalin test in rats.

BACKGROUND There is evidence that biguanides and sulfonylureas block diclofenac-induced antinociception (DIA) in rat models. However, little is known about the interaction between these hypoglycemics with respect to DIA. OBJECTIVE To determine whether metformin-sulfonylurea combinations affect DIA during the formalin test. METHODS Rats received the appropriate vehicle or diclofenac before 1% formaldehyde was injected into the paw. Rats were also pretreated with vehicle, glibenclamide, glipizide, metformin or glibenclamide⁄metformin and glipizide⁄metformin combinations before the diclofenac and formaldehyde injections, and the effect on antinociception was assessed. Isobolograms of the combinations were constructed to test for a synergistic interaction. RESULTS Systemic injection of diclofenac resulted in antinociception during the second phase of the test. Systemic pretreatment with the combinations of glibenclamide (0.56 mg⁄kg to 10 mg⁄kg)⁄metformin (10 mg⁄kg to 180 mg⁄kg) and glipizide (0.56 mg⁄kg to10 mg⁄kg)⁄metformin (10 mg⁄kg to 180 mg⁄kg) blocked DIA. The derived theoretical effective doses for 50% of subjects (ED50) for the glibenclamide⁄metformin and glipizide⁄metformin combinations were 32.52 mg⁄kg and 32.42 mg⁄kg, respectively, and were significantly higher than the actual observed experimental ED50 values (7.57 mg⁄kg and 8.43 mg⁄kg, respectively). CONCLUSION Pretreatment with glibenclamide, glipizide or metformin blocked DIA in a dose-dependent manner, and combining either sulfonylurea with metformin produced even greater effects. The observed ED50s for the combinations were approximately fourfold lower than the calculated additive effects. These data indicate that sulfonylureas interact to produce antagonism of DIA. Combination therapy is a common second-line treatment for patients with diabetes and metabolic syndrome, a group that experiences pain from multiple sources. The results suggest that at least some anti-inflammatory agents may not be effective in this group.
Explanatory Power of Accounting Variables in Determining Share Price: The Case of Cement Industry of Pakistan

This paper studies the impact of various variables on the share price of the cement sector companies in Pakistan. The financial performance of 6 out of 18 firms has been examined over a ten year period, taking changes in equity, net income, net sales, dividends and financial leverage as dependent variables and share price of the company as the independent variable. Relationship of each variable with the share price has been analyzed through ordinary least square method using eviews statistical application. After conducting the analysis on the different variables it can be concluded that dividends, net profit and net sales of Attock Cement, Financial leverage of Cherat Cement and net profit and book value of Kohat Cement are significantly effecting the share price of the stocks, whereas other variables have negligible impact on the firm’s share price. Keywords: Share Price, Accounting Variables, Pakistan Cement Industry
Population‐based case–control study of mebendazole in pregnant women for birth outcomes

ABSTRACT The objectives of the study was to check the embryotoxic‐teratogenic and fetotoxic effect of mebendazole (Vermox; Richter, Budapest, Hungary) treatment during pregnancy. Mebendazole use during pregnancy was evaluated in mothers of babies born with congenital abnormalities and in matched control mothers of babies born without congenital abnormalities in the population‐based data set of the Hungarian Case–Control Surveillance of Congenital Abnormalities between 1980 and 1996.
Application of Differential Evolution Algorithm for Transient Stability Constrained Optimal Power Flow

Consideration of transient stability constraints in optimal power flow (OPF) problems is increasingly important because modern power systems tend to operate closer to stability boundaries due to the rapid increase of electricity demand and the deregulation of electricity markets. Transient stability constrained OPF (TSCOPF) is however a nonlinear optimization problem with both algebraic and differential equations, which is difficult to be solved even for small power systems. This paper develops a robust and efficient method for solving TSCOPF problems based on differential evolution (DE), which is a new branch of evolutionary algorithms with strong ability in searching global optimal solutions of highly nonlinear and nonconvex problems. Due to the flexible properties of DE mechanism, the hybrid method for transient stability assessment, which combines time-domain simulation and transient energy function method, can be employed in DE so that the detailed dynamic models of the system can be incorporated. To reduce the computational burden, several strategies are proposed for the initialization, assessment and selection of solution individuals in evolution process of DE. Numerical tests on the WSCC three-generator, nine-bus system and New England ten-generator, 39-bus system have demonstrated the robustness and effectiveness of the proposed approach. Finally, in order to deal with the large-scale system and speed up the computation, DE is parallelized and implemented on a Beowulf PC-cluster. The effectiveness of the parallel DE approach is demonstrated by simulations on the 17-generator, 162-bus system.
Rationality of Natural Gas Prices and the Determining Factors in China

ABSTRACT The rationality of natural gas price has a significant impact on a nation’s energy-saving strategies, emission reduction, and therefore the overall economy. This article examines the rationality of the natural gas pricing strategies in China through an investigation of the natural gas price distortions in domestic and international markets and the natural gas price determining factors using a Bayesian structural equation model. It is found that there are significant distortions in the industrial, residential, and commercial sectors compared to alternative energy sources. Further analysis suggests that these distortions could cause a “reverse substitution” in the domestic energy market. Compared to the international market, Chinese natural gas prices are found to be expensive and to lack price elasticity. The results also indicate that economic activities and demand are the most important natural gas price determinants, followed by supply and alternative fuel prices. This article provides empirical evidence to assist in natural gas pricing reforms in China and presents a basis for the design of reasonable energy pricing policies.
Entropieoptimierungsverfahren in der empirischen Wirtschaftsforschung/ Entropy Optimization in Empirical Economic Research

Im vorliegenden Beitrag wird ein neu entwickeltes numerisches Verfahren dargestellt. Es dient zur Disaggregierung von Daten und zur Ermittlung von Tabellen beliebiger Dimension aus unvollständigen Basisdaten. Beide Problemstellungen treten in der quantitativen Wirtschaftsforschung häufig auf, z.B. bei der Ermittlung von Input-Output-Tabellen zu den Produktionsverflechtungen einer Volkswirtschaft. Die vorhandenen statistischen Informationen reichen hier selbst bei „mittlerem" Aggregationsgrad meist nicht zur Erstellung einer kompletten Tabelle von Inputkoeffizienten aus1). Die in diesem Zusammenhang verbreitetste Methode zur Tabellenberechnung ist das sogenannte RAS-Verfahren, das mit dem aus den loglinearen Modellen bekannten „Iterative Proportional Fitting Algorithm" (IPF) identisch ist. Mit dieser Methode wird eine Tabelle berechnet, die in ihrer Struktur einer vorgegebenen möglichst „ähnlich" ist und deren Randsummen gleichzeitig vorgegebene Werte exakt annehmen, die von denen der Basistabelle abweichen. Das Verfahren wird u.a. dann angewendet, wenn für ein Basisjahr eine vollständige Tabelle vorliegt, während für die folgenden Jahre nur stärker aggregierte Information verfügbar ist. Das im folgenden dargestellte ENTROP-Verfahren ist eine Verallgemeinerung des RAS-Verfahrens mit erweiterten Möglichkeiten hinsichtlich der Vorgaben. Zusätzlich zu der Basistabelle und den Angaben über Randsummen können Informationen über die zu berechnende Tabelle berücksichtigt werden, die beliebige Teilgruppen von
Season is associated with Pseudomonas aeruginosa acquisition in young children with cystic fibrosis.

Pseudomonas aeruginosa, the principal respiratory pathogen in cystic fibrosis (CF) patients, is ubiquitous in the environment. Initial P. aeruginosa isolates in CF patients are generally environmental in nature. However, little information regarding seasonality of P. aeruginosa acquisition is available. We conducted a retrospective study to evaluate the seasonality of initial P. aeruginosa acquisition in young children with CF in the USA using the Cystic Fibrosis Foundation National Patient Registry from 2003 to 2009. Additionally, we assessed whether seasonal acquisition varied by climate zone. A total of 4123 children met inclusion criteria and 45% (n = 1866) acquired P. aeruginosa during a mean 2.0 years (SD 0.2 years) of follow up. Compared with winter, increased P. aeruginosa acquisition was observed in summer (incidence rate ratio (IRR): 1.22; 95% CI: 1.07-1.40) and autumn (IRR: 1.34; 95% CI: 1.18-1.52), with lower acquisition observed in spring (IRR: 0.81; 95% CI: 0.70-0.94). Seasonal variations in P. aeruginosa acquisition rates in the temperate and continental climate zones were similar to those in the overall cohort. In contrast, no significant seasonal effect was observed in the dry climate zone. In a corresponding analysis, no seasonal difference was observed in the rate of acquisition of Staphylococcus aureus, another common CF respiratory pathogen. These results provide preliminary support that climatic factors may be associated with initial P. aeruginosa acquisition in CF patients. Investigation and identification of specific risk factors, as well as awareness of seasonal variation, could potentially inform clinical recommendations including increased awareness of infection control and prevention strategies.