Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronic activation of neurokinin-1 receptor induces pulmonary hypertension in rats

Chen, Li-Wen, Chau-Fong Chen, and Yih-Loong Lai. Chronic activation of neurokinin-1 receptor induces pulmonary hypertension in rats. Am. J. Physiol. 276 (Heart Circ. Physiol. 45): H1543–H1551, 1999. —In this study we explored the hypothesis that chronic activation of neurokinin-1 (NK-1) receptor induces pulmonary hypertension in Wistar rats. First, the activation of NK-1 receptor on the pulmonary circulation was investigated by use of a chronic injection of NK-1 agonist [Ser,Met(O2)]-substance P (1 3 1029 mol/kg) for 2 wk at sea level (rats breathed room air) and during hypoxia (rats were placed in a hypobaric 380-Torr chamber). Second, we studied the effect of NK-1 antagonist (CP-96345) on developing and developed (after 4 wk of chronic hypoxia) pulmonary hypertension. Pulmonary arterial pressure, the weight ratio of right ventricle to left ventricle 1 septum, hematocrit, and substance P (SP) were measured. We found that NK-1 agonist significantly increased pulmonary arterial pressure in the sea-level but not in the hypoxic group. However, NK-1 agonist induced neither right heart hypertrophy nor polycythemia. CP-96345 significantly decreased pulmonary arterial pressure in the hypoxic group but had no effect in the sea-level group. Furthermore, CP-96345 significantly attenuated the acute SP-induced increase in pulmonary arterial pressure in the sea-level and hypoxic groups, with a larger increase in the hypoxic group. These results suggest that chronic activation of NK-1 receptor induces pulmonary hypertension and that there is an increase in the sensitivity of pulmonary vessels in response to SP in chronically hypoxic rats.
Efficient Masquerade Detection Using SVM Based on Common Command Frequency in Sliding Windows

SUMMARY Masqueraders who impersonate other users pose serious threat to computer security. Unfortunately, ﬁrewalls or misuse-based intrusion detection systems are generally ine ﬀ ective in detecting masquer- aders. Anomaly detection techniques have been proposed as a comple-mentary approach to overcome such limitations. However, they are not accurate enough in detection, and the rate of false alarm is too high for the technique to be applied in practice. For example, recent empirical studies on masquerade detection using UNIX commands found the accuracy to be below 70%. In this research, we performed a comparative study to inves- tigate the e ﬀ ectiveness of SVM (Support Vector Machine) technique using the same data set and conﬁguration reported in the previous experiments. In order to improve accuracy of masquerade detection, we used command frequencies in sliding windows as feature sets. In addition, we chose to ignore commands commonly used by all the users and introduce the concept of voting engine. Though still imperfect, we were able to improve the accuracy of masquerade detection to 80.1% and 94.8%, whereas previous studies reported accuracy of 69.3% and 62.8% in the same conﬁgurations. This study convincingly demonstrates that SVM is useful as an anomaly detection technique and that there are several advantages SVM o ﬀ ers as a tool to detect masqueraders.
Efficient diversification of search results using query logs

We study the problem of diversifying search results by exploiting the knowledge mined from query logs. Our proposal exploits the presence of different "specializations" of queries in query logs to detect the submission of ambiguous/faceted queries, and manage them by diversifying the search results returned in order to cover the different possible interpretations of the query. We present an original formulation of the results diversification problem in terms of an objective function to be maximized that admits the finding of an optimal solution in linear time.
Lowering mechanical degradation of drag reducers in turbulent flow

Drag reduction (DR) agents are used in several ppm concentrations to accelerate significantly the flow through conduits in oil pipelines, oil well operations, flood water disposal, fire fighting, field irrigation, transport of suspensions and slurries, sewage systems, water heating and cooling systems, airplane tank filling, marine systems, and also in biomedical systems including blood flow. The drag reduction agents are typically high molecular mass polymers; in industrial applications they undergo mechanical degradation in turbulent flow. We provide an equation that describes quantitatively the degradation, thus predicting drag reduction as a function of time and of the concentration of the drag reduction agent. We report how grafting a polymer on the backbone of a different polymer affects the drag reduction efficacy. Our grafted polymer undergoes degradation by flow turbulence more slowly and also provides high levels of drag reduction efficacy at much lower concentrations than homopolymers do.
THE EFFECT OF PROFESSIONAL IDENTIFICATION ON JOB ATTITUDES: A STUDY OF LAWYERS IN HONG KONG

This study examined the effect of professional identification on several job attitudes (i.e., job satisfaction, organizational commitment, and career satisfaction), and explored the moderating roles of gender and organizational tenure on these relationships. Informed by social identity theory, gender role theory, and organizational socialization theory, several hypotheses were developed and tested with a data set consisting of 309 salaried lawyers collected in Hong Kong. Regression analysis revealed that (1) professional identification had a significant positive effect on both job satisfaction and organizational commitment, (2) gender moderated the relationship between professional identification—job satisfaction and professional identification—organizational commitment, and (3) organizational tenure moderated the relationship between professional identification and job satisfaction as well as the relationship between professional identification and career satisfaction. Theoretical and practical implications are discussed.
Lumenal peroxisomal protein aggregates are removed by concerted fission and autophagy events

We demonstrated that in the yeast Hansenula polymorpha peroxisome fission and degradation are coupled processes that are important to remove intra-organellar protein aggregates. Protein aggregates were formed in peroxisomes upon synthesis of a mutant catalase variant. We showed that the introduction of these aggregates in the peroxisomal lumen had physiological disadvantages as it affected growth and caused enhanced levels of reactive oxygen species. Formation of the protein aggregates was followed by asymmetric peroxisome fission to separate the aggregate from the mother organelle. Subsequently, these small, protein aggregate-containing organelles were degraded by autophagy. In line with this observation we showed that the degradation of the protein aggregates was strongly reduced in dnm1 and pex11 cells in which peroxisome fission is reduced. Moreover, this process was dependent on Atg1 and Atg11.
Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion Lexicon

Sentiment analysis aims to uncover emotions conveyed through information. In its simplest form, it is performed on a polarity basis, where the goal is to classify information with positive or negative emotion. Recent research has explored more nuanced ways to capture emotions that go beyond polarity. For these methods to work, they require a critical resource: a lexicon that is appropriate for the task at hand, in terms of the range of emotions it captures diversity. In the past, sentiment analysis lexicons have been created by experts, such as linguists and behavioural scientists, with strict rules. Lexicon evaluation was also performed by experts or gold standards. In our paper, we propose a crowdsourcing method for lexicon acquisition, which is scalable, cost-effective, and doesn't require experts or gold standards. We also compare crowd and expert evaluations of the lexicon, to assess the overall lexicon quality, and the evaluation capabilities of the crowd.
How are we measuring clinically important outcome for operative treatments in sports medicine?

ABSTRACT Objectives: Minimal clinically important difference (MCID) and other measures of minimum clinical importance are increasingly recognized as important clinical considerations for evaluating the efficacy of an intervention. As our interpretation of clinical outcome evolves beyond statistical significance, psychometric properties such as MCID will be increasingly important to various stakeholders in the orthopaedic community. The purpose of this study was to: 1) describe the state of clinically important outcome reporting and 2) describe the methods used to derive these psychometric values for sports medicine patients undergoing operative treatments. Methods: A review of the MEDLINE database was performed. Studies primarily deriving and reporting clinically important outcome measures for operative interventions in sports medicine were included. Demographic, methodological and psychometric properties of included studies were extracted. Level of Evidence and the Newcastle Ottawa Scale (NOS) were used to assess study quality. Statistical analysis was primarily descriptive. Results: Fifteen studies met inclusion criteria; 10 of the 15 studies were Level II evidence and mean NOS score was 5.3/9. Minimal detectable change (MDC) was the most commonly derived measure of clinical importance, calculated in 53.3% of studies, followed by MCID, calculated in 40.0% of studies. A combination of distribution and anchor-based methods was the most commonly used method to determine clinical importance (N = 7, 46.7%) followed by distribution only (N = 5, 33.3%). Predictors of clinically important change were reported in four studies and were most commonly related to pre-operative functional score. Conclusions: MDC and the MCID are the most commonly reported measures of clinically important outcome after operative treatment in sports medicine. A combination of both distribution and anchor-based methods is commonly used to derive these values. More attention should be paid to reporting outcomes that are clinically important and developing guidelines for reporting clinical meaningful outcome.
CASE STUDIES IN MODEL-BASED DIAGNOSIS AND FAULT ANALYSIS OF CAR-SUBSYSTEMS

The paper presents objectives and results of a series of case studies in computer support for diagnosis, failure mode and effects analysis, and the creation of repair manuals in the domain of automotive systems. Model-based prediction and diagnosis reflect the requirements of these tasks. More specifically, qualitative models of system components are necessary for both capturing the available knowledge and achieving the desired coverage and granularity of the analysis results. We describe models for parts of the anti-lock braking system (ABS) the electronic diesel control (EDC), and a controlled electric motor. The summarized results of the case study demonstrate the necessity and utility of qualitative models for the successful application of automated diagnosis to industrial problems.
Idiopathic restless legs syndrome: abnormalities in central somatosensory processing.

OBJECTIVES Neurophysiological studies have shown an impairment of temperature perception in secondary and idiopathic restless legs syndrome (RLS). It is unclear whether these deficits are caused by peripheral nerve fibre damage or by central impairment of somatosensory processing. The aim of the present study was (1) to determine the frequency of thermal hypaesthesia in a large population of secondary and idiopathic RLS patients; (2) to differentiate between a peripheral and central disturbance of somatosensory processing and (3) to correlate these findings with the clinical manifestation of the disease. METHODS From the results of clinical examination, nerve conduction studies and blood samples the patients were divided into secondary and idiopathic RLS groups. The severity of RLS symptoms was assessed by standardized questionnaires. Quantitative sensory testing (QST) assessing temperature perception was performed in all patients. The peripheral function of small nerve fibres was evaluated by the quantitative nociceptor axon reflex test (QNART). RESULTS 22 secondary and 20 idiopathic RLS patients participated in the study. Impairment of temperature perception (QST) was found in 72% of the secondary RLS patients and in 55% of idiopathic RLS patients. The peripheral C-fibre function (QNART) was normal in idiopathic RLS patients. In contrast it was significantly impaired in secondary RLS patients compared with idiopathic RLS patients and age matched controls. There was no correlation between the results obtained in QST and clinical scores. CONCLUSION Impairment of temperature perception is present in a high percentage of RLS patients. In secondary RLS the sensory deficits are at least in part caused by small fibre neuropathy. In idiopathic RLS a functional impairment of central somatosensory processing is present.
Entanglement distillation by extendible maps

It is known that from entangled states that have positive partial transpose it is not possible to distill maximally entangled states by local operations and classical communication (LOCC). A long-standing open question is whether maximally entangled states can be distilled from every state with a non-positive partial transpose. In this paper we study a possible approach to the question consisting of enlarging the class of operations allowed. Namely, instead of LOCC operations we consider k-extendible operations, defined as maps whose Choi-Jamio lkowski state is k-extendible. We find that this class is unexpectedly powerful - e.g. it is capable of distilling EPR pairs even from completely product states. We also perform numerical studies of distillation of Werner states by those maps, which show that if we raise the extension index k simultaneously with the number of copies of the state, then the class of k-extendible operations are not that powerful anymore and provide a better approximation to the set of LOCC operations.
The impact of strategy and maturity on business analytics and firm performance: A review and research agenda

Business analytics involves interpreting organizational data to improve decision-making and to optimise business processes. It has the potential to improve firm performance and increase competitive advantage. Although many case studies have been reported that describe business analytics applications and speculate about how they might contribute to firm performance, there is no clearly articulated and theoretically grounded model in the literature. This paper proposes a theoretical framework for understanding how and why business analytics technology and capabilities can lead to value-creating actions that lead to improved form performance and competitive advantage. We focus particularly on how strategy and maturity impact business analytics and firm performance. A number of propositions are developed from the framework and a research agenda for empirical evaluation and enhancement of the framework is proposed.
Analytics, Innovativeness, and Innovation Performance

Based on organizational information processing theory, this paper develops and tests a research model to deepen the understanding about the conditions under which the use of data analytics contributes to innovation performance. This paper suggests that firm innovativeness, as an organization cultural concept, should moderate the relationship between data analytics use and innovation performance. The results of a moderation analysis based on data from cross-sectional survey support this account. The findings indicate a significant inversely U-shaped effect of innovativeness on the relationship between data analytics use and innovation performance. The effect of data analytics use on innovation performance is strongest under medium levels of innovativeness but comparatively weaker when firms have a low or a high level of innovativeness. These insights contribute to the IS literature by clarifying the important role of firm cultural factors in shaping information needs and deployment of information processing
Prediction of functional outcome in individuals at clinical high risk for psychosis.

IMPORTANCE A major public health concern associated with schizophrenia and psychotic disorders is the long-term disability that involves impaired cognition, lack of social support, and an inability to function independently in the community. A critical goal of early detection and intervention studies in psychosis is therefore to understand the factors leading to this often profound impairment. OBJECTIVE To develop a predictive model of functional (social and role) outcome in a clinical high-risk sample for psychosis. DESIGN Prospective, naturalistic, longitudinal 3- to 5-year follow-up study. SETTING The Recognition and Prevention Program in New York, a research clinic located in the Zucker Hillside Hospital in New York. PARTICIPANTS One hundred one treatment-seeking patients at clinical high risk for psychosis. Ninety-two (91%) were followed up prospectively for a mean (SD) of 3 (1.6) years. INTERVENTION Neurocognitive and clinical assessment. MAIN OUTCOMES AND MEASURES The primary outcome variables were social and role functioning at the last follow-up visit. RESULTS Poor social outcome was predicted by reduced processing speed (odds ratio [OR], 1.38; 95% CI, 1.050-1.823; P = .02), impaired social functioning at baseline (OR, 1.85; 95% CI, 1.258-2.732; P = .002), and total disorganized symptoms (OR, 5.06; 95% CI, 1.548-16.527; P = .007). Reduced performance on tests for verbal memory (OR, 1.74; 95% CI, 1.169-2.594; P = .006), role functioning at baseline (OR, 1.34; 95% CI, 1.053-1.711; P = .02), and motor disturbances (OR, 1.77; 95% CI, 1.060-2.969; P = .03) predicted role outcome. The areas under the curve for the social and role prediction models were 0.824 (95% CI, 0.736-0.913; P < .001) and 0.77 (95% CI, 0.68-0.87; P < .001), respectively, demonstrating a high discriminative ability. In addition, poor functional outcomes were not entirely dependent on the development of psychosis, because 40.3% and 45.5% of nonconverters at clinical high risk had poor social and role outcomes, respectively. CONCLUSIONS AND RELEVANCE Results from this study support the increasing emphasis on functional decline as a critically important outcome that parallels conversion to psychosis and suggest that both psychosis and long-term functional disability are equally important targets for prevention. Reduced neurocognitive performance, functional impairments, and nonpositive attenuated symptoms at baseline were associated with an increased risk of poor functional outcomes in our sample. Poor functional outcomes were not entirely dependent on positive symptoms and the development of psychosis, further highlighting the need for intervention at this early stage of development for those who do and do not convert to a full-blown psychotic disorder.
Understanding Acquisition Performance: The Role of Transfer Effects

Drawing on work from transfer theory at the individual unit of analysis, this study examines positive and negative transfer effects in organization acquisitions. Data from 43 organizations reveal that, consistent with theories on positive transfer of industry knowledge, similar acquisitions are positively related to acquisition performance. In addition, hypotheses on the negative transfer effects of past acquisition knowledge are borne out: relative to first acquisitions, second acquisitions are found to be negatively associated with acquisition performance. These findings indicate that the application of transfer theory to strategic issues at the organization unit of analysis may provide new insights.
Ranking Multidocument Event Descriptions for Building Thematic Timelines

This paper tackles the problem of timeline generation from traditional news sources. Our system builds thematic timelines for a general-domain topic defined by a user query. The system selects and ranks events relevant to the input query. Each event is represented by a one-sentence description in the output timeline. We present an inter-cluster ranking algorithm that takes events from multiple clusters as input and that selects the most salient and relevant events. A cluster, in our work, contains all the events happening in a specific date. Our algorithm utilizes the temporal information derived from a large collection of extensively temporal analyzed texts. Such temporal information is combined with textual contents into an event scoring model in order to rank events based on their salience and query-relevance.
An efficient depth-adjustment deployment scheme for underwater wireless sensor networks

Underwater Wireless Sensor Networks (UWSNs) have attracted significant research attention because of the broad application prospects. Unlike a terrestrial network, an underwater sensor network is a three-dimensional (3-D) network and it has peculiar characteristics such as low available bandwidth, large propagation delays, limited energy, etc. Also, complexity of the underwater environment brings about new challenges in node deployment issue. In this paper, a depth-adjustment deployment scheme based on Voronoi diagrams is proposed. It aims to maximize the sensing coverage and behave well in the network lifetime. The nodes are initially deployed on the surface of the water randomly and they can only move in vertical direction in 3-D space. Based on Voronoi diagrams, we find out the scheduling nodes whose disappearance will not decrease the sensing coverage in the current plane. Then let the scheduling nodes move vertically towards to the determined depth. As a result, our scheme divides the target region into different layers and the whole deployment process is simplified. Simulation experiments turn out that our scheme has a good performance in network sensing coverage and connectivity, also it outperforms random and optimal node deployment in the network lifetime.
A multiobjective simulation optimization approach to define teams of workers in stochastic production systems

In this paper, we address team configuration problems in manufacturing systems, which consist in defining the number of workers to be assigned to a production system, as well as the skills that each worker must have in order to meet several performance measures. This problem is studied in a stochastic production context. A multi-objective evolutionary algorithm is connected to a simulation model to deal with this problem. Two objectives are considered. The first one is the minimization of the expected manpower cost associated to manufacturing team and the second one is the minimization of the expected mean flow time of jobs. Machines redundancy and workers multi-functionality are considered, when defining workers skills, to cope with possible random events such as workers unavailability and bottlenecks. Since the way workers are assigned to work centers strongly impact the results, a recent adaptive assignment heuristic is embedded in the simulation model and its parameters are also optimized. The proposed multi-objective simulation optimization approach is applied to design manufacturing teams, of a job shop production system, using the Nondominated Sorting Genetic Algorithm II (NSGA-II) connected to a simulation model developed using Arena. The set of non dominated solutions is found, so that an additional multi-criteria analysis can be performed.