Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of aging on growth hormone and prolactin release after growth hormone-releasing hormone and domperidone in man.

Growth hormone (GH) and prolactin (PRL) secretion after GH-releasing hormone (GHRH) and domperidone (DOM), an antidopaminergic drug which does not cross the blood-brain barrier (BBB), was evaluated in 8 healthy elderly men (65-91 years) and in 7 young adults (23-40 years). All received in random order at 2-day intervals: GHRH(1-40) (50 micrograms i.v.) bolus, DOM (5 mg/h) infusion, GHRH(1-40) (50 micrograms i.v.) plus DOM (5 mg/h i.v.), saline solution. In elderly men GH increase after GHRH was significantly lower than in young men. DOM alone did not change GH secretion in either of these groups, whereas it increased the GH response to GHRH only in young adults. PRL levels increased in both young and elderly men during both DOM and GHRH plus DOM, but the PRL release was more marked in young than in elderly men. Both integrated secretion of GH after GHRH and of PRL after DOM were inversely correlated to chronological age. Our data show an impairment of GH rise after GHRH and of PRL after DOM in elderly adults. It is also stressed that peripheral blockade of dopamine receptors by DOM is unable to amplify the GH response to GHRH only in elderly men. A reduction in GH release after GHRH might be related to aging, perhaps through a reduction of dopaminergic tonus.
Errors on Space Software Requirements: A Field Study and Application Scenarios

This paper presents a field study on real errors found in space software requirements documents. The goal is to understand and characterize the most frequent types of requirement problems in this critical application domain. To classify the software requirement errors analyzed we initially used a well-known existing taxonomy that was later extended in order to allow a more thorough analysis. The results of the study show a high rate of requirement errors (9.5 errors per each 100 requirements), which is surprising if we consider that the focus of the work is critical embedded software. Besides the characterization of the most frequent types of errors, the paper also proposes a set of operators that define how to inject realistic errors in requirement documents. This may be used in several scenarios, including: evaluating and training reviewers, estimating the number of requirement errors in real specifications, defining checklists for quick requirement verification, and defining benchmarks for requirements specifications.
Characterization of potential donor acceptor pairs for polymer solar cells by ESR, optical, and electrochemical investigations

In this report new PPV-PPE copolymers DE 69, DE 11 were compared with the state of the art materials MDMO-PPV and poly(3-alkylthiophenes) (P3DDT, P3OT). The optical band gap energy of the two copolymers DE 69, DE11 is somewhat higher than that one of MDMO-PPV. The electrochemical band gap was found to be lower for DE 69, DE11 related to for MDMO-PPV. The absorption coefficient of the new PPV-PPE copolymers is higher than for MDMO-PPV but in the same order of magnitude. Films from composites of MDMO-PPV/PCBM and DE69 or DE11 with PCBM show a clear photoluminescence quenching effect. At 77 K two kinds of LESR signals were identified, one of polarons (P+.) and one of radical anions of fullerenes. The LESR results show strong differences in kinetics between the separation and recombination processes of photoexcited charge carriers. The relaxation rates of paramagnetic centers were estimated by microwave power saturation experiments. Photovoltaic devices were prepared under ambient conditions on flexible PET-ITO foils with MDMO-PPV/ PCBM (1:3 wt. %) with ηAM1. 5 = 2.4% and DE 69/ PCBM (1:3 wt. %) with ηAM1. 5 = 1.75% (A=0.25 cm2, Pin = 100 mW/cm2).
A fully dynamic algorithm for maintaining the transitive closure

This paper presents an efficient fully dynamic graph algorithm for maintaining the transitive closure of a directed graph. The algorithm updates the adjacency matrix of the transitive closure with each update to the graph; hence, each reachability query of the form "Is there a directed path from i to j? " can be answered in O(1) time. The algorithm is randomized and has a one-sided error; it is correct when answering yes, but has O(1/nc) probability of error when answering no, for any constant c. In acyclic graphs, worst case update time is O(n2). In general graphs, the update time is O(n2.26). The space complexity of the algorithm is O(n2).
CURRENT APPROACHES TO SEASONAL-TO-INTERANNUAL CLIMATE PREDICTIONS

This review paper presents an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century. The discussion covers the full range of issues involved in climate forecasting, including (1) the theory and empirical evidence for predictability; (2) predictions of surface boundary conditions, such as sea surface temperatures (SSTs) that drive the predictable part of the climate; (3) predictions of the climate; and (4) a brief consideration of the application of climate forecasts. Within this context, the research of the coming decades that seeks to address shortcomings in each area is described. Copyright © 2001 Royal Meteorological Society.
Antidepressant Response in Major Depressive Disorder: A Meta-Regression Comparison of Randomized Controlled Trials and Observational Studies

Background To compare response to antidepressants between randomized controlled trials (RCTs) and observational trials. Methods and Findings Published and unpublished studies (from 1989 to 2009) were searched for by 2 reviewers on Medline, the Cochrane library, Embase, clinicaltrials.gov, Current Controlled Trial, bibliographies and by mailing key organisations and researchers. RCTs and observational studies on fluoxetine or venlafaxine in first-line treatment for major depressive disorder reported in English, French or Spanish language were included in the main analysis. Studies including patients from a wider spectrum of depressive disorders (anxious depression, minor depressive episode, dysthymia) were added in a second analysis. The main outcome was the pre-/post-treatment difference on depression scales standardised to 100 (17-item or 21-item Hamilton Rating Scale for Depression or Montgomery and Åsberg Rating Scale) in each study arm. A meta-regression was conducted to adjust the comparison between observational studies and RCTs on treatment type, study characteristics and average patient characteristics. 12 observational studies and 109 RCTs involving 6757 and 11035 patients in 12 and 149 arms were included in the main analysis. Meta-regression showed that the standardised treatment response in RCTs is greater by a magnitude of 4.59 (2.61 to 6.56). Study characteristics were related to standardised treatment response, positively (study duration, number of follow-up assessments, outpatients versus inpatients, per protocol analysis versus intention to treat analysis) or negatively (blinded design, placebo design). At patient level, response increased with baseline severity and decreased with age. Results of the second analysis were consistent with this. Conclusions Response to antidepressants is greater in RCTs than in observational studies. Observational studies should be considered as a necessary complement to RCTs.
Five Withanolides from the Leaves of Datura metel L. and Their Inhibitory Effects on Nitric Oxide Production

Four new withanolides named dmetelins A–D (compounds 1–4), along with the known compound 7α,27-dihydroxy-1-oxo-witha-2,5,24-trienolide (5) were isolated from the leaves of Datura metel L. (Solanaceae). Their structures were elucidated on the basis of detailed analysis of 1D and 2D NMR and mass spectrometry data. All the compounds were evaluated for their inhibitory effects on lipopolysaccharide (LPS)-induced nitric oxide (NO) production in RAW 264.7 cells. Compounds 1, 4 and 5 showed significant inhibitory activities, and compounds 2 and 3 showed moderate inhibitory activities with IC50 values of 17.8, 11.6, 14.9, 33.3 and 28.6 μM, respectively.
Efficient Inference in Persistent Dynamic Bayesian Networks

Numerous temporal inference tasks such as fault monitoring and anomaly detection exhibit a persistence property: for example, if something breaks, it stays broken until an intervention. When modeled as a Dynamic Bayesian Network, persistence adds dependencies between adjacent time slices, often making exact inference over time intractable using standard inference algorithms. However, we show that persistence implies a regular structure that can be exploited for efficient inference. We present three successively more general classes of models: persistent causal chains (PCCs), persistent causal trees (PCTs) and persistent polytrees (PPTs), and the corresponding exact inference algorithms that exploit persistence. We show that analytic asymptotic bounds for our algorithms compare favorably to junction tree inference; and we demonstrate empirically that we can perform exact smoothing on the order of 100 times faster than the approximate Boyen-Koller method on randomly generated instances of persistent tree models. We also show how to handle non-persistent variables and how persistence can be exploited effectively for approximate filtering.
Meeting behavior detection in smart environments: Nonverbal cues that help to obtain natural interaction

Unobtrusive and multiple-sensor interfaces enable observation of natural human behavior in smart environments. Being able to detect, analyze and interpret this activity allows for the implementation of various applications, including real-time surveillance and real-time support in smart home and office environments. We focus on smart meeting environments, where nonverbal behavioral cues sometimes tell more about issues such as discussion participation, involvement and contribution, than information obtained from verbal contributions. An important aspect of this behavior is the interaction between meeting participants. We regard the special case where some of the participants are at physically different locations, which hinders natural interaction. We discuss how we can exploit the ability of a sensor-equipped environment to detect nonverbal interaction cues and use these to allow and improve natural interaction between collaborating participants at distributed locations. We focus on research efforts to detect nonverbal interaction cues by looking at various modalities. Also, we discuss how information obtained from the fusion of these modalities allows us to generate and display behavioral cues that allow remote participants to take part in a distributed meeting in a natural way.
CORAL - Control, Relations and Logic

CORAL is a modular declarative query language/programming language that supports general Horn clauses with complex terms, set-grouping, aggregation, negation, and relations with tuples that contain (universally quantified) variables. Support for persistent relations is provided by using the EXODUS storage manager. A unique feature of CORAL is that it provides a wide range of evaluation strategies and allows users to optionally tailor execution of a program through high-level annotations. A CORAL program is organized as a collection of modules, and this structure is used as the basis for expressing control choices. CORAL has an interface to C++, and uses the class structure of C++ to provide extensibility. FinaUy, CORAL supports a command sublanguage, in which statements are evaluated in a user-specified order. The statements can be queries, updates, production-system style rules, or any command that can be typed in at the CORAL
Process Modeling as a Basis for Auditing Information Privacy

Information privacy has become an important task for every data processing organization. To meet its demands, organizations apply privacy-enhancing technologies and identity management to their business processes. But the increasing number of privacy breaches shows that this task is complex and not well understood. In this position paper, a formal method for modeling an proving information privacy within a process model is envisioned. Such a model would allow an integration at process design, increase the understanding and effectiveness of the privacy protection mechanisms, and enable compliance checks and data protection auditing. Acknowledgment: This research work was funded by the Federal Ministry of Education and Research of the Federal Republic of Germany (Funding-Id: 01|S08016B). 1 Information Privacy in Business Processes With the shift from a paper-based to a digital information society, collection and processing of data have become much easier and very fast. Without the proper carefulness, information easily can be used for purposes they are neither intended for nor permitted to. This problem is documented by the increasing number of incidents regarding data leakages, missuses of personal data and even identity thefts. Like in the latest incident, where a secret file of the US-Army was found by a civilian on an second-hand mp3-player, often the reason is the missing awareness in using non-confidential communication channels. This results in loss of control on information flow and enables data misuse and theft. A predictable and comprehensible information flow could avoid such incidents or even aids investigation. We call this information privacy. Reasonable information privacy requires at least reconcilement of privacy and accountability of a data subject’s electronic interaction (empowering the data subject in its self-determination) [CSS05] and a privacy-aware identity and information lifecycle management within the data processing organizations (including compliance, data protection, and access control) [MB07]. Both depend on the underlying business processes and the involved information privacy techniques. 1In January 2009, the New Zealand news channel One News gets possession of a 60 page long file containing name and contact information of US-Soldiers, details on equipment, and other secret information. The file was stored on an mp3-player bought at a second-hand shop by a civilian.
Modeling Ship Behaviors as an Intelligent Agent for Collision Avoidance Simulation

Ship is modeled as an intelligent agent for collision avoidance simulation and studies in this paper. The ship intelligent agent (SIA) architecture is designed and seven basic units are proposed, they are perception unit, memory unit, communication unit, emotion unit, action unit, learning unit and thinking unit. Furthermore, the simulated environment model consisted by ship sailing environment and society environment, is also discussed.
The STRA6 Receptor Is Essential for Retinol-binding Protein-induced Insulin Resistance but Not for Maintaining Vitamin A Homeostasis in Tissues Other Than the Eye*

Background: STRA6 transports retinol into cells and activates cell signaling. Results: Ablation of Stra6 does not impair vitamin A homeostasis in tissues other than the eye but protects mice against RBP-induced insulin resistance. Conclusion: One major function of STRA6 is to control cell signaling. Significance: The data point at a new function for vitamin A and its blood carrier RBP. The plasma membrane protein STRA6 is thought to mediate uptake of retinol from its blood carrier retinol-binding protein (RBP) into cells and to function as a surface receptor that, upon binding of holo-RBP, activates a JAK/STAT cascade. It was suggested that STRA6 signaling underlies insulin resistance induced by elevated serum levels of RBP in obese animals. To investigate these activities in vivo, we generated and analyzed Stra6-null mice. We show that the contribution of STRA6 to retinol uptake by tissues in vivo is small and that, with the exception of the eye, ablation of Stra6 has only a modest effect on retinoid homeostasis and does not impair physiological functions that critically depend on retinoic acid in the embryo or in the adult. However, ablation of Stra6 effectively protects mice from RBP-induced suppression of insulin signaling. Thus one biological function of STRA6 in tissues other than the eye appears to be the coupling of circulating holo-RBP levels to cell signaling, in turn regulating key processes such as insulin response.
Hierarchical paracrine interaction of breast cancer associated fibroblasts with cancer cells via hMAPK-microRNAs to drive ER-negative breast cancer phenotype

Multiple juxtacrine and paracrine interactions occur between cancer cells and non-cancer cells of the tumor microenvironment (TME) that direct tumor progression. Cancer Associated Fibroblasts (CAFs) are an integral component of the TME, and the majority of breast tumor stroma is comprised of CAFs. Heterotypic interactions between cancer cells and non-cancer cells of the TME occur via soluble agents, including cytokines, hormones, growth factors, and secreted microRNAs. We previously identified a microRNA signature indicative of hyperactive MAPK signaling (hMAPK-miRNA signature) that significantly associated with reduced recurrence-free and overall survival. Here we report that the hMAPK-miRNA signature associates with a high metric of stromal cell infiltrate, and we investigate the role of microRNAs, particularly hMAPK-microRNAs, secreted by CAFs on estrogen receptor (ER) expression in breast cancer cells. ER-positive MCF-7/ltE2- cells were treated with conditioned media (CM) from CAFs derived from breast cancers of different PAM50 subtypes (CAFBAS, CAFHER2, and CAFLA). CAF CM isolated specifically from ER-negative primary breast tumors led to ER repression in vitro. Nanoparticle tracking analysis and transmission electron microscopy confirmed the presence of CAF-secreted exosomes in CM and the uptake of these exosomes by the ER+ MCF-7/ltE2- cells. Differentially expressed microRNAs in CAF CM as well as in MCF-7/ltE2- cells treated with this CM were identified. Knockdown of miR-221/222 in CAFBAS resulted in knockdown of miR221/222 levels in the conditioned media and the CM from CAFBAS; miR221/222 knockdown rescued ER repression in ER-positive cell lines treated with CAFBAS-CM. Collectively, our results demonstrate that CAF-secreted microRNAs are directly involved in ER-repression, and may contribute to the MAPK-induced ER repression in breast cancer cells.
Evolution under Fluctuating Environments Explains Observed Robustness in Metabolic Networks

A high level of robustness against gene deletion is observed in many organisms. However, it is still not clear which biochemical features underline this robustness and how these are acquired during evolution. One hypothesis, specific to metabolic networks, is that robustness emerges as a byproduct of selection for biomass production in different environments. To test this hypothesis we performed evolutionary simulations of metabolic networks under stable and fluctuating environments. We find that networks evolved under the latter scenario can better tolerate single gene deletion in specific environments. Such robustness is underlined by an increased number of independent fluxes and multifunctional enzymes in the evolved networks. Observed robustness in networks evolved under fluctuating environments was “apparent,” in the sense that it decreased significantly as we tested effects of gene deletions under all environments experienced during evolution. Furthermore, when we continued evolution of these networks under a stable environment, we found that any robustness they had acquired was completely lost. These findings provide evidence that evolution under fluctuating environments can account for the observed robustness in metabolic networks. Further, they suggest that organisms living under stable environments should display lower robustness in their metabolic networks, and that robustness should decrease upon switching to more stable environments.
Quantitative Data Cleaning for Large Databases

Data collection has become a ubiquitous function of large organizations – not only for record keeping, but to support a variety of data analysis tasks that are critical to the organizational mission. Data analysis typically drives decision-making processes and efficiency optimizations, and in an increasing number of settings is the raison d’etre of entire agencies or firms. Despite the importance of data collection and analysis, data quality remains a pervasive and thorny problem in almost every large organization. The presence of incorrect or inconsistent data can significantly distort the results of analyses, often negating the potential benefits of information-driven approaches. As a result, there has been a variety of research over the last decades on various aspects of data cleaning: computational procedures to automatically or semi-automatically identify – and, when possible, correct – errors in large data sets. In this report, we survey data cleaning methods that focus on errors in quantitative attributes of large databases, though we also provide references to data cleaning methods for other types of attributes. The discussion is targeted at computer practitioners who manage large databases of quantitative information, and designers developing data entry and auditing tools for end users. Because of our focus on quantitative data, we take a statistical view of data quality, with an emphasis on intuitive outlier detection and exploratory data analysis methods based in robust statistics [Rousseeuw and Leroy, 1987, Hampel et al., 1986, Huber, 1981]. In addition, we stress algorithms and implementations that can be easily and efficiently implemented in very large databases, and which are easy to understand and visualize graphically. The discussion mixes statistical intuitions and methods, algorithmic building blocks, efficient relational database implementation strategies, and user interface considerations. Throughout the discussion, references are provided for deeper reading on all of these issues.
2016 Ieee International Conference on Big Data (big Data) an Efficient Parallel Topic-sensitive Expert Finding Algorithm Using Spark

—Expert finding is an important technique to obtain the user authority ranking in community question answering (CQA) websites. ZhihuRank is a topic-sensitive expert finding algorithm, which is based on both LDA and PageRank. Currently, with the amount of participants and documents increasing rapidly in CQA websites, how to parallel expert finding algorithms for big data analysis has received significant attention. In this paper, we find that the Spark framework is more suitable for paralleling expert finding algorithms than the MapReduce framework, which is a memory-based parallel computing model to support complicated iterative algorithms. As an example, we parallel ZhihuRank using MLlib's LDA and GraphX's PageRank in Spark. Experiments have been conducted on large-scale real data from Zhihu 1 (the most popular CQA website in China). And the experimental results confirmed the effectiveness and scalability of our proposed approach.