Miracle-dz/UniFAR-FTUs huggingface dataset:
Fgf21 Impairs Adipocyte Insulin Sensitivity in Mice Fed a Low-Carbohydrate, High-Fat Ketogenic Diet

Background A low-carbohydrate, high-fat ketogenic diet (KD) induces hepatic ketogenesis and is believed to affect energy metabolism in mice. As hepatic Fgf21 expression was markedly induced in mice fed KD, we examined the effects of KD feeding on metabolism and the roles of Fgf21 in metabolism in mice fed KD using Fgf21 knockout mice. Methodology/Principal Findings We examined C57BL/6 mice fed KD for 6 or 14 days. Blood β-hydroxybutyrate levels were greatly increased at 6 days, indicating that hepatic ketogenesis was induced effectively by KD feeding for 6 days. KD feeding for 6 and 14 days impaired glucose tolerance and insulin sensitivity, although it did not affect body weight, blood NEFA, and triglyceride levels. Hepatic Fgf21 expression and blood Fgf21 levels were markedly increased in mice fed KD for 6 days. Blood β-hydroxybutyrate levels in the knockout mice fed KD for 6 days were comparable to those in wild-type mice fed KD, indicating that Fgf21 is not required for ketogenesis. However, the impaired glucose tolerance and insulin sensitivity caused by KD feeding were improved in the knockout mice. Insulin-stimulated Akt phosphorylation was significantly decreased in the white adipose tissue in wild-type mice fed KD compared with those fed normal chow, but not in the muscle and liver. Its phosphorylation in the white adipose tissue was significantly increased in the knockout mice fed KD compared with wild-type mice fed KD. In contrast, hepatic gluconeogenic gene expression in Fgf21 knockout mice fed KD was comparable to those in the wild-type mice fed KD. Conclusions/Significance The present findings indicate that KD feeding impairs insulin sensitivity in mice due to insulin resistance in white adipose tissue. In addition, our findings indicate that Fgf21 induced to express by KD is a negative regulator of adipocyte insulin sensitivity in adaptation to a low-carbohydrate malnutritional state.
Combining photothermal therapy and immunotherapy against melanoma by polydopamine-coated Al2O3 nanoparticles

Photothermal therapy (PTT) can be an effective antitumor therapy, but it may not completely eliminate tumor cells, leading to the risk of recurrence or metastasis. Here we describe nanocarriers that allow combination therapy involving PTT and immunotherapy. Nanocarriers are prepared by coating Al2O3 nanoparticles with non-toxic, biodegradable polydopamine, which shows high photothermal efficiency. A near-infrared laser irradiation can kill the majority of tumor tissues, resulting in the release of tumor-associated antigens. The Al2O3 within the nanoparticles, together with CpG, acts as an adjuvant to trigger robust cell-mediated immune responses that can help eliminate the residual tumor cells and reduce the risk of tumor recurrence. Methods: The characteristics and photothermal performance of polydopamine-coated Al2O3 nanoparticles were examined after one-step preparation. Then we studied their internalization, photothermal toxicity and immunostimulatory activity in vitro. For in vivo experiments, these nanocarriers were injected directly into B16F10 melanoma allografts in mice to ensure specific localization. After photothermal irradiation on day 0, mice were subcutaneously injected with CpG adjuvant on day 1, 3 and 5. Tumor volumes and number of living mice were recorded every two days. Moreover, various immune responses induced by our combined therapy were tested for mechanism research. Results: 50% of mice after our combined treatment successfully achieved the goal of tumor eradication, and survived for 120 days, which was the end point of the experiment. Mechanism studies demonstrated the combined therapy efficiently led to dendritic cell maturation, resulting in the secretion of antibodies and cytokines as well as the proliferation of splenocytes and lymphocytes for anti-tumor immunotherapy. Conclusion: Taken together, these results demonstrated the promise of our combined photothermal therapy and immunotherapy for tumor shrinkage, which merited further research.
Physical Properties of Silicone Gel Breast Implants

Abstract Background Surgical applications using breast implants are individualized operations to fill and shape the breast. Physical properties beyond shape, size, and surface texture are important considerations during implant selection. Objectives Compare form stability, gel material properties, and shell thickness of textured shaped, textured round, and smooth round breast implants from 4 manufacturers: Allergan, Mentor, Sientra, and Establishment Labs, through bench testing. Methods Using a mandrel height gauge, form stability was measured by retention of dimensions on device movement from a horizontal to vertical supported orientation. Dynamic response of the gel material (gel cohesivity, resistance to gel deformation, energy absorption) was measured using a synchronized target laser following application of graded negative pressure. Shell thickness was measured using digital thickness gauge calipers. Results Form stability, gel material properties, and shell thickness differed across breast implants. Of textured shaped devices, Allergan Natrelle 410 exhibited greater form stability than Mentor MemoryShape and Sientra Shaped implants. Allergan Inspira round implants containing TruForm 3 gel had greater form stability, higher gel cohesivity, greater resistance to gel deformation, and lower energy absorption than those containing TruForm 2 gel and in turn, implants containing TruForm 1 gel. Shell thickness was greater for textured vs smooth devices, and differed across styles. Conclusions Gel cohesivity, resistance to gel deformation, and energy absorption are directly related to form stability, which in turn determines shape retention. These characteristics provide information to aid surgeons choosing an implant based on surgical application, patient tissue characteristics, and desired outcome.
Controlling Global Statistics in Recurrent Neural Network Text Generation

Recurrent neural network language models (RNNLMs) are an essential component for many language generation tasks such as machine translation, summarization, and automated conversation. Often, we would like to subject the text generated by the RNNLM to constraints, in order to overcome systemic errors (e.g. word repetition) or achieve application-specific goals (e.g. more positive sentiment). In this paper, we present a method for training RNNLMs to simultaneously optimize likelihood and follow a given set of statistical constraints on text generation. The problem is challenging because the statistical constraints are defined over aggregate model behavior, rather than model parameters, meaning that a straightforward parameter regularization approach is insufficient. We solve this problem using a dynamic regularizer that updates as training proceeds, based on the generative behavior of the RNNLMs. Our experiments show that the dynamic regularizer outperforms both generic training and a static regularization baseline. The approach is successful at improving word-level repetition statistics by a factor of four in RNNLMs on a definition modeling task. It also improves model perplexity when the statistical constraints are $n$-gram statistics taken from a large corpus.
Reply to Kovacs et al.: Concerning acute inflammatory response following focused ultrasound and microbubbles in the brain

We thank Kovacs et al. for their editorial [15] on our recent publication [1] regarding the influence of microbubble (MB) dose on acute inflammatory response (AIR) following focused ultrasound (FUS)-induced increases in blood-brain barrier (BBB) permeability. We wish to address key points of disparity in interpretations of the data presented by us and in a previous report from Kovacs et al [2]. All data published to date indicate that an AIR follows increased BBB permeability induced by FUS + MBs [1–5]; however, there are discrepancies in the reported magnitude and duration of this response. To address this, we approximated sonication parameters shown by Kovacs et al. in PNAS to induce a substantial AIR [2] and compared this to parameters that better reflect those used in ongoing clinical trials (ClinicalTrials.gov identifiers: NCT02343991, NCT02986932, NCT03119961) and previous preclinical research [6–10]. Results indicated that MB dose and acoustic pressure impacts the degree of AIR, as measured by changes in the expression of NFκB pathway-related genes. We demonstrated a high degree of correlation between gene expression changes reported by Kovacs et al. and those measured in the sonication scheme designed to approximate this work (r2 = 0.84; p = 0.00001). Importantly, when MB dose was reduced, and peak negative pressure was calibrated to avoid inertial cavitation, a substantial reduction in the magnitude of AIR was observed. We believe this demonstrates that the permeability of the BBB can be transiently increased using FUS + MBs with a minimal AIR, as well as highlights the need for both careful attention to sonication parameters and the use of acoustic feedback control. In their editorial, Kovacs et al. discuss differences in sonication parameters between the two studies [1,2] and conclude that these differences preclude a direct comparison of results. The first major difference is in MB dose. To approximate equivalent doses, necessitated by a disparity in MB type between the two studies, we used clinical imaging dose as a normalizing factor. Optison was administered at ~500 μL/kg versus Definity administered at 100 μL/kg, in Kovacs and McMahon, respectively. Both doses equate to 10 times the clinical imaging dose of their respective MB type. Kovacs et al. correctly highlight differences in MB number/kg between studies. However, using MB number as a method of comparing doses of different MB types necessarily considers all cavitation nuclei as equivalent; the assumption that each Definity MB will respond like an Optison MB, or vice versa, is somewhat over simplistic since differences in their shell properties and size alter their response to ultrasound [11,12]. Additionally, McDannold et al. have previously demonstrated that the probability of increased blood-brain barrier (BBB) permeability following FUS is approximately equivalent for Definity and Optison at their respective clinical imaging doses over a range of pressures [6]. Ivyspring
Commonsense mining as knowledge base completion? A study on the impact of novelty

Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by recent work by Li et al., we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method that outperforms the previous state of the art on predicting more novel triples.
One-bit compressive sensing vs. multi-bit compressive sensing for cognitive radio networks

Compressive sensing has been proposed as an alternative solution for signal acquisition to directly acquire compressible signals at a rate lower than the Nyquist rate. Compressive sensing techniques can be classified into two categories: one-bit compressive sensing and multi-bit compressive sensing. One-bit compressive sensing performs by acquiring only the sign of each measurement while multi-bit compressive sensing performs by acquiring multiple bits. In this paper, we analyze the performance of each category. We compare their efficiencies using metrics that cover the most aspects of the compressive sensing performance in cognitive radio networks. Simulation results show that one-bit compressive sensing can outperform the multi-bit compressive sensing in terms of speed, robustness to noise, complexity, and reconstruction success rate.
Design of an adaptive feed-forward control scheme for the DC bus voltage control of single phase grid connected converters

This paper presents a robust control strategy for DC bus voltage control of single-phase Grid connected Converters (GcCs). This control strategy is based on a PI controller with a feed-forward of the DC load (or source) current estimated through an adaptive gain matrix based Luenberger observer. The aim of the proposed control strategy is to ensure simultaneously the following control objectives: 1) an accurate DC bus voltage control, 2) a reduced level of DC bus voltage fluctuations during transient conditions caused by sudden and sever changes of the transferred active power and 3) a reduced grid current harmonics during steady state operation. The validity and effectiveness of the designed DC bus voltage controller was evaluated and confirmed through experimental tests.
A Natural Language Driven Approach for Automated Web API Development: Gherkin2OAS

Speeding up the development process of Web Services, while adhering to high quality software standards is a typical requirement in the software industry. This is why industry specialists usually suggest "driven by" development approaches to tackle this problem. In this paper, we propose such a methodology that employs Specification Driven Development and Behavior Driven Development in order to facilitate the phases of Web Service requirements elicitation and specification. Furthermore, we introduce gherkin2OAS, a software tool that aspires to bridge the aforementioned development approaches. Through the suggested methodology and tool, one may design and build RESTful services fast, while ensuring proper functionality.
N 94-34045 Reinforcement Learning in Scheduling

The goal of this research is to apply reinforcement learning methods to real-world problems like scheduling. In this preliminary paper, we show that learning to solve scheduling problems such as the Space Shuttle Payload Processing and the Automatic Guided Vehicle (AGV) scheduling can be usefully studied in the reinforcement learning framework. We discuss some of the special challenges posed by the scheduling domain to these methods and propose some possible solutions we plan to implement.
Interpenetrating Cooperative Localization in Dynamic Connected Vehicle Networks

In this paper, we proposed the Interpenetrating Cooperative Localization (ICL) method to enhance the localization accuracy in dynamic connected vehicle networks. This mechanism makes the information from one group of connected vehicles interpenetrate to other groups without full communication between all nodes, thus improving the utility of information in a low connected vehicle penetration situation. We tested the approach using the dynamic traffic data collected in the Safety Pilot Model Deployment program in Ann Arbor Michigan, USA, with dynamic changing networks due to the traveling of vehicles and packet drops of the Dedicated Short-Range Communication. Results show enhancement of localization accuracy with errors reduced by up to 70 % even in complex dynamic scenarios.
The Effect of Monetary Policy on Housing Tenure Choice as an Explanation for the Price Puzzle

In this paper we provide an alternative explanation for the price puzzle (Sims 1992) based on the effect of monetary policy on housing tenure choice and the weight of the shelter component in overall CPI. In the presence of nominal or financial frictions, when interest rates increase, the real cost of owning a house increases, and this increase may make some people prefer to rent instead of buying. This change in consumption behavior increases the price of rents relative to other goods. Starting in 1983, homeownership costs are based on a measure of implied owner equivalent rent, which is calculated using observed house rents. This change implies that, directly and indirectly, prices in the rental market almost entirely command the shelter component of CPI, which weighs around 30% in the overall index. When we take these two pieces into account and use CPI net of shelter services as a measure of inflation, we obtain impulse responses of prices to a monetary contraction shock more in line with what is predicted by theory. In addition, our results also suggest that inflation is much less persistent than what is implied by analyses using a measure of inflation that includes shelter services. Our results pass a long list of robustness check exercises and compare well against other explanations of the price puzzle.
Sign-Full Random Projections

The method of 1-bit (“sign-sign”) random projections has been a popular tool for efficient search and machine learning on large datasets. Given two D-dim data vectors u, v ∈ ℝD, one can generate x = ∑i=1D uiri, and y = ∑i=1D viri, where ri ∼ N(0, 1) iid. Then one can estimate the cosine similarity ρ from sgn(x) and sgn(y). In this paper, we study a series of estimators for “sign-full” random projections. First we prove E(sgn(x)y) = √2/πρ, which provides an estimator for ρ. Interestingly this estimator can be substantially improved by normalizing y. Then we study estimators based on E (y−1x≥0 + y+1x<0) and its normalized version. We analyze the theoretical limit (using the MLE) and conclude that, among the proposed estimators, no single estimator can achieve (close to) the theoretical optimal asymptotic variance, for the entire range of ρ. On the other hand, the estimators can be combined to achieve the variance close to that of the MLE. In applications such as near neighbor search, duplicate detection, knn-classification, etc, the training data are first transformed via random projections and then only the signs of the projected data points are stored (i.e., the sgn(x)). The original training data are discarded. When a new data point arrives, we apply random projections but we do not necessarily need to quantize the projected data (i.e., the y) to 1-bit. Therefore, sign-full random projections can be practically useful. This gain essentially comes at no additional cost.
A Tree Search Algorithm for Sequence Labeling

In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word. Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of LSTMs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of the whole sentence are produced. The policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a better search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters. Our work is the first to apply the MCTS enhanced MDP model to the sequence tagging task. We show that MM-Tag can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS. Experimental results show based on a chunking benchmark showed that MM-Tag outperformed the state-of-the-art sequence tagging baselines including CRF and CRF with LSTM.
Extracting the Most Weighted Throughput in UAV Empowered Wireless Systems With Nonlinear Energy Harvester

With the maturity of unmanned aerial vehicle (UAV) technology, this work investigates the integration of UAV into wireless communication systems. Since the UAV is powered by a capacity-limited battery, this work proposes to use the radio energy harvesting technology at the UAV in order to extend the lifetime of UAV empowered base station. To extract the most weighted throughput of UAV empowered wireless systems, the dirty paper coding scheme and information-theoretic uplinkdownlink channel duality are exploited to propose an extracting the most weighted throughput algorithm. Numerical results are used to verify the proposed algorithm.
2 2 N ov 2 01 7 Inductive Representation Learning in Large Attributed Graphs

Graphs (networks) are ubiquitous and allow us to model entities (nodes) and the dependencies (edges) between them. Graph data is often observed directly in the natural world (e.g., biological or social networks) [20] or constructed from non-relational data by deriving a metric space between entities and retaining only the most significant edges [5, 22]. Learning a useful feature representation from graph data lies at the heart and success of many machine learning tasks such as classification, anomaly detection, link prediction, among many others. Many existing techniques use random walks as a basis for learning features or estimating the parameters of a graph model for a downstream prediction task. Examples include recent node embedding methods such as DeepWalk [12], node2vec [4], as well as graph-based deep learning algorithms. However, the simple random walk used by these methods is fundamentally tied to the identity of the node. This has three main disadvantages. First, these approaches are inherently transductive and do not generalize to unseen nodes and other graphs [15]. Second, they are not space-efficient as a feature vector is learned for each node which is impractical for large graphs. Third, most of these approaches lack support for attributed graphs.
A mini outburst from the nightside of comet 67P/Churyumov-Gerasimenko observed by the OSIRIS camera on Rosetta

On 12 March 2015 the OSIRIS WAC camera onboard the ESA Rosetta spacecraft orbiting comet 67P/Churyumov-Gerasimenko observed a small outburst originating from the Imhotep region at the foot of the big lobe of the comet. These measurements are unique since it was the first time that the initial phase of a transient outburst event could be directly observed. Aims. We investigate the evolution of the dust jet in order to derive clues about the outburst source mechanism and the ejected dust particles, in particular the dust mass, dust-to-gas ratio and the particle size distribution. Methods. Analysis of the images and of the observation geometry using comet shape models in combination with gasdynamic modeling of the transient dust jet were the main tools used in this study. Synthetic images were computed for comparison with the observations. Results. Analysis of the geometry revealed that the source region was not illuminated until 1.5 h after the event implying true nightside activity was observed. The outburst lasted for less than one hour and the average dust production rate during the initial four minutes was of the order of 1 kg/s. During this time the outburst dust production rate was approximately constant, no sign for an initial explosion could be detected. For dust grains between 0.01-1 mm a power law size distribution characterized by an index of about 2.6 provides the best fit to the observed radiance profiles. The dust-to-gas ratio of the outburst jet is in the range 0.6-1.8.