nils-herrmann/clean_scirepeval huggingface dataset:
Hepatic macrophages in homeostasis and liver diseases: from pathogenesis to novel therapeutic strategies
Venue: Cellular and Molecular Immunology

Macrophages represent a major cell type of innate immunity and have emerged as a critical player and therapeutic target in many chronic inflammatory diseases. Hepatic macrophages consist of Kupffer cells, which are originated from the fetal yolk sack, and infiltrated bone marrow derived monocytes/macrophages. Hepatic macrophages play a central role in maintaining homeostasis of the liver and in the pathogenesis of liver injury, making them an attractive therapeutic target for liver diseases. However, the various populations of hepatic macrophages display different phenotypes and exert distinct functions. Thus, more research is required to better understand these cells to guide the development of macrophage based therapeutic interventions. This review article will summarize the current knowledge on the origins and composition of hepatic macrophages, their functions in maintaining hepatic homeostasis, and their involvement in both promoting and resolving liver inflammation, injury, and fibrosis. Finally, the current strategies being developed to target hepatic macrophages for the treatment of liver diseases will be reviewed.
Irritable Bowel Syndrome: A Clinical Review.
Venue: Current rheumatology reviews

Symptoms of irritable bowel syndrome (IBS) are common in population studies including chronic abdominal pain associated with altered bowel habits. Patients often have associated gastrointestinal and somatic symptoms suggesting a possible common contributing mechanism, but the heterogeneous symptom patterns of individual patients make generalizations difficult. The pathophysiology of IBS is incompletely understood but includes disturbances of the brain gut axis. Central mechanisms are: the psychosocial history and environment, dysfunctional brain processing of peripheral signals attributed to the intestine including the enteric nervous system, the microbiome and the innate and adaptive immune system. As a result there is visceral hypersensitivity and disturbed intestinal secretory and motor activity. Some mechanisms of visceral pain hypersensitivity may overlap with other pain syndromes including fibromyalgia (FMS) Central Sensitization (CS) would offer a way to conceptualize an integration of life experience and psychologic response into a biopsychosocial framework of pathophysiology, diagnosis and treatment of IBS. Corticotropin releasing factor, a principle regulator in the stress and pain response may contribute to a neuroendocrine mechanism for the brain gut interaction. The positive diagnostic approach to IBS symptoms to avoid excess testing and enhance the patient provider therapeutic relationship requires the recognition of the "cluster" of IBS symptoms while identifying "alarm" symptoms requiring specific attention. The severity of the symptoms and other individual psychosocial factors characterize patients who seek medical care. The presence of significant psychosocial comorbidities adds to the complexity of management which often requires a multidisciplinary approach. Several treatment options exist but no single method is effective for all the symptoms of IBS. The therapeutic benefit of the well executed physician patient relationship is considered essential to success in managing IBS symptoms over the long term.
Design Of Analog Cmos Integrated Circuits

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Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter
Venue: Journal of medical Internet research

Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. There are several vaccines that protect against strains of HPV most associated with cervical and other cancers. Thus, HPV vaccination has become an important component of adolescent preventive health care. As media evolves, more information about HPV vaccination is shifting to social media platforms such as Twitter. Health information consumed on social media may be especially influential for segments of society such as younger populations, as well as ethnic and racial minorities.  The objectives of our study were to quantify HPV vaccine communication on Twitter, and to develop a novel methodology to improve the collection and analysis of Twitter data.  We collected Twitter data using 10 keywords related to HPV vaccination from August 1, 2014 to July 31, 2015. Prospective data collection used the Twitter Search API and retrospective data collection used Twitter Firehose. Using a codebook to characterize tweet sentiment and content, we coded a subsample of tweets by hand to develop classification models to code the entire sample using machine learning procedures. We also documented the words in the 140 character tweet text most associated with each keyword. We used chi square tests, analysis of variance, and nonparametric equality of medians to test for significant differences in tweet characteristic by sentiment.  A total of 193,379 English language tweets were collected, classified, and analyzed. Associated words varied with each keyword, with more positive and preventive words associated with "HPV vaccine" and more negative words associated with name brand vaccines. Positive sentiment was the largest type of sentiment in the sample, with 75,393 positive tweets (38.99% of the sample) followed by negative sentiment with 48,940 tweets (25.31% of the sample) Positive and neutral tweets constituted the largest percentage of tweets mentioning prevention or protection (20,425/75,393, 27.09% and 6477/25,110, 25.79% respectively) compared with only 11.5% of negative tweets (5647/48,940; P<.001) Nearly one half (22,726/48,940, 46.44% of negative tweets mentioned side effects, compared with only 17.14% (12,921/75,393) of positive tweets and 15.08% of neutral tweets (3787/25,110; P<.001) Conclusions Examining social media to detect health trends, as well as to communicate important health information, is a growing area of research in public health. Understanding the content and implications of conversations that form around HPV vaccination on social media can aid health organizations and health focused Twitter users in creating a meaningful exchange of ideas and in having a significant impact on vaccine uptake. This area of research is inherently interdisciplinary, and this study supports this movement by applying public health, health communication, and data science approaches to extend methodologies across fields.
Underexplored Identities: Attending to Asexuality in Therapeutic Contexts

Asexuality is an identity held by individuals who have low to no sexual desire or drive which can often be mistaken for hypoactive sexual desire disorder. This article highlights the key diagnostic and clinical differences between hypoactive sexual desire disorder and asexuality. It provides key information about the various typologies of asexuality. We describe the 4 phases of treatment: joining, clarifying definitions, challenging beliefs, and assumptions of socially sanctioned behavior, and developing a common or agreed upon definition of expression. A case vignette is included.
Gradient Based Multiobjective Optimization with Uncertainties

In this article we develop a gradient based algorithm for the solution of multiobjective optimization problems with uncertainties. To this end, an additional condition is derived for the descent direction in order to account for inaccuracies in the gradients and then incorporated into a subdivision algorithm for the computation of global solutions to multiobjective optimization problems. Convergence to a superset of the Pareto set is proved and an upper bound for the maximal distance to the set of substationary points is given. Besides the applicability to problems with uncertainties, the algorithm is developed with the intention to use it in combination with model order reduction techniques in order to efficiently solve PDE constrained multiobjective optimization problems.
A novel InSAR based off road positive and negative obstacle detection technique for unmanned ground vehicle
Venue: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Off road positive and negative obstacle detection is a challenge problem to be solved by unmanned ground vehicle. Traditional sensors, such as: optical camera, lidar and millimeter wave radar, have limited performance in off road environments, especially when obstacles are far away or covered by sparse grasses. We have proposed a forward looking InSAR sensor to tackle the problem and have built a rail based InSAR prototype. The forward looking InSAR can provide more information of harsh off road environments than existing unmanned ground vehicle (UGV) based radars. The forward looking InSAR can provide a scattering image, a coherence image and a digital terrain model (DTM) of the same scene ahead the radar during each scan. Each type of image can highlight some unique features of an obstacle. In this paper, an obstacle detection method is proposed by combining the shadow feature and the edge scattering feature. The principle method is close related to the scattering property difference between positive obstacles, negative obstacles and other objects. Positive obstacle feature large amplitude followed by low coherence area; while at the same time, negative obstacles feature low coherence area followed by large amplitude. Other objects don't have the unique feature. To mitigate false alarms, shadows are segmented in coherence images, and edge scattering features are extracted in scattering image. Firstly, the coherence image is converted into a binary image by applying a threshold. Shadow areas are roughly segmented as their coherences are low. Then the binary image is filtered by morphologic opening operation to eliminate small patches. Subsequently, an edge detection operation is applied to the filtered image. The edges of positive and negative obstacle are among the detected edge image. For each position of the detected edge, a cut is performed on the same position in the scattering image to extract a slice along the range direction. The judgment is formed by calculating the energy ratio between the near half slice of the farther half slice. Finally, positive and negative obstacles can be discriminated by comparing the judgment with two thresholds in an unsupervised fashion. We have conducted a field experiment on a ground covered by sparse grasses. A pit and a mound are deliberately built in the experiment scene. Experimental results have validated the proposed method.
Antiproton Flux, Antiproton to Proton Flux Ratio, and Properties of Elementary Particle Fluxes in Primary Cosmic Rays Measured with the Alpha Magnetic Spectrometer on the International Space Station.
Venue: Physical review letters

A precision measurement by AMS of the antiproton flux and the antiproton to proton flux ratio in primary cosmic rays in the absolute rigidity range from 1 to 450 GV is presented based on 3.49x10^{5} antiproton events and 2.42x10^{9} proton events. The fluxes and flux ratios of charged elementary particles in cosmic rays are also presented. In the absolute rigidity range ~60 to ~500 GV, the antiproton p[over proton p, and positron e^ fluxes are found to have nearly identical rigidity dependence and the electron e^ flux exhibits a different rigidity dependence. Below 60 GV, the (p[over /p) (p[over /e^ and (p/e^ flux ratios each reaches a maximum. From ~60 to ~500 GV, the (p[over /p) (p[over /e^ and (p/e^ flux ratios show no rigidity dependence. These are new observations of the properties of elementary particles in the cosmos.
Value co creation between firms and customers: The role of big data based cooperative assets
Venue: Inf. Manag.

Big data interconnects firms and customers in promoting value co creation.Big data can be transformed from resources to valuable cooperative assets.We describes the transformation process of big data based cooperative assets.The transformation requires heterogeneous resource bases and cooperative capabilities. To better understand how big data interconnects firms and customers in promoting value co creation, we propose a theoretical framework of big data based cooperative assets based on evidence of multiple case studies. We identify four types of big data resources and four types of associated digital platforms, and we explore how firms develop the cooperative assets by transforming big data resources via the theoretical lens of service dominant logic. This study offers a new theoretical perspective on value co creation and an alternative competitive strategy in the era of big data for firms.
Big Data Meet Green Challenges: Big Data Toward Green Applications
Venue: IEEE Systems Journal

Big data are widely recognized as being one of the most powerful drivers to promote productivity, improve efficiency, and support innovation. It is highly expected to explore the power of big data and turn big data into big values. To answer the interesting question whether there are inherent correlations between the two tendencies of big data and green challenges, a recent study has investigated the issues on greening the whole life cycle of big data systems. This paper would like to discover the relations between the trend of big data era and that of the new generation green revolution through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.
Agile project management and stage gate model A hybrid framework for technology based companies

This study reports an empirical analysis of a hybrid management framework combining agile project management and stage gate model implemented in a technology driven project. The results indicate positive impact on the project and product development performance and suggest that combining these two approaches to balance stability with flexibility is a potential solution for managing innovation projects in high technology based companies. The evidence indicated critical aspects to be considered, such as the proper diagnosis of organizational factors and implementation of practices, and the alignment of agile project tools (e.g. visual boards) with traditional information systems used in the stage gate process.
Information Technology Project Management: Providing Measurable Organizational Value

The 5th Edition of Jack Marchewka's Information Technology Project Management focuses on how to create measurable organizational value (MOV) through IT projects. The author uses the concept of MOV, combined with his own research, to create a solid foundation for making decisions throughout the project's lifecycle. The book's integration of project management and IT concepts provides students with the tools and techniques they need to develop in this field.
The Health Benefits of Exercise and Physical Activity
Venue: Current Nutrition Reports

Physical inactivity is a modifiable risk factor (similar to dyslipidemia and hypertension) for a variety of chronic diseases, including cancer and cardiovascular disease. Exercise provides a clear health benefit, which serves in the primary and secondary prevention of these disease processes (the most important being a reduction in cardiovascular disease and premature death) The physiologic mechanisms for such a benefit occur at both a cellular and multisystem level. Prolonged periods of occupational or leisure time sitting have adverse health effects independent of exercise performed before or after. Almost any form of physical activity (PA) is beneficial, whether part of a regular exercise program or as a series of intermittent, incidental, non purposeful, lifestyle embedded activity (causing non exercise activity thermogenesis or NEAT) The health benefits of exercise appear to be dose dependent. Physicians should recommend near daily exercise which includes at various times strength training, stretching, and aerobic activity in addition to emphasizing adjustments that allow for reduced sitting and increased activity during daily routines. Patients should understand that for optimal health, exercise is no longer optional.
Stromal PD L1 Expression Is Associated With Better Disease Free Survival in Triple Negative Breast Cancer.
Venue: American journal of clinical pathology

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer, and there is no approved targeted therapy. We studied the expression of programmed cell death protein 1 (PD 1) and its ligand (PD L1) in TNBC.  Full face sections from 136 TNBC cases without neoadjuvant therapy between 2004 and 2013 were stained and evaluated for immune cell PD 1 staining and stromal or tumoral PD L1 staining using the H score (staining percentage x intensity) Nottingham histologic grade, lymphovascular invasion (LVI) mitotic count, and tumor infiltrating lymphocytes (TILs) were evaluated. Tumor size, lymph node status, Ki 67 score, metastasis, overall survival (OS) and disease free survival (DFS) were retrieved from medical records.  Of the 136 TNBC cases, 69 (51% had any PD L1 staining and 35 (26% had PD L1 staining with an H score of 5 or more; 117 (86% had any PD 1 staining and 68 (50% had PD 1 staining with an H score of 5 or more. Tumor size and LVI were significantly associated with worse OS and DFS, and TILs and LVI were significantly associated with metastasis in univariate analysis. Stromal PD L1 expression was significantly associated with better DFS in multivariate analysis. PD 1 expression was not associated with DFS, OS, or metastasis.  PD L1 expression is seen in a high proportion of TNBCs and associated with better DFS.
PD L1 expression and CD274 gene alteration in triple negative breast cancer: implication for prognostic biomarker
Venue: SpringerPlus

PurposeTo estimate the therapeutic potential of PD L1 inhibition in breast cancer, we evaluated the prevalence and significance of PD L1 protein expression with a validated antibody and CD274 gene alternation in a large cohort of triple negative breast cancer (TNBC) and correlated with clinicopathological data and patients overall survival.MethodsImmunohistochemistry and in situ mRNA hybridization was used to detect PD L1 protein and mRNA expression in tumor tissues from 183 TNBC patients respectively. Fluorescence in situ hybridization analysis was performed on PD L1 strong expression samples to assess copy number on chromosome 9p24.1 of CD274 gene.ResultsExpression of PD L1 by immune cells was observed in 4.9 of TNBC, while expression by tumor cells accounted for 8.7 There was a high concordance in PD L1 protein expression and PDL1 mRNA expression. Samples with PD L1 strong expression were found to have a CD274 gene copy number gain. PD L1 expression was correlated with higher tumor grade, but was independent of menopausal status, lymph nodes metastasis, histological subtype and tumor size. In addition, we used precise stratification of PD L1 expression on tumor or immune cells of certain breast cancer subtype and suggested that patients with PD L1 expression in basal like tumors by immune cells or with CD274 gene copy number gain had a longer disease specific overall survival.ConclusionsOur findings may promote the more precise analysis of PD L1 expression in breast cancer and aid the selection of patients who may benefit from immune therapy.
Tumor Infiltrating Lymphocytes in Triple Negative Breast Cancer: The Future of Immune Targeting
Venue: Clinical Medicine Insights. Oncology

Triple negative breast cancer (TNBC) is a highly heterogeneous tumor. There is increasing evidence of the role of tumor lymphocytic immune infiltrates in this subtype of breast cancer. Robust levels of tumor infiltrating lymphocytes (TILs) have been associated with improved disease free and overall survival rates in TNBC patients with and without any treatment. Recent efforts have been made to develop a standardized methodology for evaluating TILs. The presence of TILs in the breast tumor microenvironment can also predict responses not only to neoadjuvant but also to adjuvant chemotherapy treatments. High numbers of TILs correlate with increased pathological complete responses (pCR) in TNBC. TILs are prognostic and predictive of response to standard therapies; thus, the immune system appears to play an active role in a subgroup of breast cancer. There is an increasing interest in directly targeting the immune system as part of breast cancer therapy, mainly in patients with TNBC. New immune modulatory agents, including immune checkpoints inhibitors, have shown promising activity in a subgroup of metastatic TNBC. Increased programmed cell death protein 1 ligand (PD L1) expression on the surface of TNBC provides the rationale for implementing therapeutic strategies targeting the PD 1/PD L1 axis in TNBC. The programmed cell death protein 1 (PD 1) inhibitor pembrolizumab, and the PD L1 inhibitor atezolizumab have shown promising results in clinical trials.
Artificial Neural Networks Approach to the Forecast of Stock Market Price Movements

In this work we present an Artificial Neural Network (ANN) approach to predict stock market indices. In particular, we focus our attention on their trend movement up or down. We provide results of experiments exploiting different Neural Networks architectures, namely the Multi layer Perceptron (MLP) the Convolutional Neural Networks (CNN) and the Long Short Term Memory (LSTM) recurrent neural networks technique. We show importance of choosing correct input features and their preprocessing for learning algorithm. Finally we test our algorithm on the S&P500 and FOREX EUR/USD historical time series, predicting trend on the basis of data from the past n days, in the case of S&P500, or minutes, in the FOREX framework. We provide a novel approach based on combination of wavelets and CNN which outperforms basic neural networks approaches.
Artificial Neural Networks architectures for stock price prediction: comparisons and applications

We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief resume of the existing literature on the subject, we consider the Multi layer Perceptron (MLP) the Convolutional Neural Networks (CNN) and the Long Short Term Memory (LSTM) recurrent neural networks techniques. We focus on the importance of choosing the correct input features, along with their preprocessing, for the specific learning algorithm one wants to use. Eventually, we consider the S&P500 historical time series, predicting trend on the basis of data from the past days, and proposing a novel approach based on combination of wavelets and CNN, which outperforms the basic neural networks ones. We show, that neural networks are able to predict financial time series movements even trained only on plain time series data and propose more ways to improve results.  Artificial neural networks, Multi layer neural network, Convolutional neural network, Long shortterm memory, Recurrent neural network, Deep Learning, Stock markets analysis, Time series analysis, financial forecasting