Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of NPY-Expressing Neocortical Interneurons

Neuropeptide Y (NPY) is an abundant neuropeptide of the neocortex involved in numerous physiological and pathological processes. Because of the large electrophysiological, molecular, and morphological diversity of NPY-expressing neurons their precise identity remains unclear. To define distinct populations of NPY neurons we characterized, in acute slices of rat barrel cortex, 200 cortical neurons of layers I–IV by means of whole-cell patch-clamp recordings, biocytin labeling, and single-cell reverse transcriptase-PCR designed to probe for the expression of well established molecular markers for cortical neurons. To classify reliably cortical NPY neurons, we used and compared different unsupervised clustering algorithms based on laminar location and electrophysiological and molecular properties. These classification schemes confirmed that NPY neurons are nearly exclusively GABAergic and consistently disclosed three main types of NPY-expressing interneurons. (1) Neurogliaform-like neurons exhibiting a dense axonal arbor, were the most frequent and superficial, and substantially expressed the neuronal isoform of nitric oxide synthase. (2) Martinotti-like cells characterized by an ascending axon ramifying in layer I coexpressed somatostatin and were the most excitable type. (3) Among fast-spiking and parvalbumin-positive basket cells, NPY expression was correlated with pronounced spike latency. By clarifying the diversity of cortical NPY neurons, this study establishes a basis for future investigations aiming at elucidating their physiological roles.
Investigation of Repeat Client Drop-Out and Re-Enrolment Cycles in Fourteen Methadone Maintenance Treatment Clinics in Guangdong, China

Objective Client adherence is vital for effective methadone maintenance treatment (MMT). This study explores the pattern and associated factors of client adherence, drop-out and re-enrolment in the Chinese MMT programme over the period of 2006–2013. Methods This retrospective study was conducted in 14 MMT clinics in Guangdong Province, China. We employed Kaplan-Meier survival analysis to estimate the rates of drop-out and re-enrolment of MMT clients and multivariate Cox regression to identify associated factors. Results Among 1,512 study participants, 79% have experienced ‘drop-out’ during the 7-year study period. However, 82% ‘dropped-out’ clients resumed treatment at a later time. Low education level (junior high or below versus otherwise, HR = 1.21, 1.05–1.40), low methadone dosage in the first treatment episode (<50 ml versus ≥50 ml, HR = 1.84, 1.64–2.06) and higher proportion of positive urine test (≥50% versus<50%, HR = 3.72, 3.30–4.20) during the first treatment episode were strong predictors of subsequent drop-outs of the participants. Among the ‘dropped-out’ clients, being female (HR = 1.40, 1.23–1.60), being married (HR = 1.19, 1.09–1.30), and having a higher proportion of positive urine tests in the first treatment episode (≥50% versus<50%, HR = 1.35, 1.20–1.51) had greater likelihood of subsequent re-enrolment in MMT. Clients receiving lower methadone dosage (first treatment episode <50 ml versus ≥50 ml, HR = 1.12, 1.03–1.23; the last intake before drop-out <50 ml versus ≥50 ml, HR = 1.16, 1.04–1.30) were also more likely to re-enrol. Conclusion Persistent cycling in-and-out of clients in MMT programmes is common. Insufficient dosage and higher proportion of positive urine samples in the first treatment episode are the key determinants for subsequent client drop-out and re-enrolment. Interventions should target clients in their early stage of treatment to improve retention in the long term.
Economic Implications from Deficit Finance

The paper enlightens popular part of the budget policy - deficit finance. In the process of securing economic conditions to surpass the current economic crises, the governments all over the world incline towards debt deficit finance. The intention is to describe the implications such as multiplier effect, crowding out effect, correlation between budget and trade deficit. One of them are positive, they increase the aggregate demand and national income, other negative in term that they crowd out the private sector from the capital market under increased demand for loanable funds.
The USAGE review corpus for fine grained multi lingual opinion analysis

Opinion mining has received wide attention in recent years. Models for this task are typically trained or evaluated with a manually annotated dataset. However, fine-grained annotation of sentiments including information about aspects and their evaluation is very labour-intensive. The data available so far is limited. Contributing to this situation, this paper describes the Bielefeld University Sentiment Analysis Corpus for German and English (USAGE), which we offer freely to the community and which contains the annotation of product reviews from Amazon with both aspects and subjective phrases. It provides information on segments in the text which denote an aspect or a subjective evaluative phrase which refers to the aspect. Relations and coreferences are explicitly annotated. This dataset contains 622 English and 611 German reviews, allowing to investigate how to port sentiment analysis systems across languages and domains. We describe the methodology how the corpus was created and provide statistics including inter-annotator agreement. We further provide figures for a baseline system and results for German and English as well as in a cross-domain setting. The results are encouraging in that they show that aspects and phrases can be extracted robustly without the need of tuning to a particular type of products.
An Artificial Agent for Robust Image Registration

3-D image registration, which involves aligning two or more images, is a critical step in a variety of medical applications from diagnosis to therapy. Image registration is commonly performed by optimizing an image matching metric as a cost function. However this task is challenging due to the non-convex nature of the matching metric over the plausible registration parameter space and insufficient approches for a robust optimization. As a result, current approaches are often customized to a specific problem and sensitive to image quality and artifacts. In this paper, we propose a completely different approach to image registration, inspired by how experts perform the task. We first cast the image registration problem as a "strategic learning" process, where the goal is to find the best sequence of motion actions (e.g. up, down, etc) that yields image alignment. Within this approach, an artificial agent is learned, modeled using deep convolutional neural networks, with 3D raw image data as the input, and the next optimal action as the output. To copy with the dimensionality of the problem, we propose a greedy supervised approach for an end-to-end training, coupled with attention-driven hierarchical strategy. The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy). We demonstrate on two 3-D/3-D medical image registration examples with drastically different nature of challenges, that the artificial agent outperforms several state-of-the-art registration methods by a large margin in terms of both accuracy and robustness.
Optimal Degree Distribution for LT Codes with Small Message Length

Fountain codes provide an efficient way to transfer information over erasure channels. We give an exact performance analysis of a specific type of fountain codes, called LT codes, when the message length N is small. Two different approaches are developed. In a Markov chain approach the state space explosion, even with reduction based on permutation isomorphism, limits the analysis to very short messages, N < 4. An alternative combinatorial method allows recursive calculation of the probability of decoding after N received packets. The recursion can be solved symbolically for values of N < 10 and numerically up to N ap30. Examples of optimization results give insight into the nature of the problem. In particular, we argue that a few conditions are sufficient to define an almost optimal LT encoding.
Information Exchange in Prediction Markets: How Social Networks Promote Forecast Efficiency

This paper studies the effects of information exchange and social networks on the performance of prediction markets with endogenous information acquisition. We provide a game-theoretic framework to resolve the question: Can social networks and information exchange promote the forecast efficiency in prediction markets? Our study shows that a social network is not a panacea in terms of improving forecast efficiency. The use of social networks could be detrimental to forecast performance when the cost of information acquisition is high. Although social networks can provide internal communications among participants, they reduce the incentive to acquire information because of free riding. We also study the effects of social networks on information acquisition in prediction markets. In the symmetric Bayes-Nash Equilibrium, all participants use a threshold strategy, and the equilibrium action of information acquisition is decreasing in the number of participant's friends and increasing in the network density. The aforementioned results are robust to two commonly used mechanisms of prediction markets: a forecast-report mechanism and a security-trading mechanism.
Pseudomonas Pyocyanin Increases Interleukin-8 Expression by Human Airway Epithelial Cells

ABSTRACT Pseudomonas aeruginosa, an opportunistic human pathogen, causes acute pneumonia in patients with hospital-acquired infections and is commonly associated with chronic lung disease in individuals with cystic fibrosis (CF). Evidence suggests that the pathophysiological effects of P. aeruginosa are mediated in part by virulence factors secreted by the bacterium. Among these factors is pyocyanin, a redox active compound that increases intracellular oxidant stress. We find that pyocyanin increases release of interleukin-8 (IL-8) by both normal and CF airway epithelial cell lines and by primary airway epithelial cells. Moreover, pyocyanin synergizes with the inflammatory cytokines tumor necrosis factor alpha and IL-1α. RNase protection assays indicate that increased IL-8 release is accompanied by increased levels of IL-8 mRNA. The antioxidant n -acetyl cysteine, general inhibitors of protein tyrosine kinases, and specific inhibitors of mitogen-activated protein kinases diminish pyocyanin-dependent increases in IL-8 release. Conversely, inhibitors of protein kinases C (PKC) and PKA have no effect. In contrast to its effects on IL-8 expression, pyocyanin inhibits cytokine-dependent expression of the monocyte/macrophage/T-cell chemokine RANTES. Increased release of IL-8, a potent neutrophil chemoattractant, in response to pyocyanin could contribute to the marked infiltration of neutrophils and subsequent neutrophil-mediated tissue damage that are observed inPseudomonas-associated lung disease.
Centroid neural network for unsupervised competitive learning

An unsupervised competitive learning algorithm based on the classical -means clustering algorithm is proposed. The proposed learning algorithm called the centroid neural network (CNN) estimates centroids of the related cluster groups in training date. This paper also explains algorithmic relationships among the CNN and some of the conventional unsupervised competitive learning algorithms including Kohonen's self-organizing map (SOM) and Kosko's differential competitive learning (DCL) algorithm. The CNN algorithm requires neither a predetermined schedule for learning coefficient nor a total number of iterations for clustering. The simulation results on clustering problems and image compression problems show that CNN converges much faster than conventional algorithms with compatible clustering quality while other algorithms may give unstable results depending on the initial values of the learning coefficient and the total number of iterations.
Intention to trust and share health-related online rumors: Studying the role of risk propensity

As the Internet has become a popular avenue for users to seek health-related information, the prevalence of online rumors represents a growing concern. If users trust and share bogus health information on the Internet, repurcussions could be serious. Therefore, this paper examines how users' intention to trust and share rumors vary as a function of three factors: length, sentiment of rumors, and presence of pictures. The individual difference of risk propensity is also taken into account. Data were collected from 110 participants using a 2 (length of rumors: short or long) × 2 (rumor sentiments: positive or negative) × 2 (presence of pictures: present or absent) within-participants web-based experiment. Results indicate that long rumors as well as those without pictures were likely to be trusted and shared regardless of individuals' risk propensity. However, the relationship between rumor sentiment and intention was not always consistent between risk-averse and risk-seeking individuals. Implications of the results for both theory and practice are highlighted.
Scandal! : an interdisciplinary approach to the consequences, outcomes, and significance of political scandals

Contributors Introduction by Alison Dagnes Section I: Congress Preface by Lara Brown, Jeff Gulati, Doug Harris and Scott Basinger Chapter 1: Sack the Quarterback by Doug Harris Chapter 2: Spending More Time with My Family: Scandals and Premature Departures from the House by Lara Brown & Jeff Gulati Chapter 3: The Electoral Effects of Congressional Scandals by Scott Basinger Section II: Historical Accounts, Modern Implications Introduction by Mark Sachleben Chapter 4: Tom Clark Under Fire by Craig Smith Chapter 5: Sherman Adams' Fall, and the Scandal Behind the Scandal by Michael Birkner Chapter 6: The More Things Change, the More They Stay the Same by George Hale Chapter 7: Sex, Scandal and Catholic Politics during Italy's Dolce Vita by Roy Domenico Section II Conclusions: Paralleling History: Scandal and the Lessons of the 2012 Election by Neal Allen Section III: Shaping the Perceptions of Scandal Introduction by Stephanie Jirard Chapter 8: Scandal and the Politics of Race by Neal Allen Chapter 9: He Can't Say with Certitude" by Joan Conners Chapter 10: Shattered Dignity: The Apologia of John Edwards by Misty Knight Conclusions Index
How Practitioners Perceive the Relevance of ESEM Research

Background: The relevance of ESEM research to industry practitioners is key to the long-term health of the conference. Aims: The goal of this work is to understand how ESEM research is perceived within the practitioner community and provide feedback to the ESEM community ensure our research remains relevant. Method: To understand how practitioners perceive ESEM research, we replicated previous work by sending a survey to several hundred industry practitioners at a number of companies around the world. We asked the survey participants to rate the relevance of the research described in 156 ESEM papers published between 2011 and 2015. Results: We received 9,941 ratings by 437 practitioners who labeled ideas as Essential, Worth-while, Unimportant, or Unwise. The results showed that overall, industrial practitioners find the work published in ESEM to be valuable: 67% of all ratings were essential or worthwhile. We found no correlation between citation count and perceived relevance of the papers. Through a qualitative analysis, we also identified a number of research themes on which practitioners would like to see an increased research focus. Conclusions: The work published in ESEM is generally relevant to industrial practitioners. There are a number of topics for which those practitioners would like to see additional research undertaken.
Metastatic colorectal cancer: systemic treatment in the new millennium.

BACKGROUND Colorectal cancer (CRC) is common in North America. Metastatic disease is present at diagnosis in 30% of the patients, and approximately half of early-stage patients will eventually present with metastatic disease. Until recently, few chemotherapy options were available to treat metastatic CRC. METHODS The authors review the results of recent clinical trials and the design of ongoing trials in the management of patients with metastatic colorectal cancer. RESULTS Fluorouracil (5-FU) with leucovorin (LV) modulation has a marginal but positive effect on survival in those patients. The recent incorporation of irinotecan (CPT-11) and oxaliplatin for the management of advanced CRC has generated further improvement in survival. The development of oral fluoropyrimidines, mimicking continuous infusion 5-FU, is convenient. In randomized trials, capecitabine was equally effective to bolus 5-FU and LV in the management of metastatic CRC. CONCLUSIONS Recently completed or ongoing clinical trials to study novel targeting agents have initiated a new era of drug development. Anti-angiogenesis drugs, tyrosine kinase inhibitors, and epidermal growth factor blockers are among this new generation of agents with encouraging preliminary data. Randomized trials will determine the impact of these newer agents on survival and quality of life of patients with metastatic CRC.
Nitric oxide levels regulate macrophage commitment to apoptosis or necrosis during pneumococcal infection

Macrophages are resistant to constitutive apoptosis, but infectious stimuli can induce either microbial or host‐mediated macrophage apoptosis. Phagocytosis and killing of opsonized pneumococci by macrophages are potent stimuli for host‐mediated apoptosis, but the link between pneumococcal killing and apoptosis induction remains undefined. We now show phagocytosis of pneumococci by differentiated human monocyte‐derived macrophages (MDM) results in up‐regulation of inducible nitric oxide synthase (iNOS) and increased production of NO and reactive nitrogen species. NO accumulation in macrophages initiates an apoptotic program that involves NO‐dependent mitochondrial membrane permeabilization, Mcl‐1 down‐ regulation, and caspase activation and results in nuclear condensation and fragmentation. An inhibitor of mitochondrial permeability transition, bongkrekic acid, decreases pneumococcal‐ associated macrophage apoptosis. Conversely, inhibition of NO production using iNOS inhibitors decreases bacterial killing and shifts the cell death program from apoptosis to necrosis. Pneumolysin contributes to both NO production and apoptosis induction. After initial microbial killing, NO accumulation switches the macrophage phenotype from an activated cell to a cell susceptible to apoptosis. These results illustrate important roles for NO in the integration of host defense and regulation of inflammation in human macrophages.
‘Been there, done that’: international student migration and human capital transfers from the UK to Slovakia

International student migration remains an under-researched field in migration studies, and this is especially true of return migration. This paper analyses students from Slovakia who have studied in the UK, both on degree courses and language/vocational courses, and have subsequently returned to their country of origin. It analyses their motivations, their acquisition of human capital in the UK, and the extent to which they have been able to realise individual welfare gains after returning to Slovakia. Their evaluations of their experiences are highly positive, with substantial numbers also reporting improvements in their jobs and incomes, even following relatively short stays abroad. The study emphasises the importance of the specific competences acquired by the students, rather than broad skill categories, or qualifications. It highlights the value attached to language competence, in particular, but also to learning, attitudinal and interpersonal competences, as well as networking. The paper concludes that there is a need to pay more attention to individual social biographies when understanding the relationship between migration and learning. At the same time, it also stresses structural parameters to individual agency, including the specific economic conditions in a transition economy, and the market value of competence in English as a world language. Copyright © 2004 John Wiley & Sons, Ltd.
Do IPOs' Characteristics Affect the Likelihood of Post-Offering Acquisitions of Entrepreneurial Firms?

The strategy and financial economics literature have long investigated the problem of adverse selection between buyers and sellers in M&A markets, which can create inefficiencies and stop acquirers from gaining access to valuable resources and capabilities via acquisitions. In turn, the entrepreneurship literature has highlighted the challenges faced by entrepreneurial firms to raise external capital, due to the asymmetric information that separates them from prospective exchange partners. In this paper, we study initial public offerings and their characteristics as vehicles to bring these parties together in M&A. Precisely, we investigate several micro features of IPOs that provide signals mitigating the problems stemming from information asymmetry across entrepreneurial firms and acquirers. We construct hazard models that bring strong evidence that certain IPO characteristics can influence the likelihood of entrepreneurial firms attracting M&A suitors.
The US Wage Phillips Curve Across Frequencies and Over Time

Although widely used in many areas of applied sciences, wavelet analysis has not fully entered the economic discipline yet. In this article we apply wavelet analysis to one of the most investigated relationships is in empirical macroeconomics: the relationship between wage inflation and unemployment. Using US postwar data we find a frequency-dependent relationship of a sort that is consistent with Phillips’ original insights. It also turns out that this relationship is remarkably stable over the 1948–93 period, but not in the aftermath, as a consequence of a process of adaption of the wage formation process to a low inflation environment.
Radio Emission from SN 2001gd in NGC 5033

We present the results of monitoring the radio emission from the Type IIb supernova SN 2001gd between 2002 February 8 and 2002 October 28. Most of the data were obtained using the Very Large Array at the five wavelengths of 1.3 cm (22.4 GHz), 2.0 cm (14.9 GHz), 3.6 cm (8.44 GHz), 6.2 cm (4.86 GHz), and 21 cm (1.4 GHz). Observations were also made with the Giant Meterwave Radio Telescope at 21 cm (1.4 GHz). The object was discovered optically well after maximum light, making any determination of the early radio evolution difficult. However, subsequent observations indicate that the radio emission has evolved regularly in both time and frequency and is well described by the supernova shock-circumstellar medium interaction model.