LLukas22/scidocs huggingface dataset:
Multiple Molecular Pathways in Melanomagenesis: Characterization of Therapeutic Targets

Molecular mechanisms involved in pathogenesis of malignant melanoma have been widely studied and novel therapeutic treatments developed in recent past years. Molecular targets for therapy have mostly been recognized in the RAS-RAF-MEK-ERK and PI3K-AKT signaling pathways; small-molecule inhibitors were drawn to specifically target key kinases. Unfortunately, these targeted drugs may display intrinsic or acquired resistance and various evidences suggest that inhibition of a single effector of the signal transduction cascades involved in melanoma pathogenesis may be ineffective in blocking the tumor growth. In this sense, a wider comprehension of the multiple molecular alterations accounting for either response or resistance to treatments with targeted inhibitors may be helpful in assessing, which is the most effective combination of such therapies. In the present review, we summarize the known molecular mechanisms underlying either intrinsic and acquired drug resistance either alternative roads to melanoma pathogenesis, which may become targets for innovative anticancer approaches.
The Coping with Asthma Study: a randomised controlled trial of a home based, nurse led psychoeducational intervention for adults at risk of adverse asthma outcomes.

BACKGROUND
Morbidity and mortality associated with severe asthma might be reduced by interventions that address psychosocial factors contributing to adverse outcomes. A study was undertaken to assess the effectiveness of a 6 month home based psychoeducational intervention delivered by a respiratory nurse specialist for adults at risk of adverse asthma outcomes.


METHODS
A pragmatic randomised controlled trial was performed in 92 adults registered with hospital or primary care asthma clinics. All had previous hospital admissions and/or were on British Thoracic Society step 4-5 treatment and had failed to attend clinic appointments or were considered to have poor adherence to other aspects of their agreed management. Patients were visited in their homes for assessment and, where appropriate, intervention. The main outcomes measured were symptom control, asthma specific quality of life, and generic health status.


RESULTS
At the 6 month primary time point there were no significant differences between usual care and intervention groups in mean symptom control, physical functioning, or mental health scores (differences (with 95% CI) -0.35 (-1.83 to 1.13), 3.10 (-11.42 to 17.63), 0.42 (-10.22 to 11.07), respectively). Small effects on asthma specific quality of life up to 12 months (e.g. adjusted difference at 12 months 0.13 (95% CI 0.02 to 0.25)) and short term effects on generic health status, which mirrored improvements in aspects of self-care observed at the end of the intensive phase of the intervention, were apparent only from fully adjusted analyses.


CONCLUSIONS
A home based intervention provided by a nurse receiving psychological supervision may have effects on quality of life but is overall of limited long term benefit to adults at risk of adverse asthma outcomes.
Fast decorrelated neural network ensembles with random weights

Negative correlation learning (NCL) aims to produce ensembles with sound generalization capability through controlling the disagreement among base learners’ outputs. Such a learning scheme is usually implemented by using feed-forward neural networks with error back-propagation algorithms (BPNNs). However, it suffers from slow convergence, local minima problem and model uncertainties caused by the initial weights and the setting of learning parameters. To achieve a better solution, this paper employs the random vector functional link (RVFL) networks as base components, and incorporates with the NCL strategy for building neural network ensembles. The basis functions of the base models are generated randomly and the parameters of the RVFL networks can be determined by solving a linear equation system. An analytical solution is derived for these parameters, where a cost function defined for NCL and the well-known least squares method are used. To examine the merits of our proposed algorithm, a comparative study is carried out with nine benchmark datasets. Results indicate that our approach outperforms other ensembling techniques on the testing datasets in terms of both effectiveness and efficiency. Crown Copyright 2013 Published by Elsevier Inc. All rights reserved.
Efficient architecture of variable size HEVC 2D-DCT for FPGA platforms

This study presents a design of two-dimensional (2D) discrete cosine transform (DCT) hardware architecture dedicated for High Efficiency Video Coding (HEVC) in field programmable gate array (FPGA) platforms. The proposed methodology efficiently proceeds 2D-DCT computation to fit internal components and characteristics of FPGA resources. A four-stage circuit architecture is developed to implement the proposed methodology. This architecture supports variable size of DCT computation, including 4×4, 8×8, 16×16, and 32×32. The proposed architecture has been implemented in System Verilog and synthesized in various FPGA platforms. Compared with existing related works in literature, this proposed architecture demonstrates significant advantages in hardware cost and performance improvement. The proposed architecture is able to sustain 4K@30fps ultra high definition (UHD) TV real-time encoding applications with a reduction of 31-64% in hardware cost. Keywords—H.265/HEVC, two-dimensional discrete cosine transform (2D-DCT), FPGA platform, hardware architecture
Epigenetic polypharmacology: from combination therapy to multitargeted drugs

The modern drug discovery process has largely focused its attention in the so-called magic bullets, single chemical entities that exhibit high selectivity and potency for a particular target. This approach was based on the assumption that the deregulation of a protein was causally linked to a disease state, and the pharmacological intervention through inhibition of the deregulated target was able to restore normal cell function. However, the use of cocktails or multicomponent drugs to address several targets simultaneously is also popular to treat multifactorial diseases such as cancer and neurological disorders. We review the state of the art with such combinations that have an epigenetic target as one of their mechanisms of action. Epigenetic drug discovery is a rapidly advancing field, and drugs targeting epigenetic enzymes are in the clinic for the treatment of hematological cancers. Approved and experimental epigenetic drugs are undergoing clinical trials in combination with other therapeutic agents via fused or linked pharmacophores in order to benefit from synergistic effects of polypharmacology. In addition, ligands are being discovered which, as single chemical entities, are able to modulate multiple epigenetic targets simultaneously (multitarget epigenetic drugs). These multiple ligands should in principle have a lower risk of drug-drug interactions and drug resistance compared to cocktails or multicomponent drugs. This new generation may rival the so-called magic bullets in the treatment of diseases that arise as a consequence of the deregulation of multiple signaling pathways provided the challenge of optimization of the activities shown by the pharmacophores with the different targets is addressed.
Experiments with SVM to classify opinions in different domains

0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.05.070 ⇑ Corresponding author. Tel.: +34 953 212898; fax: E-mail address: msaleh@ujaen.es (M. Rushdi Saleh 1 http://www.amazon.com. 2 http://www.epinions.com. 3 http://www.imdb.com. Recently, opinion mining is receiving more attention due to the abundance of forums, blogs, e-commerce web sites, news reports and additional web sources where people tend to express their opinions. Opinion mining is the task of identifying whether the opinion expressed in a document is positive or negative about a given topic. In this paper we explore this new research area applying Support Vector Machines (SVM) for testing different domains of data sets and using several weighting schemes. We have accomplished experiments with different features on three corpora. Two of them have already been used in several works. The last one has been built from Amazon.com specifically for this paper in order to prove the feasibility of the SVM for different domains. 2011 Elsevier Ltd. All rights reserved.
Internet survey of Foley catheter practices and knowledge among Minnesota nurses.

BACKGROUND
Although nurses insert and care for many Foley catheters, little is known about nurses' attitudes and knowledge regarding indications for catheter use or methods to prevent catheter-associated urinary tract infection (CAUTI).


METHODS
An Internet-based survey was sent to a random sample of registered nurses (RNs) in Minnesota. The survey contained demographic questions and 5-point Likert-scale questions regarding indications for Foley catheter placement and effectiveness of various interventions for preventing CAUTI.


RESULTS
Nurses perceived early catheter removal as the most effective intervention to prevent CAUTI (mean score, 4.5; range 1-5). Compared with other RNs, those reporting additional catheter education were more likely to rate interventions to prevent CAUTI as effective, regardless of whether the interventions actually reduce the incidence of symptomatic CAUTI or asymptomatic bacteriuria/funguria. Intensive care unit RNs were significantly more likely than other RNs to endorse that a Foley catheter was indicated for any given clinical scenario and to endorse antimicrobial-coated catheters as effective in preventing CAUTI. Most respondents reported no institutional guidance regarding catheterization.


CONCLUSION
Although the surveyed Minnesota RNs demonstrated high-level awareness of the utility of early Foley catheter removal for preventing CAUTI, other aspects of their catheter-related knowledge were concerning. Improving these deficits may help improve catheter-related practice.
Hierarchical routing in multi-domain optical networks

Given the increasing adoption of DWDM networking technology, there is now a growing need to address distributed inter-domain lightpath provisioning issues. Although inter-domain provisioning has been well studied for packet/cell-switching networks, the wavelength dimension presents many additional challenges. To address this challenge, a detailed hierarchical routing GMPLS-based framework for provisioning all-optical and opto-electronic multi-domain DWDM networks is presented. The scheme adapts various topology abstraction schemes to improve routing scalability and lower inter-domain blocking probabilities. Related inter-domain lightpath RWA and signaling schemes are also tabled. Performance analysis results are also presented to demonstrate the effectiveness of the proposed mechanisms along with directions for future research work. 2006 Elsevier B.V. All rights reserved.
New media – new pleasures?

The discourse surrounding new media suggests that they offer experiences and pleasures which older media do not. This paper critically explores this proposition both theoretically and empirically in a small number of households in Ireland and in relation to digital games, DVDs and digital television. It argues that we can only understand the pleasures of new media texts through an examination of user experiences and by investigating the constraints and barriers which surround new media consumption in particular social and cultural contexts. It also suggests that insights from political economy of the media might be usefully employed within cultural studies.
Long-term depression in the nucleus accumbens: a neural correlate of behavioral sensitization to cocaine

A compelling model of experience-dependent plasticity is the long-lasting sensitization to the locomotor stimulatory effects of drugs of abuse. Adaptations in the nucleus accumbens (NAc), a component of the mesolimbic dopamine system, are thought to contribute to this behavioral change. Here we examine excitatory synaptic transmission in NAc slices prepared from animals displaying sensitization 10–14 days after repeated in vivo cocaine exposure. The ratio of AMPA (α-amino-3-hydroxy-5-methyl-4- isoxazole propionic acid) receptor- to NMDA (N-methyl-d-aspartate) receptor-mediated excitatory postsynaptic currents (EPSCs) was decreased at synapses made by prefrontal cortical afferents onto medium spiny neurons in the shell of the NAc. The amplitude of miniature EPSCs at these synapses also was decreased, as was the magnitude of long-term depression. These data suggest that chronic in vivo administration of cocaine elicits a long-lasting depression of excitatory synaptic transmission in the NAc, a change that may contribute to behavioral sensitization and addiction.
Architecture and measured characteristics of a cloud based internet of things

The Internet of Things (IoT) many be thought of as the availability of physical objects, or devices, on the Internet [1]. Given such an arrangement it is possible to access sensor data and control actuators remotely. Furthermore such data may be combined with data from other sources - e.g. with data that is contained in the Web - or operated on by cloud based services to create applications far richer than can be provided by isolated embedded systems [2,3]. This is the vision of the Internet of Things. We present a cloud-compatible open source controller and an extensible API, hereafter referred to as `IoTCloud', which enables developers to create scalable high performance IoT and sensor-centric applications. The IoTCloud software is written in Java and built on popular open source packages such as Apache Active MQ [4] and JBoss Netty [5]. We present an overview of the IoT Cloud architecture and describe its developer API. Next we introduce the FutureGrid - a geographically distributed and heterogeneous cloud test-bed [6,7] - used in our experiments. Our preliminary results indicate that a distributed cloud infrastructure like the FutureGrid coupled with our flexible IoTCloud framework is an environment suitable for the study and development of IoT and sensor-centric applications. We also report on our initial study of certain measured characteristics of an IoTCloud application running on the FutureGrid. We conclude by inviting interested parties to use the IoTCloud to create their own IoT applications or contribute to its further development.
Insight in schizophrenia: associations with empathy

Many people with schizophrenia (50–80 %) demonstrate impaired insight, something which has been associated with a poorer outcome. Two types of empathy can be distinguished: affective empathy via shared emotions and cognitive empathy, also referred to as Theory of Mind (ToM). ToM can be subdivided into cognitive ToM (knowledge about beliefs of other people via perspective taking) and affective ToM (knowledge about other people’s emotions via perspective taking). Recent studies show a relationship between Theory of Mind (ToM) and insight. However, the relationship between affective empathy and insight in schizophrenia was not examined previously. This was the aim of the present study. We expected that affective empathy would show a stronger relationship with insight than both cognitive and affective ToM. We assessed forty-six patients with a diagnosis of schizophrenia, and fifty-three healthy controls were assessed with a test battery consisting of tests of social cognition (a self-rating scale for affective empathy, a ToM task assessing both cognitive and affective ToM, and two tests of emotion perception), verbal memory, executive functioning, psychomotor speed, and intelligence. Insight was assessed with item G12 of the PANSS-interview. A regression equation showed that affective empathy made the strongest unique contribution to insight, followed closely by affective ToM. Together, they explained 45 % of the variance in insight. None of the other independent variables made a unique contribution to the prediction of insight. Both affective ToM and affective empathy are associated with insight in schizophrenia. Being able to take empathize with other peoples feeling at both the affective and cognitive level may enhance insight in schizophrenia.
RAPPORT A Cluster Algorithm for Graphs

A cluster algorithm for graphs called the Markov Cluster algorithm (MCL algorithm) is introduced. The algorithm provides basically an interface to an algebraic process de ned on stochastic matrices, called the MCL process. The graphs may be both weighted (with nonnegative weight) and directed. Let G be such a graph. The MCL algorithm simulates ow in G by rst identifying G in a canonical way with a Markov graph G1. Flow is then alternatingly expanded and contracted, leading to a row of Markov Graphs G(i). Flow expansion corresponds with taking the k power of a stochastic matrix, where k 2 IN . Flow contraction corresponds with a parametrized operator r, r 0, which maps the set of (column) stochastic matrices onto itself. The image rM is obtained by raising each entry in M to the r th power and rescaling each column to have sum 1 again. The heuristic underlying this approach is the expectation that ow between dense regions which are sparsely connected will evaporate. The invariant limits of the process are easily derived and in practice the process converges very fast to such a limit, the structure of which has a generic interpretation as an overlapping clustering of the graph G. Overlap is limited to cases where the input graph has a symmetric structure inducing it. The contraction and expansion parameters of the MCL process in uence the granularity of the output. The algorithm is space and time e cient and lends itself to drastic scaling. This report describes the MCL algorithm and process, convergence towards equilibrium states, interpretation of the states as clusterings, and implementation and scalability. The algorithm is introduced by rst considering several related proposals towards graph clustering, of both combinatorial and probabilistic nature. 2000 Mathematics Subject Classi cation: 05B20, 15A48, 15A51, 62H30, 68R10, 68T10, 90C35.
PROBLEMS OF EMPLOYABILITY-A STUDY OF JOB – SKILL AND QUALIFICATION MISMATCH

1.1 CONCEPT OF EMPLOYABILITY Employability has emerged as a major element in Indian labor market and has now become the most common topic of discussion. One of the main impediments in economic growth of India is the insufficient supply of quality skills. According to World Bank, 2009, one of the major constraints most industries in India face is the skills shortage. Each year over 3 million graduates and post-graduates are added to the Indian workforce. Of these, only 25 percent of technical graduates and 10-15 percent of regular graduates are considered employable by the industry (NASSCOM Report). In the current business environment, there is a need of education for employability. Many public and private training institutions focus on the development of key skills and work experience among most of the proficient and non-proficient workforces which enhance their employability. The industrial by the trainees is the matter which concern employers the most, as stated by Nation‟s Higher Educational Research Institution. Level of productivity can be increased by providing industrial training to employers.
Transformational leadership training programme for charge nurses.

AIM
This paper is a report of an evaluation of the effects of a transformational leadership training programme on Unit Charge Nurses' leadership practices.


BACKGROUND
Current healthcare regulations in the European Union and accreditation efforts of hospitals for their services mandate transformation in healthcare services in Turkey. Therefore, the transformational leadership role of nurse managers is vital in determining and achieving long-term goals in this process.


METHOD
The sample consisted of 30 Unit Charge Nurses with a baccalaureate degree and 151 observers at two university hospitals in Turkey. Data were collected using the Leadership Practices Inventory-Self and Observer (applied four times during a 14-month study process from December 2005 to January 2007). The transformational leadership training programme had theoretical (14 hours) and individual study (14 hours) in five sections. Means, standard deviations and percentages, repeated measure tests and two-way factor analysis were used for analysis.


RESULTS
According the Leadership Practices Inventory-Self and Observer ratings, leadership practices increased statistically significantly with the implementation of the programme. There were no significant differences between groups in age, length of time in current job and current position. The Unit Charge Nurses Leadership Practices Inventory self-ratings were significantly higher than those of the observers.


CONCLUSION
There is a need to develop similar programmes to improve the leadership skills of Unit Charge Nurses, and to make it mandatory for nurses assigned to positions of Unit Charge Nurse to attend this kind of leadership programme.
Issues in supply chain management

Successful supply chain management requires cross-functional integration and marketing must play a critical role. The challenge is to determine how to successfully accomplish this integration. We present a framework for supply chain management as well as questions for how it might be implemented and questions for future research. Case studies conducted at several companies and involving multiple members of supply chains are used to illustrate the concepts described. Choose an option to locate/access this article: Purchase Export
Explainable Recommendation: Theory and Applications

Although personalized recommendation has been investigated for decades, the wide adoption of Latent Factor Models (LFM) has made the explainability of recommendations a critical issue to both the research community and practical application of recommender systems. For example, in many practical systems the algorithm just provides a personalized item recommendation list to the users, without persuasive personalized explanation about why such an item is recommended while another is not. Unexplainable recommendations introduce negative effects to the trustworthiness of recommender systems, and thus affect the effectiveness of recommendation engines. In this work, we investigate explainable recommendation in aspects of data explainability, model explainability, and result explainability, and the main contributions are as follows: 1. Data Explainability: We propose Localized Matrix Factorization (LMF) framework based Bordered Block Diagonal Form (BBDF) matrices, and further applied this technique for parallelized matrix factorization. 2. Model Explainability: We propose Explicit Factor Models (EFM) based on phrase-level sentiment analysis, as well as dynamic user preference modeling based on time series analysis. In this work, we extract product features and user opinions towards different features from large-scale user textual reviews based on phrase-level sentiment analysis techniques, and introduce the EFM approach for explainable model learning and recommendation. 3. Economic Explainability: We propose the Total Surplus Maximization (TSM) framework for personalized recommendation, as well as the model specification in different types of online applications. Based on basic economic concepts, we provide the definitions of utility, cost, and surplus in the application scenario of Web services, and propose the general framework of web total surplus calculation and maximization.