Miracle-dz/UniFAR-FTUs huggingface dataset:
A CTD–Pfizer collaboration: manual curation of 88 000 scientific articles text mined for drug–disease and drug–phenotype interactions

Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88 629 articles relating over 1 200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity. In 1 year, CTD biocurators curated 2 54 173 toxicogenomic interactions (1 52 173 chemical–disease, 58 572 chemical–gene, 5 345 gene–disease and 38 083 phenotype interactions). All chemical–gene–disease interactions are fully integrated with public CTD, and phenotype interactions can be downloaded. We describe Pfizer’s text-mining process to collate the articles, and CTD’s curation strategy, performance metrics, enhanced data content and new module to curate phenotype information. As well, we show how data integration can connect phenotypes to diseases. This curation can be leveraged for information about toxic endpoints important to drug safety and help develop testable hypotheses for drug–disease events. The availability of these detailed, contextualized, high-quality annotations curated from seven decades’ worth of the scientific literature should help facilitate new mechanistic screening assays for pharmaceutical compound survival. This unique partnership demonstrates the importance of resource sharing and collaboration between public and private entities and underscores the complementary needs of the environmental health science and pharmaceutical communities. Database URL: http://ctdbase.org/
Design and Implementation of Fuzzy Policy Gradient Gait Learning Method for Walking Pattern Generation of Humanoid Robots

The design and implementation of Fuzzy Policy Gradient Learning (FPGL) method for humanoid robot is proposed in this paper. This paper not only introduces the phases of the humanoid robot walking, but also improves and parameterizes the gait pattern of the robot. FPGL is an integrated machine learning method based on Policy Gradient Reinforcement Learning (PGRL) and fuzzy logic concept in order to improve the efficiency and speed of gait learning computation. The result of the experiment shows that FPGL method can train the gait pattern from 9.26 mm/s walking speed to 162.27 mm/s within an hour. The training data of experiments also shows that this method could improve the efficiency of basic PGRL method up to 13%. The effect of arm movement to reduce the tilt of the trunk is also proved by the experimental results. All the results successfully demonstrate the feasibility and the flexibility of the proposed method.
Use and success of pharmacologic and nonpharmacologic strategies for sleep problems

Purpose: The purpose of this web‐based, quantitative, descriptive, correlational study was to identify frequency of use and success of strategies to alleviate sleep problems, to determine whether there is any relationship between use and success (perceived effectiveness) of strategies, on the one hand, and adults’ gender, age, income, or education, on the other. Data sources: Data were collected from a web‐based survey that examined strategy use and success to alleviate insomnia. Conclusions: The most frequently used strategies and those perceived as successful in alleviating sleep problems included medications and sleep hygiene strategies. Users of some strategies differed from nonusers on gender, age, income, and education. Implications for practice: Results from this study can be used to advise patients with sleep problems about choosing strategies to help alleviate those problems to reduce insomnia and to lower the risks of comorbid illness and healthcare costs. This study contributes to the current available findings that focus on determining which strategies are being used to alleviate sleep problems, those perceived as the most successful, and identifying strategy use and success based on demographics.
NETWORK MONITORING AND DATA ANALYSIS IN WIRELESS NETWORKS

Various wireless network technologies have been created to meet the ever-increasing demand for wireless access to the Internet, such as wireless local area network, cellular network and sensor network and many more. The communication devices have transformed from large computational servers to small wireless hand-held devices, ranging from laptops, tablets, smartphones to small sensors. The advances of these wireless networks (e.g., faster network speed) and their intensive usages result in an enormous growth of network data in terms of volume, diversity, and complexity. All of these changes have raised complicated network measurement and management issues. In this proposal, I first investigate the impact of wireless local area network in home and network security monitoring. Then I propose effective and efficient approaches in analyzing network data, particularly those generated by smartphone apps and sensor networks.
Effects of Amendment with Ferrihydrite and Gypsum on the Structure and Activity of Methanogenic Populations in Rice Field Soil

ABSTRACT Methane emission from paddy fields may be reduced by the addition of electron acceptors to stimulate microbial populations competitive to methanogens. We have studied the effects of ferrihydrite and gypsum (CaSO4 · 2H2O) amendment on methanogenesis and population dynamics of methanogens after flooding of Italian rice field soil slurries. Changes in methanogen community structure were followed by archaeal small subunit (SSU) ribosomal DNA (rDNA)- and rRNA-based terminal restriction fragment length polymorphism analysis and by quantitative SSU rRNA hybridization probing. Under ferrihydrite amendment, acetate was consumed efficiently (<60 μM) and a rapid but incomplete inhibition of methanogenesis occurred after 3 days. In contrast to unamended controls, the dynamics of Methanosarcina populations were largely suppressed as indicated by rDNA and rRNA analysis. However, the low acetate availability was still sufficient for activation of Methanosaeta spp. , as indicated by a strong increase of SSU rRNA but not of relative rDNA frequencies. Unexpectedly, rRNA amounts of the novel rice cluster I (RC-I) methanogens increased significantly, while methanogenesis was low, which may be indicative of transient energy conservation coupled to Fe(III) reduction by these methanogens. Under gypsum addition, hydrogen was rapidly consumed to low levels (∼0.4 Pa), indicating the presence of a competitive population of hydrogenotrophic sulfate-reducing bacteria (SRB). This was paralleled by a suppressed activity of the hydrogenotrophic RC-I methanogens as indicated by the lowest SSU rRNA quantities detected in all experiments. Full inhibition of methanogenesis only became apparent when acetate was depleted to nonpermissive thresholds (<5 μM) after 10 days. Apparently, a competitive, acetotrophic population of SRB was not present initially, and hence, acetotrophic methanosarcinal populations were less suppressed than under ferrihydrite amendment. In conclusion, although methane production was inhibited effectively under both mitigation regimens, different methanogenic populations were either suppressed or stimulated, which demonstrates that functionally similar disturbances of an ecosystem may result in distinct responses of the populations involved.
Cancellation of nonlinearities in bandpass QAM systems

An orthogonalized input-output characterization of nonlinear bandpass quadrature amplitude modulation systems with memory is developed. The orthogonal expansion is used with a nonlinear canceller structure to counteract the effect of nonlinear distortion introduced by high-power amplifiers at the transmitter. It is shown through simulations that when the transmitted symbols are known at the canceller, relatively simple structures can eliminate the effect of the nonlinearity. Even when the transmitted symbols are not known at the canceller, the improvement in performance with the nonlinear canceller, with respect to a linear receiver, is significant. >
Implications for the Constrained MSSM from a new prediction for b to s gamma

We re-examine the properties of the Constrained MSSM in light of updated constraints, paying particular attention to the impact of the recent substantial shift in the Standard Model prediction for → Xsγ. With the help of a Markov Chain Monte Carlo scanning technique, we vary all relevant parameters simultaneously and derive Bayesian posterior probability maps. We find that the case of μ > 0 remains favored, and that for μ < 0 it is considerably more difficult to find a good global fit to current constraints. In both cases we find a strong preference for a focus point region. This leads to improved prospects for detecting neutralino dark matter in direct searches, while superpartner searches at the LHC become more problematic, especially when μ < 0. In contrast, prospects for exploring the whole mass range of the lightest Higgs boson at the Tevatron and the LHC remain very good, which should, along with dark matter searches, allow one to gain access to the otherwise experimentally challenging focus point region. An alternative measure of the mean quality-of-fit which we also employ implies that present data are not yet constraining enough to draw more definite conclusions. We also comment on the dependence of our results on the choice of priors and on some other assumptions.
Integrating shape and Edge Histogram Descriptor with Stationary Wavelet Transform for Effective Content Based Image Retrieval

In this paper we propose a hybrid approach for Effective Content Based Image Retrieval based on texture and shape feature. Towards this, first Stationary Wavelet Transform (SWT) is applied on query image to extract horizontal, vertical and diagonal detail matrices. Stationary Wavelet Transform is used because of its translational invariant property. After this Edge Histogram Descriptor (EHD) is used to exploit the absolute location of edges in the image as well their global composition. To aid the retrieval process, five different shape measures have also been included. Finally Euclidean distance is used to retrieve the relevant results. Experimental results show that the combination of SWT and EHD techniques provides significant improvement over existing methods thereby increasing the retrieval efficiency.
Histopathological Osteomyelitis Evaluation Score (HOES) – an innovative approach to histopathological diagnostics and scoring of osteomyelitis

Background: Treatment and diagnosis of osteomyelitis are still a challenging problem for surgeons, microbiologists and histopathologists. A direct microbiological detection of bacteria in tissues is still gold standard, but it is not always successful for example in chronic osteomyelitis and/or when an antibiotic treatment has already been started or in cases of low virulent bacteria. The goal of this study was to define diagnostic criteria of osteomyelitis, the inflammatory regression of osteomyelitis (“osteomyelitis score”) under specific therapy by the correlation of histopathological and microbiological and clinical standard tests. Methods: In this retrospective analysis patients with medical history and clinically clear signs of bacterial infection and osteomyelitis underwent surgery between 01.01. 2013 and 31.12. 2012. Their formal consent was given. Tissue samples were taken during surgery according to defined criteria including surgical interventions. Histopathological diagnosis was carried out by conventional techniques based on defined criteria of bacterial infection in connective tissue, peri-implant membrane and bone. These results were carried out in tables by numbers representing the histopathological criteria of acute osteomyelitis (A1 to A3) as well as the chronic criteria (C1 and C2) in a semiquantitative way (scale 0 to 3). On the other hand a notational, graduated histopathological report was performed. Preoperative clinical diagnosis, perioperative macroscopic diagnosis, histopathological and microbiological findings were correlated. Results: Histopathological samples of 52 surgical interventions based on the preoperative diagnosis “osteomyelitis” (AOM, ECOM or COM) were included. 37 times preoperatively signs of a chronic osteomyelitis (COM), 10 times preoperatively acute osteomyelitis (AOM) was diagnosed. Another 5 patients were preoperatively diagnosed as acute exacerbated osteomyelitis (ECOM). The correlation of the histopathological infection including the inflammatory activity and microbiological detection of bacteria was 57%. The correlation between preoperative diagnosis and histopathological findings was 68%. Conclusion: The relatively small 68% correlation between clinical preoperative and histopathological diagnosis and 57% correlation between preoperative clinical diagnosis and microbiological findings indicates: Clinical findings are not sufficient for the diagnosis “osteomyelitis”. Clinical findings are not sufficient for the differentiation between AOM, ECOM and COM. Histopathological analysis is the critical factor for the diagnosis (“osteomyelitis”) and differential diagnosis (AOM vs. COM). Histopathological analysis represents the basis for further treatment. HOES facilitates the classification of the histopathological findings. HOES is a sufficient tool for the treating physician in order to define the further treatment.
Exploring transfer learning for gastrointestinal bleeding detection on small-size imbalanced endoscopy images

The success of Convolutional Neural Network (CNN) is attributed to their ability to learn rich midlevel image representations as opposed to hand-crafted low-level features used in many natural image classification methods. Learning CNN, however, amounts to estimating millions of parameters and requires a very large number of annotated image samples. In this paper, we explored transfer learning for gastrointestinal bleeding detection on small-size imbalanced endoscopy images, and showed how image representations learned with CNN on large-scale annotated datasets can be efficiently transferred to other tasks with limited amount of training data. We first transferred pre-trained Inception V3 model trained on the ImageNet dataset to compute mid-level image representation, and then fine-tuned the trained model with labeled endoscopy images, and resumed training from already learned weights. Additionally, we introduce both data augmentation and image resampling to increase the size of the training database and the positive sample rate to perform the Transfer Learning. Our results showed that our transfer learning method produces the best performance on AUC (the area under the receiver operating curve), Precision, Recall and Accuracy as compared to both the hand-crafted feature based method and training CNN model from-scratch method.
Solid-State Fermentation as a Novel Paradigm for Organic Waste Valorization: A Review

The abundance of organic solid waste throughout the world has become a common issue that needs complete management at every level. Also, the scarcity of fuel and the competition between food and substance as an alternative to a petroleum-based product has become a major problem that needs to be properly handled. An urge to find renewable substances for sustainable development results in a strategy to valorize organic solid waste using solid state fermentation (SSF) and to manage the issue of solid wastes in a green approach. This paper reviews management of solid wastes using SSF, with regard to its current application, advantages and challenges, downstream processing in SSF, economic viewpoint, and future perspectives.
Broker Selection Strategies in Interoperable Grid Systems

The increasing demand for resources of the high performance computing systems has led to new forms of collaboration of distributed systems such as interoperable grid systems that contain and manage their own resources. While with a single grid domain one of the most important tasks is the selection of the most appropriate set of resources to dispatch a job, in an interoperable grid environment this problem shifts to selecting the most appropriate domain containing the requiring resources for the job. In this paper, we present and evaluate broker selection strategies for interoperable grid systems. They use aggregated resource information as well as dynamic performance information of the underlying scheduling layers. From our evaluations performed with simulation tools, we conclude that aggregation techniques do not penalize performance significantly, and that delegating part of the scheduling responsibilities to the underlying scheduling layers is a good way to balance the load among the different grid systems.
A technical perspective of knowledge management in collaborative software maintenance environment

Knowledge management (KM) is critical in Software Maintenance (SM) organizations to provide an environment for creating and sharing knowledge. SM environment is complex, knowledge-driven and highly collaborative. Issue such as inadequate knowledge is still regarded as a major challenge in SM. To find out more on the above limitation in term of technical perspective, we study the activities of a maintenance organization and conducted a survey and discussion. Based on the results, we conclude that domain and technical knowledge are important to maintainers, but are often not available. Also, usefulness of tools to acquire and share knowledge are limited, mainly due to lack of integration between tools.
Generation and Comparative Analysis of ∼3.3 Mb of Mouse Genomic Sequence Orthologous to the Region of Human Chromosome 7q11.23 Implicated in Williams Syndrome

Williams syndrome is a complex developmental disorder that results from the heterozygous deletion of a approximately 1.6-Mb segment of human chromosome 7q11.23. These deletions are mediated by large (approximately 300 kb) duplicated blocks of DNA of near-identical sequence. Previously, we showed that the orthologous region of the mouse genome is devoid of such duplicated segments. Here, we extend our studies to include the generation of approximately 3.3 Mb of genomic sequence from the mouse Williams syndrome region, of which just over 1.4 Mb is finished to high accuracy. Comparative analyses of the mouse and human sequences within and immediately flanking the interval commonly deleted in Williams syndrome have facilitated the identification of nine previously unreported genes, provided detailed sequence-based information regarding 30 genes residing in the region, and revealed a number of potentially interesting conserved noncoding sequences. Finally, to facilitate comparative sequence analysis, we implemented several enhancements to the program, including the addition of links from annotated features within a generated percent-identity plot to specific records in public databases. Taken together, the results reported here provide an important comparative sequence resource that should catalyze additional studies of Williams syndrome, including those that aim to characterize genes within the commonly deleted interval and to develop mouse models of the disorder.
Wearable Noncontact Armband for Mobile ECG Monitoring System

One of the best ways to obtain health information is from an electrocardiogram (ECG). Through an ECG, characteristics such as patients' heartbeats, heart conditions, and heart disease can be analyzed. Unfortunately, most available healthcare devices do not provide clinical data such as information regarding patients' heart activities. Many researchers have tried to solve this problem by inventing wearable heart monitoring systems with a chest strap or wristband, but their performances were not feasible for practical applications. Thus, the aim of this study is to build a new system to monitor heart activity through ECG signals. The proposed system consists of capacitive-coupled electrodes embedded in an armband. It is considered to be a reliable, robust, and low-power-transmission ECG monitoring system. The reliability of this system was achieved by the careful placement of sensors in the armband. Bluetooth low energy (BLE) was used as the protocol for data transmission; this protocol was proposed to develop the low-power-transmission system. For robustness, the proposed system is equipped with analysis capabilities-e.g., real-time heartbeat detection and a filter algorithm to ignore distractions from body movements or noise from the environment.
Utility-optimal random access: Reduced complexity, fast convergence, and robust performance

In this paper, we propose two distributed contention-based medium access control (MAC) algorithms for solving a network utility maximization (NUM) problem in wireless ad hoc networks. Most of the previous NUM-based random access algorithms have one or more of the following performance bottlenecks: (1) extensive signaling among the nodes to achieve semi-distributed implementations, (2) synchronous updates of contention probabilities, (3) small update stepsizes to ensure convergence but with typically slow speed, and (4) supporting a limited range of utility functions under which the NUM is shown to be convex. Our proposed algorithms overcome the bottlenecks in all four aspects. First, only limited amount of message passing among nodes is required. Second, fully asynchronous updates of contention probabilities are allowed. Furthermore, our algorithms are robust to arbitrary large message passing delay and message loss. Third, we do not utilize any stepsize during updates, thus our algorithms can achieve faster convergence. Finally, our proposed algorithms have provable convergence, optimality, and robustness properties under a wider range of utility functions, even if the NUM problem is non-convex. Simulation results show the optimality and fast convergence of our algorithms, performance improvements compared with the subgradient-based MAC, and better efficiency-fairness tradeoff compared with the IEEE 802.11 distributed coordination function.
Neuroethics: A New Way of Doing Ethics

The aim of this article is to argue, by example, for neuroethics as a new way of doing ethics. Rather than simply giving us a new subject matter—the ethical issues arising from neuroscience—to attend to, neuroethics offers us the opportunity to refine the tools we use. Ethicists often need to appeal to the intuitions provoked by consideration of cases to evaluate the permissibility of types of actions; data from the sciences of the mind give us reason to believe that some of these intuitions are less reliable than others. I focus on the doctrine of double effect to illustrate my case, arguing that experimental results suggest that appeal to it might be question-begging. The doctrine of double effect is supposed to show that there is a moral difference between effects that are brought about intentionally and those that are merely foreseen; I argue that the data suggest that we regard some effects as merely foreseen only because we regard bringing them about as permissible. Appeal to the doctrine of double effect therefore cannot establish that there are such moral differences.