Miracle-dz/UniFAR-FTUs huggingface dataset:
Dopamine D2 receptors in the basolateral amygdala modulate erectile function in a rat model of nonorganic erectile dysfunction

Nonorganic erectile dysfunction is a problem with unknown central mechanisms. Changes in brain activity in the amygdala have been observed in human patients. This study aimed to investigate the dopamine system in the basolateral amygdala of male rats with nonorganic erectile dysfunction. We applied chronic mild stress to induce nonorganic erectile dysfunction. After exposure to chronic mild stress, the sucrose consumption test, sexual behaviour test and apomorphine test were used to select depression‐like rats with erectile dysfunction as nonorganic erectile dysfunction model rats. The sexual behaviour of these rats after central infusion of a dopamine D1/D2 receptor agonist/antagonist was observed. The expression levels of dopamine D1/D2 receptors and tyrosine hydroxylase in the basolateral amygdala were also measured. The result of the sucrose consumption test, sexual behaviour test and apomorphine test indicated a successful nonorganic erectile dysfunction model. Central infusion of a dopamine D2 receptor agonist increased intromission ratio in model rats. Lower expression levels of tyrosine hydroxylase and the dopamine D2 receptor in the basolateral amygdala were observed in rats with nonorganic erectile dysfunction. These results suggest that impairment of the dopamine D2 receptor pathway in the basolateral amygdala may contribute to the development of nonorganic erectile dysfunction.
Intraday News Trading: The Reciprocal Relationships Between the Stock Market and Economic News

This study investigates the interdependent relationships between the stock market and economic news in the U.S. context. 2,440 economic tweets from Reuters and Bloomberg published in September 2015 were analyzed within short-term intervals (5 minutes, 20 minutes, and 1 hour) as well as 50 influential Bloomberg market coverage stories distributed via their terminals for the same period of time. Using Vector Auto Regression analyses, it was found that news volume, news relevance, and expert opinion in tweets seem to influence the fluctuation of the Dow Jones Industrial Average (DJI) positively, while economic news appears to respond to market fluctuation with less coverage, including fewer retweets, favorites, updates, or expert opinions conveyed. Inspecting the influential market stories by Bloomberg, the results imply that while Bloomberg terminals provide firsthand information on the market to professionals, tweets rather seem to offer follow-up reporting to the public. Furthermore, given that the effect of economic tweets on the DJI fluctuations was found to be strongest within longer time intervals (i.e., 1 hour), the findings imply that public traders need more time to evaluate information and to make a trading decision than professional investors.
Cyclostationarity-Based Multi-Antenna Cooperative Spectrum Sensing in Cognitive Radio Networks Over Correlated Fading Channels

Spectrum sensing is the key functionality of the cognitive radio networks. A variety of methods for a reliable and robust spectrum sensing scheme has been proposed. However, noise uncertainty, fading and correlation among receivers degrade the performance of these methods considerably. In this paper, we exploit cyclostationarity-based spectrum sensing, which is robust against noise uncertainty, as well as multiple antennas and cooperation among users to lessen the effects of these phenomena. In our performance evaluation, we consider Rayleigh and Nakagami fading, and spatial correlation among antennas to study the spectrum sensing method in a more realistic radio environment. Our simulation results show that using three well-separated antennas (uncorrelated fading) is more advantageous than using ten antennas in a dense space (highly correlated fading).
A Data Mining Service for Non-Programmers

With the emergence of Big Data, the scarcity of data scientists to analyse all the data being produced in different domains became evident. To train new data scientists faster, web applications providing data science practices without requiring programming skills can be a great help. However, some available web applications lack in providing good data mining practices, specially for assessment and selection of models. Thus, in this paper we describe a system, currently under development, that will provide the construction of data mining processes enforcing good data mining practices. The system will be available through a web UI and will follow a microservices architecture that is still being designed and tested. Preliminary usability tests, were conducted with two groups of users to evaluate the envisioned concept for the creation of data mining processes. In these tests we observed a general high level of user satisfaction. To assess the performance of the current system design, we have done tests in a public cloud where we observed interesting results that will guide us in new
Selecting Relevance Thresholds to Improve a Recommender System in a Parliamentary Setting

In the context of building a recommendation/filtering system to deliver relevant documents to the Members of Parliament (MPs), we have tackled this problem by learning about their political interests by mining their parliamentary activity using supervised classification methods. The performance of the learned text classifiers, one for each MP, depends on a critical parameter, the relevance threshold. This is used by comparing it with the numerical score returned by each classifier and then deciding whether the document being considered should be sent to the corresponding MP. In this paper we study several methods which try to estimate the best relevance threshold for each MP, in the sense of maximizing the system performance. Our proposals are experimentally tested with data from the regional Andalusian Parliament at Spain, more precisely using the textual transcriptions of the speeches of the MPs in this parliament.
On the Convergence and Robustness of Batch Normalization

Despite its empirical success, the theoretical underpinnings of the stability, convergence and acceleration properties of batch normalization (BN) remain elusive. In this paper, we attack this problem from a modeling approach, where we perform a thorough theoretical analysis on BN applied to a simplified model: ordinary least squares (OLS). We discover that gradient descent on OLS with BN has interesting properties, including a scaling law, convergence for arbitrary learning rates for the weights, asymptotic acceleration effects, as well as insensitivity to the choice of learning rates. We then demonstrate numerically that these findings are not specific to the OLS problem and hold qualitatively for more complex supervised learning problems. This points to a new direction towards uncovering the mathematical principles that underlies batch normalization.
Signal to Noise Ratio Improvement of Ultrasound Harmonic Images Using Coded Excitation and Adaptive Line Enhancer

Signal to Noise Ratio (SNR), is one of the parameters that has significant effect on quality of ultrasound images, especially harmonic images. In ultrasound images the frequency dependent attenuation can reduce the SNR of images that are reconstructed with higher harmonics. In this paper, to improve the SNR of harmonic images, frequency modulated signals (chirp), along with the adaptive line enhancer (ALE) is proposed. The ALE is applied on compressed receive signal after matched filtering (MF). The results show more than 3dB improvement in SNR value and around 5mm improvement in penetration depth of harmonic signals that are separated by pulse inversion method.
A 3.2-GHz Supply Noise-Insensitive PLL Using a Gate-Voltage-Boosted Source-Follower Regulator and Residual Noise Cancellation

In this brief, we propose a supply noise-insensitive charge-pump phase-locked loop (PLL) using a source-follower (SF) regulator and noise cancellation. In order to minimize the voltage drop of the SF regulator while improving supply rejection, a gate-voltage-boosting technique and the body-controlled noise cancellation are proposed. To suppress the phase noise from the ring oscillator, a reference multiplier is employed to maximize the PLL loop bandwidth. Implemented in 65-nm CMOS, a prototype PLL at 3.2 GHz achieves supply noise spur of less than −33 dBc for a 50-<inline-formula> <tex-math notation="LaTeX">$\text{m}V_{\mathrm{ pp}}$ </tex-math></inline-formula> supply noise around the loop bandwidth while consuming 3.12 mW from a 1-V supply.
The JAK inhibitor antcin H exhibits direct anticancer activity while enhancing chemotherapy against LMP1-expressed lymphoma

Abstract Epstein-Barr virus (EBV) infection is associated with B cell lymphomas in humans. The latent membrane protein 1 (LMP-1) of EBV constitutively activates the JAK/STAT signaling pathway and contributes to the proliferation of EBV-infected primary human B lymphocytes. Thus, targeting LMP1-induced JAK/STAT signaling may prove effective in treating B-cell lymphomas. The extract of the fruiting body of Antrodia cinnamomea, has been reported to have cytotoxicity on blood cancer cells. Here, we report that the bioactivity of antcin H, an analog of the JAK2 inhibitor zhankuic acid A (ZAA), inhibits LMP1-induced JAK/STAT related signaling and induces lymphoma cell line apoptosis. Moreover, antcin H enhances low-dose methotrexate (MTX) cytotoxicity against lymphoma cells. Treatment of antcin H with low-dose MTX significantly suppressed tumor growth and prolonged the survival of tumor-bearing mice. Our findings indicate antcin H as a potential therapeutic agent for the treatment of EBV-infected cancer cells.
Use of a Metabolic Syndrome Severity Z Score to Track Risk During Treatment of Prediabetes: An Analysis of the Diabetes Prevention Program

OBJECTIVE We assessed whether changes in metabolic syndrome (MetS) severity during the treatment of prediabetes are associated with reduced risk of type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD). RESEARCH DESIGN AND METHODS We analyzed data from the Diabetes Prevention Program (DPP) for 2,476 adults in 1996–1999 with prediabetes randomized to receive treatment with lifestyle modification, metformin, or placebo for 2–3 years and followed through 2014 for T2DM and CVD outcomes. We calculated effect sizes from baseline in a MetS severity z score (MetS-Z) and the individual MetS components, and assessed relationships between 1-year effect size and incident T2DM and CVD using hazard ratios (HRs) and mediation analysis. RESULTS Baseline MetS-Z and its components were associated with risk of incident T2DM and CVD. During year 1 of intervention, MetS-Z and its components decreased most with lifestyle modification, followed by treatment with metformin and placebo. Risk of T2DM within 1–5 years was most strongly associated with 1-year changes in MetS-Z and waist circumference (both HRs for a 1 SD increase = 1.80), whereas the risk of CVD was associated with a 1-year change in MetS-Z, glucose, and systolic blood pressure. In mediation analyses, the effect of lifestyle modification on T2DM risk was mediated by 1-year changes in MetS-Z, waist circumference, glucose, and triglycerides, whereas the effect of metformin was mediated by MetS-Z and glucose. CONCLUSIONS Changes in these risk indicators of MetS severity during intervention in the DPP reflect altered disease risk and may help in tracking earlier responses to treatment and in motivating patients.
Survival at the Museum: A Cooperation Experiment with Emotionally Expressive Virtual Characters

Correctly interpreting an interlocutor's emotional expression is paramount to a successful interaction. But what happens when one of the interlocutors is a machine? The facilitation of human-machine communication and cooperation is of growing importance as smartphones, autonomous cars, or social robots increasingly pervade human social spaces. Previous research has shown that emotionally expressive virtual characters generally elicit higher cooperation and trust than 'neutral' ones. Since emotional expressions are multi-modal, and given that virtual characters can be designed to our liking in all their components, would a mismatch in the emotion expressed in the face and voice influence people's cooperation with a virtual character? We developed a game where people had to cooperate with a virtual character in order to survive on the moon. The character's face and voice were designed to either smile or not, resulting in 4 conditions: smiling voice and face, neutral voice and face, smiling voice only (neutral face), smiling face only (neutral voice). The experiment was set up in a museum over the course of several weeks; we report preliminary results from over 500 visitors, showing that people tend to trust the virtual character in the mismatched condition with the smiling face and neutral voice more. This might be because the two channels express different aspects of an emotion, as previously suggested.
Periodic Defragmentation in Elastic Optical Networks

The main weakness of Elastic Optical Networks (EON) stems from spectrum fragmentation. An analysis of periodic defragmentation in EONs under dynamic traffic conditions is carried out. The effects of different defragmentation parameters on the EON performance are evaluated, and appropriate values, guaranteeing suitable network performance while keeping the network control complexity at a reasonable point, are obtained through simulations. Different network topologies as well as traffic conditions are simulated to assess the validity of the obtained results.
Utilization of Data Classification in the Realization of a Surface Plasmon Resonance Readout System Using an FPGA Controlled RGB LED Light Source

This paper presents the realization of a surface plasmon resonance (SPR) sensor readout system using a tricolor red, green, and blue light emitting diode light source. Time domain intensity modulation of each color channel is applied to interrogate three bands of interest in the SPR spectrum using a single photodiode detector. A low computing resource classification approach is used through the combination of k-nearest neighbor (kNN) and adapted clustering using representative. An optimized number of representatives is chosen in the validation process to reduce the required amount of data for the kNN classification. This scheme was used to classify the concentrations of different glucose solutions. The sensor readout system hardware is based on the use of a field programmable gate array and the glucose solution classification is developed and undertaken on a personal computer using the Python open source programming language.
Dynamic Threshold based Cooperative Spectrum Sensing using Coalitional Game for CRNs

Cooperative Spectrum Sensing (CSS) in Cognitive Radio Network (CRN) is proven to enhance the efficiency of spectrum sensing by using spatial diversity of secondary users (SUs). However, during designing of CSS schemes the incorporation of dynamic interaction among the SUs and the selection of appropriate sensing threshold play an important role. This paper contributes in the following way by - (i) proposing a coalitional game theory based model for CSS, (ii) proposing a distance based adaptive sensing threshold determination technique and (iii) considering cooperation overhead in CSS due to reporting error. The CSS model is evaluated via simulation studies and the results obtained show that proposed model improves performance of spectrum sensing in terms of detection probability compared to similar CSS techniques.
Persistent Nociception Facilitates the Extinction of Morphine-Induced Conditioned Place Preference.

BACKGROUND As opioid abuse and addiction have developed into a major national health crisis, prescription of opioids for pain management has become more controversial. However, opioids do help some patients by providing pain relief and improving the quality of life. To better understand the addictive properties of opioids under chronic pain conditions, we used a conditioned place preference (CPP) paradigm to examine the rewarding properties of morphine in rats with persistent nociception. METHODS Spared nerve injury (SNI) model was used to induce persistent nociception in rats. Nociceptive behavior was assessed by von Frey test. CPP test was used to examine the rewarding properties of morphine. RESULTS Our findings are as follows: (1) SNI rats did not show a difference compared with sham rats in magnitude of morphine-induced CPP 1 day after last morphine injection (2-way analysis of variance; for SNI versus sham, F[1,42] = 0.014, P = .91; and 95% confidence intervals for difference of means, -5.9 [-58 to 46], 0.76 [-51 to 53], and 0.90 [-51 to 53] for 2.5, 5, and 10 mg/kg, respectively); (2) increasing morphine dosage (2.5, 5, and 10 mg/kg) did not further increase the magnitude of CPP in both sham and SNI rats (for dosage: F[2,42] = 0.94, P = .40); and (3) morphine-induced CPP persisted in sham rats but extinguished in SNI rats when tested at 8 days after last morphine injection (for sham versus SNI: Bonferroni correction, P < .006 for both 5 and 10 mg/kg doses; and 95% confidence intervals for difference of means, 80.3 [19.7-141] and 87.0 [26.3-148] for 5 and 10 mg/kg, respectively). CONCLUSIONS Our data provide new evidence supporting the notion that the brain's reward circuitry changes in the context of persistent pain. This observational study suggests that future investigation into the neurobiology of opioid reward requires consideration of the circumstances in which opioid analgesics are administered.
Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning

Learning in sparse reward settings remains a challenge in Reinforcement Learning, which is often addressed by using intrinsic rewards. One promising strategy is inspired by human curiosity, requiring the agent to learn to predict the future. In this paper a curiosity-driven agent is extended to use these predictions directly for training. To achieve this, the agent predicts the value function of the next state at any point in time. Subsequently, the consistency of this prediction with the current value function is measured, which is then used as a regularization term in the loss function of the algorithm. Experiments were made on grid-world environments as well as on a 3D navigation task, both with sparse rewards. In the first case the extended agent is able to learn significantly faster than the baselines.
Analytics in Supply Change Management: Is There a Dark Side?

: The growing ability to collect real-time data combined with a desire to optimize efficiency and effectiveness have pushed the organizations to realize the value of analytics and intelligent supply chain. This study offers an overview of supply chain management and the critical role of analytics to enhance supply chain processes and, subsequently, performance. While efficiency and effectiveness is the ultimate measure of success for any organization, this study recommends a look at the consequences of such success in the global economy. As the world of commerce increasingly relies on outsourcing and the cheap labor market, the role of technology to expedite the exploitation of that market should be scrutinized. It is time to discuss not only the contributions of analytics to facilitate supply chain management but also its impact on exploitation through fierce competition among suppliers operating in developing countries.
Meta‐Analysis of Genetic Influences on Initial Alcohol Sensitivity

BACKGROUND Previous studies indicate that low initial sensitivity to alcohol may be a risk factor for later alcohol misuse. Evidence suggests that initial sensitivity is influenced by genetic factors, but few molecular genetic studies have been reported. METHODS We conducted a meta-analysis of 2 population-based genome-wide association studies of the Self-Rating of the Effects of Alcohol scale. Our final sample consisted of 7,339 individuals (82.3% of European descent; 59.2% female) who reported having used alcohol at least 5 times. In addition, we estimated single nucleotide polymorphism (SNP)-based heritability and conducted a series of secondary aggregate genetic analyses. RESULTS No individual locus reached genome-wide significance. Gene and set based analyses, both overall and using tissue-specific expression data, yielded largely null results, and genes previously implicated in alcohol problems and consumption were overall not associated with initial sensitivity. Only 1 gene set, related to hormone signaling and including core clock genes, survived correction for multiple testing. A meta-analysis of SNP-based heritability resulted in a modest estimate of h SNP 2 = 0.19 (SE = 0.10), though this was driven by 1 sample (N = 3,683, h SNP 2 = 0.36, SE = 0.14, p = 0.04). No significant genetic correlations with other relevant outcomes were observed. CONCLUSIONS Findings yielded only modest support for a genetic component underlying initial alcohol sensitivity. Results suggest that its biological underpinnings may diverge somewhat from that of other alcohol outcomes and may be related to core clock genes or other aspects of hormone signaling. Larger samples, ideally of prospectively assessed samples, are likely necessary to improve gene identification efforts and confirm the current findings.
Copenhagen Prospective Personalized Oncology (CoPPO)—Clinical Utility of Using Molecular Profiling to Select Patients to Phase I Trials

Purpose: We evaluated the clinical benefit of tumor molecular profiling to select treatment in the phase I setting. Experimental Design: Patients with advanced solid cancers and exhausted treatment options referred to a phase I unit were included in a prospective, single-center, single-arm open-label study (NCT02290522). Tumor biopsies were obtained for comprehensive genomic analysis including whole-exome sequencing and RNA sequencing. When possible, patients were treated with regimen matched to the genomic profile. Primary endpoint was progression-free survival (PFS). Results: From May 2013 to January 2017, a total of 591 patients were enrolled, with 500 patients undergoing biopsy. Genomic profiles were obtained in 460 patients and a potential actionable target was identified in 352 (70%) of 500 biopsied patients. A total of 101 patients (20%) received matched treatment based on either gene mutations or RNA expression levels of targets available in early clinical trials or off-label treatment. Objective response according to RECIST1.1 was observed in 15 of 101 patients (0% complete response, 15% partial response), with a median PFS of 12 weeks (95% confidence interval, 9.9–14.4). Conclusions: Our study supports the feasibility of genomic profiling to select patients in the phase I setting and suggests that genomic matching can be beneficial for a minor subset of patients with no other treatment options. Randomized studies may validate this assumption. See related commentary by Ratain, p. 1136
Disease of the Year: Differential Diagnosis of Uveitic Macular Edema

ABSTRACT Uveitic cystoid macular edema (UME) is an important cause of visual morbidity among patients with both infectious and non-infectious uveitis. UME may be associated in more than 30% cases of active uveitis. However, even patients with minimal features of intraocular inflammation may develop recurrent or chronic UME. Therefore, the evaluation and management of UME in patients with uveitis may be challenging. A number of vitreoretinal pathologies may result in UME and accumulation of fluid in the intra- or subretinal space. These need to be carefully distinguished from each other so that appropriate management can be initiated. All types of uveitis, including anterior uveitis (where the primary site of inflammation is not in the posterior segment) can present with UME. Other conditions such as diabetes, and surgery, can present with macular edema. This index review highlights various differential diagnoses of UME and provides illustrative case examples with multimodal imaging evaluation.