Miracle-dz/UniFAR-FTUs huggingface dataset:
G Protein-Coupled Estrogen Receptor-Selective Ligands Modulate Endometrial Tumor Growth

Endometrial carcinoma is the most common cancer of the female reproductive tract. GPER/GPR30 is a 7-transmembrane spanning G protein-coupled receptor that has been identified as the third estrogen receptor, in addition to ERα and ERβ. High GPER expression is predictive of poor survival in endometrial and ovarian cancer, but despite this, the estrogen-mediated signaling pathways and specific estrogen receptors involved in endometrial cancer remain unclear. Here, employing ERα-negative Hec50 endometrial cancer cells, we demonstrate that GPER mediates estrogen-stimulated activation of ERK and PI3K via matrix metalloproteinase activation and subsequent transactivation of the EGFR and that ER-targeted therapeutic agents (4-hydroxytamoxifen, ICI182,780/fulvestrant, and Raloxifene), the phytoestrogen genistein, and the “ERα-selective” agonist propylpyrazole triol also function as GPER agonists. Furthermore, xenograft tumors of Hec50 cells yield enhanced growth with G-1 and estrogen, the latter being inhibited by GPER-selective pharmacologic antagonism with G36. These results have important implications with respect to the use of putatively ER-selective ligands and particularly for the widespread long-term use of “ER-targeted” therapeutics. Moreover, our findings shed light on the potential mechanisms of SERM/SERD side effects reported in many clinical studies. Finally, our results provide the first demonstration that pharmacological inhibition of GPER activity in vivo prevents estrogen-mediated tumor growth.
The Complexity of Generalized Satisfiability for Linear Temporal Logic

In a seminal paper from 1985, Sistla and Clarke showed that satisfiability for Linear Temporal Logic (LTL) is either NP-complete or PSPACE-complete, depending on the set of temporal operators used. If, in contrast, the set of propositional operators is restricted, the complexity may decrease. This paper undertakes a systematic study of satisfiability for LTL formulae over restricted sets of propositional and temporal operators. Since every propositional operator corresponds to a Boolean function, there exist infinitely many propositional operators. In order to systematically cover all possible sets of them, we use Post's lattice. With its help, we determine the computational complexity of LTL satisfiability for all combinations of temporal operators and all but two classes of propositional functions. Each of these infinitely many problems is shown to be either PSPACE-complete, NP-complete, or in P.
Dynamic bitmap index recompression through workload-based optimizations

Many large-scale read-only databases and data warehouses use bitmap indices in an effort to speed up data analysis. These indices have the dual properties of compressibility and being able to leverage fast bit-wise operations for query processing. Numerous hybrid run-length encoding compression schemes have been proposed that greatly compress the index and enable querying without the need to decompress. Typically, these schemes align their compression with the computer architecture's word size to further accelerate queries. Previously, we introduced Variable Length Compression (VLC), which uses a general encoding that can achieve better compression than word-aligned schemes. However, VLC's querying efficiency can vary widely due to mismatched alignment of compressed columns. In this paper, we present an optimizer which recompresses the bitmap over time. Based on query history, our approach allows the VLC user to specify the priority of compression versus query efficiency, then possibly recompress the bitmap accordingly. In an empirical study using scientific data sets, we showed that our approach was able to achieve both better compression ratios and query speedup over WAH and PLWAH. On the largest data set, our VLC optimizer compressed up to 1.73x better than WAH, and 1.46x over PLWAH. We also show a slight improvement in query efficiency in most experiments, while observing lucrative (11x to 16x) speedup in special cases.
S CUOLA N ORMALE S UPERIORE DI P ISA Classe di Scienze

We investigate the Cauchy problem for a system of the form atu = A(x)axu + f (t, x), where A(x) is a pseudosymmetric matrix with analytic entries = 1,. , N. We prove the well-posedness at each point xo where aij (xo) ~ (xo) = 0 for all i, j. In the case N = 3, it is sufficient to assume such a condition for i = j.
National Quail Symposium Proceedings National

The conceptualization of security of bobwhite during winter has been predicated on the assumption that winter ranges differ in quality, based on habitat structure, composition, or interspersion. Although some studies have qualitatively related habitat composition to survival, no studies have quantitatively linked habitat or landscape characteristics to winter survival and the speciﬁc structural or compositional characteristics that inﬂuence quality are unknown. To quantify winter habitat quality, we modeled hazards as a function of habitat characteristics in relation to winter survival of radio-marked bobwhite (2000, n = 118 in 16 coveys; 2001, n = 49 in 7 coveys) in a managed agricultural landscape in Mississippi, as a function of landscape structure and composition at 2 spatial scales (daily and seasonal ranges). For each spatial scale we constructed a priori models that estimated year-speciﬁc winter survival as a function of unique combinations of variables that characterized landscape composition and structure and had previously been identiﬁed as relevant to bobwhite ecology. At the spatial scale of winter ranges, the a priori model containing % of landscape, mean patch size, and edge density of linear herbaceous was the best approximating model and suggested a negative effect of linear herbaceous cover on survival. In retrospective analyses, models containing variables describing quantity and structure of linear herbaceous cover and cropland indicated that as these elements increased, risk of mortality increased. At the spatial scale of daily activity, metrics describing landscape structure and composition were poor predictors of survival. During this study, the quantity, patch size, amount of edge, or interspersion of patch types within the winter range or surrounding daily activity locations did not measurably inﬂuence the hazard function, suggesting that seasonal ranges can have different composition and structure, yet produce similar survival rates for the birds that inhabit each range.
News Articles Tag Categorization using Neural Networks and Natural Language Processing

: This research paper and its subsequent implementation is aimed at employing a combination of neural networks and natural language processing methodologies to successfully identify and categorize tags found in unmarked news articles and can be extended to other literary publications as well. These methodologies will then be applied onto the specific dataset containing a collection of news headlines associated with a brief description of the article along with other associated information. The result of this implementation will allow us to identify such tags in the future for unmarked articles. Unmarked news articles are becoming increasingly common these days and thus, make it even more difficult for the everyday user to identify and read forth articles that are of importance and of interest to him. In the present day, people are attracted to news articles based on their headlines primarily, and unmarked articles are a means of representation for unambiguous statements found not only in the headlines but also in the description of the article as well. Thus, the goal of this implementation is to successfully classify the tags found in both the headlines and description and to produce a clear representation regarding the category the article belongs under. Once the models have been successfully built, we will then evaluate their accuracy and hence determine their effectiveness for the use of similar datasets in the future. An extension of this paper can be used to identify the type of language being used in the articles in order to identify the different writing styles present. After the models have been successfully implemented their accuracy will be used to determine their effectiveness in predicting such similar datasets in the future. In this paper, the methods being used are Text based Convolutional Neural networks, Bidirectional Gated Research Unit (GRU) and a Long Short-Term Memory (LSTM) with Attention. The performance is then the topic of discussion based on how effectively the testing data is handled as visualized by the confusion matrices of the respective models.
Budget Transparency in Local Governments: An Empirical Analysis

Abstract The aim of this paper is to shed additional light on the determinants of budget transparency in local governments. Our work is based on a Likert-type survey questionnaire specifically designed to measure budget transparency in small municipalities. The questionnaire is based on the IMF's revised Code of Good Practices on Fiscal Transparency (2007). Results from 33 Galician municipalities are used to assess its internal consistency and to test a battery of hypotheses on the determinants of budget transparency. While several previous findings of the literature are confirmed, some new results are also obtained.
Legionella Contamination and Molecular Identification in Water Systems of the Public Facilities in Korea

With the increase in population visiting hot water systems such as spas and water parks, the opportunity to be exposed to the risk of legionellae’s infection is rising in Chungnam, Korea. So, we investigated Legionella contamination and its population on environmental sources for assessing the relationships in the types of facilities and water sources. PCR and culture method were used to detect and isolate Legionella spp. Partial 16s rRNA and rpoB gene sequences were used to identify the isolates and determine subspecies of Legionella pneumophila isolates. The detection rate (29%) of Legionella spp. from public bathes was the highest and L . pneumophila was the dominant species in Chungnam, Korea. L . pneumophila isolates used in this study were divided into subsp. pneumophila and subsp. fraseri . All of Legionella species isolated in this study except for L . nautarum were pathogenic to human. L . pneumophila isolates used in this study were divided into subsp. pneumophila and subsp. fraseri . Results of identification between rpoB and 16s rRNA trees were nearly consistent, except for some isolates. Our findings will be helpful to create a better understanding of the molecular basis of Legionella and further preventing and controlling outbreaks by legionellosis.
Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams

Abstract —A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms un der the windowed streaming model. Keywords —Frequent Itemsets, Weighted Support, Window Sliding, Weighted Support
Transparent adaptive parallelism on NOWs using OpenMP

We present a system that allows OpenMP programs to execute on a network of workstations with a variable number of nodes. The ability to adapt to a variable number of nodes allows a program to take advantage of additional nodes that become available after it starts execution, or to gracefully scale down when the number of available nodes is reduced. We demonstrate that the cost of adaptation is modest; the system allows a program to adapt at a moderate rate without much performance loss.Two ideas underlie the efficiency of our design. First, we recognize that OpenMP programs exhibit convenient adaptation points during their execution, points at which the cost of adaptation can be much reduced. Second, by allowing a process a certain grace period before it must leave a node, we insure that most adaptations can occur at these adaptation points, and thus at low cost. Migration of a process, a much more expensive method for providing adaptivity, is used only as a back-up solution, when the process cannot reach an adaptation point within the grace period.Our implementation consists of an OpenMP pre-processor that generates TreadMarks distributed shared memory (DSM) programs, and a version of TreadMarks modified to adapt to a variable number of nodes. Using a DSM as the underlying substrate facilitates the data (re-)distribution necessary after an adaptation.
Modeling short-term antidepressant responsiveness with artificial neural networks

correspondence: Po see chen Department of Psychiatry, hospital and college of Medicine, national cheng Kung University, Tainan, Taiwan Tel + 886 6-235-3535 ext 5190 Fax + 886 6-275-9259 email chenps@mail.ncku. edu.tw Abstract: Due to the varying nature of patient response to different types and even dosages of the same antidepressant, doctors currently prescribe antidepressants on a trial and error basis. Therefore, it is highly desirable, both clinically and economically, to establish tools that distinguish responders from non-responders and to predict possible outcomes of the antidepressant treatments. The overall effectiveness of treatment using antidepressants may thus be optimized. Common genetic polymorphisms, such as single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. In this work we developed a prediction model resulting from the analysis of clinical factors such as SNPs, age, baseline Hamilton Rating Scale for Depression (HAM-D) score, antidepressant groups, and gender of depression patients. We used it to predict the responsiveness of antidepressant treatment. By using candidate genes reported in the literature, we selected four SNPs that were strongly relevant to antidepressant efficacy. Our study population consisted of Taiwanese patients with major depression recruited from the National Cheng Kung University Hospital. The genotyping data was generated in the high-throughput genomics lab of Vita Genomics, Inc. With the wrapper-based feature selection approach, we employed multilayer feedforward neural network (MFNN) and logistic regression as a basis for comparisons. Our data revealed that the MFNN models were superior to the logistic regression model. The MFNN approach provides an efficient way to develop a tool for distinguishing responders from nonresponders prior to treatments. Our preliminary results showed that the MFNN algorithm is effective for deriving models for pharmacogenomics studies and for providing the link from clinical factors such as SNPs to the responsiveness of antidepressants in clinical association studies.
Quantitative reverse transcription-polymerase chain reaction to study mRNA decay: comparison of endpoint and real-time methods.

Four quantitative reverse transcription-PCR (RT-PCR) methods were compared to evaluate the time course of mRNA formation and decay. Mouse fibroblasts (NIH 3T3) transfected with the human beta-globin open reading frame/c-myc 3'-untranslated region chimeric gene under control of the c-fos promoter (fos-glo-myc) were used for serum-inducible transcription. The amount of fos-glo-myc mRNA, relative to beta-actin, was measured by quantitative, RT-PCR at various times following the addition of serum to serum-starved fibroblasts transfected with the chimeric gene. Both endpoint (band densitometry and probe hybridization) and real-time (SYBR green and TaqMan) PCR methods were used to assay the identical cDNA. The real-time methods produced a 4- to 5-log dynamic range of amplification, while the dynamic range of the endpoint assays was 1-log. The real-time and probe hybridization assays produced a comparable level of sensitivity that was considerably greater than band densitometry. The coefficient of variation from 22 replicate PCR reactions was 14.2 and 24.0% for the SYBR green and TaqMan detection, respectively, and 44.9 and 45.1% for the band densitometry and probe hybridization assays, respectively. The rank order for the values of r(2) obtained from the linear regression of the first-order mRNA decay plots was SYBR green > TaqMan > probe hybridization > band densitometry. Real-time PCR is more precise and displays a greater dynamic range than endpoint PCR. Among the real-time methods, SYBR green and TaqMan assays produced comparable dynamic range and sensitivity while SYBR green detection was more precise and produced a more linear decay plot than TaqMan detection.
Empirical analysis of rough and classical stochastic volatility models to the SPX and VIX markets1

We conduct an empirical analysis of rough and classical stochastic volatility models to the SPX and VIX options markets. Our analysis focusses primarily on calibration quality and is split in two parts. In part one, we perform a historical calibration to SPX options over the years 2004–2019 of a selection of models that include the one-factor rough Bergomi and rough Heston models. In part two, we consider three calibration dates with low, typical, and high volatility, examine a wide selection of models, and calibrate to both SPX options as well as jointly to SPX and VIX options. The key results are as follows: The rough Bergomi and rough Heston models fail to create a term structure of smile effect that is sufﬁciently pronounced for SPX options. Moreover, we discover that short-expiry SPX smiles generally are more symmetric than long-expiry smiles, a feature we neither ﬁnd that these models can reproduce. We propose an alternative volatility model driven by two Ornstein-Uhlenbeck processes that uses a non-standard transformation function. Calibrating it to SPX options, we obtain almost perfect ﬁts, and calibrating it jointly to SPX and VIX options, we obtain very decent ﬁts. This suggests, contrary to what one might be led to believe based on much of the existing literature, that the joint SPX-VIX calibration problem is largely solvable with classical two-factor volatility, all without roughness and jumps.
Identification of Polyketide Gene from Streptomyces roseoverticillatus and Its Antimicrobial Properties against Pathogenic Bacteria and Fungi

The aim of this study was isolation and characterization of antimicrobial metabolite producing actinomycetes from forest soil. A new actinomycete designated ERIAS-10 was selected based on antibacterial and antifungal activity in the primary screening. Biochemical, physiological, morphological and carbohydrate fermentation pattern of the isolate was characterized and confirmed that the isolate belong to Strepomyces species. The 16S rRNA gene sequence shared 100% sequence similarity to Streptomyces roseoverticillatus . The antimicrobial substances were extracted with organic solvents from the spent medium in which ERIAS-10 was cultivated. Antimicrobial activity was assessed using the broth micro dilution technique. The minimum inhibitory concentration (MIC) of the extracts against Enterococcus faecalis was 7.81 µg/mL and Aspergillus niger, A. flavus, A. clavatus and Fusarium oxysporum was 15.63 and 31.25 µg /mL respectively, whereas, Humicola grisea showed MIC at 150 µg/mL level. Cloning and sequence analysis of ketoacyl synthase gene revealed similarity to the type II polyketide synthase gene of Streptomyces species. The results showed that the antimicrobial potential of ERIAS-10 isolate could be suitable for pharmaceutical applications.
High performance field-weakening control algorithm of sensorless induction motor using speed adaptive full-order observer

In this paper, a novel voltage control scheme with a speed adaptive full-order observer is presented to improve the performance of sensorless induction motor control in the field-weakening region. The difference between the switching period time and the valid switching time of SVPWM (Space Vector Pulse Width Modulation) is introduced to regulate the field current, which maximizes the utilization of the dc-link voltage and extends the output torque. So when the motor speed exceeds the base speed, the corresponding magnetizing current would decrease with the increase of the motor speed. Meanwhile, a speed adaptive full-order observer is designed to obtain accurate rotor speed of the motor. The experiment results based on a 7.5 KW induction motor drive system show the validity of the proposed field-weakening method.
Profile-based dynamic voltage scheduling using program checkpoints

Dynamic voltage scaling (DVS) is a known effective mechanism for reducing CPU energy consumption without significant performance degradation. While a lot of work has been done on inter-task scheduling algorithms to implement DVS under operating system control, new research challenges exist in intra-task DVS techniques under software and compiler control. In this paper we introduce a novel intra-task DVS technique under compiler control using program checkpoints. Checkpoints are generated at compile time and indicate places in the code where the processor speed and voltage should be re-calculated. Checkpoints also carry user-defined time constraints. Our technique handles multiple intra-task performance deadlines and modulates power consumption according to a run-time power budget. We experimented with two heuristics for adjusting the clock frequency and voltage. For the particular benchmark studied, one heuristic yielded 63% more energy savings than the other. With the best of the heuristics we designed, our technique resulted in 82% energy savings over the execution of the program without employing DVS.
Optimal-sampling-inspired Self-Triggered control

Self-triggered control is an appealing sampling strategy that promises to preserve the same control performance as traditional (periodic) sampling, yet consuming less computing resource by executing the controller less frequently. While the stability of self-triggered controllers is often addressed, their capacity to actually reduce the cost is more difficult to be evaluated. Inspired by a recent result on the optimal density of the sampling instants that minimizes the control cost, in this paper we propose a new self-triggered control strategy. The proposed sampling rule is extremely simple and effective. Significant cost reductions compared to the optimal periodic controller are shown even with larger minimum intersample separation. Thanks to the simplicity of the sampling rule, we also implemented the proposed self-triggered controller over a physical plant. The experimental results are aligned with the theoretical ones.