Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-dose synchrotron nano-CT via compressed sensing

Synchrotron phase nano-CT is a very useful technique for studying bone diseases, which requires investigating bone at the cellular level. Nevertheless, imaging biological tissue at this resolution is challenging due to the very high radiation dose. Compressed sensing provides a framework that permits to reconstruct an image from a limited amount of data. The most promising way to reduce radiation exposure in X-ray CT is to reduce the number of projections. The aim of this study is to assess the use of compressed sensing to reduce dose for synchrotron phase nano-CT for bone applications. In this paper, we address the tomographic reconstruction step by posing a total variation problem and solving it with the Split Bregman formulation. To assess the proposed method we created different low-dose imaging scenarios by reducing the number of projections, and tested them on several bone samples. The proposed method allowed accurate reconstruction using 1/4th of the projections, preserving bone features, details, and a high signal to noise ratio.
Thrombospondin-1 promotes matrix homeostasis by interacting with collagen and lysyl oxidase precursors and collagen cross-linking sites

Thrombospondin-1 promotes collagen matrix homeostasis through both intracellular and extracellular mechanisms. Thrombospondin-1 controls collagen homeostasis directly Collagens, components of the extracellular matrix important for tissue architecture and function, undergo a complex series of processing events before and after secretion. Deposition of excessive amounts of collagens and abnormal cross-linking of collagen fibrils are associated with fibrosis. Using knockout mice, primary human dermal fibroblasts, and in vitro binding assays, Rosini et al. found that thrombospondin-1 (TSP1) bound to and inhibited the processing of the precursor form of the collagen cross-linking enzyme lysyl oxidase. TSP1 also bound to collagen molecules intracellularly and at conserved cross-linking sites. Blocking the latter interaction extracellularly stimulated fibroblasts to undergo inappropriate conversion to myofibroblasts. Although the mechanism by which the TSP1-collagen interaction prevents fibroblast-to-myofibroblast conversion is unclear, these findings identify a direct role for TSP1 as a modulator of the collagen matrix. Fibrillar collagens of the extracellular matrix are critical for tissue structure and physiology; however, excessive or abnormal deposition of collagens is a defining feature of fibrosis. Regulatory mechanisms that act on collagen fibril assembly potentially offer new targets for antifibrotic treatments. Tissue weakening, altered collagen fibril morphologies, or both, are shared phenotypes of mice lacking matricellular thrombospondins. Thrombospondin-1 (TSP1) plays an indirect role in collagen homeostasis through interactions with matrix metalloproteinases and transforming growth factor–β1 (TGF-β1). We found that TSP1 also affects collagen fibril formation directly. Compared to skin from wild-type mice, skin from Thbs1−/− mice had reduced collagen cross-linking and reduced prolysyl oxidase (proLOX) abundance with increased conversion to catalytically active LOX. In vitro, TSP1 bound to both the C-propeptide domain of collagen I and the highly conserved KGHR sequences of the collagen triple-helical domain that participate in cross-linking. TSP1 also bound to proLOX and inhibited proLOX processing by bone morphogenetic protein-1. In human dermal fibroblasts (HDFs), TSP1 and collagen I colocalized in intracellular vesicles and on extracellular collagen fibrils, whereas TSP1 and proLOX colocalized only in intracellular vesicles. Inhibition of LOX-mediated collagen cross-linking did not prevent the extracellular association between collagen and TSP1; however, treatment of HDFs with KGHR-containing, TSP1-binding, triple-helical peptides disrupted the collagen-TSP1 association, perturbed the collagen extracellular matrix, and increased myofibroblastic differentiation in a manner that depended on TGF-β receptor 1. Thus, the extracellular KGHR-dependent interaction of TSP1 with fibrillar collagens contributes to fibroblast homeostasis.
Solution to Delayed FBSDEs and Application to the Stochastic LQ Problem With Input Delay

This brief is concerned with a kind of forward and backward stochastic differential equations (FBSDEs) with time delay. The explicit solution of the delayed FBSDEs is given by using the solution to the modified Riccati equation. Two schemes are then proposed for the modified Riccati equation, which can be solved numerically. The authors also apply the derived solution of the delayed FBSDEs to the stochastic linear quadratic control problem with input delay.
Nonylphenol and Octylphenol Differently Affect Cell Redox Balance by Modulating the Nitric Oxide Signaling

Nonylphenol (NP) and octylphenol (OP) are pervasive environmental contaminants belonging to the broader class of compounds known as alkylphenols, with potential human toxic effects. Classified as “xenoestrogens,” NP and OP are able to interfere with the cell endocrine physiology via a direct interaction with the estrogen receptors. Here, using HepG2 cells in culture, the changes of the cell redox balance and mitochondrial activity induced by OP and NP have been investigated at μM concentrations, largely below those provoking acute toxicity, as those typical of environmental contaminants. Following 24 h cell exposure to both OP and NP, ROS production appeared significantly increased (p ≤ 0.01), together with the production of higher NO oxides (p = 0.003) and peroxynitrated protein-derivatives (NP versus CTR, p = 0.003). The mitochondrial proton electrochemical potential gradient instead was decreased (p ≤ 0.05), as the oxygen consumption by complex IV, particularly following incubation with NP (NP versus CTR, p = 0.017). Consistently, the RT-PCR and Western blot analyses proved that the OP and NP can modulate to a different extent the expression of the inducible NOS (NP versus CTR, p ≤ 0.01) and the endothelial NOS (OP versus CTR, p ≤ 0.05), with a significant variation of the coupling efficiency of the latter (NP versus CTR, p ≤ 0.05), a finding that may provide a novel clue to understand the specific xenoestrogenic properties of OP and NP.
Object Detection with Multi-RCNN Detectors

Object detection and tracking are important in real time surveillance systems, such as traffic control, counting and public security field. Deep neural networks have been applied to solve many hard problems, computer vision problems for particular. Convolutional neural networks have been widely used in object detection and tracking, such as RCNN, Fast RCNN and Faster RCNN. However, accuracy and speed are two main challenges in object detection and tracking. Before object detection, we use Gaussian mixture model to subtract background, because background subtraction has a great impact on object detection. Then a multi-detector model based on Fast RCNN is proposed to detect objects in images captured. The experiment shows that the model we propose can improve the performance of detection, speed in particular.
Reducing Data Complexity in Feature Extraction and Feature Selection for Big Data Security Analytics

Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect cybersecurity threats and attacks by utilizing data mining techniques in the field of Artificial Intelligence. When it comes to the analysis of heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult to be managed efficiently. In this paper, we present an approach for handling feature extraction and feature selection utilizing machine learning algorithms for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark.
Structure Learning from Time Series with False Discovery Control

We consider the Granger causal structure learning problem from time series data. Granger causal algorithms predict a 'Granger causal effect' between two variables by testing if prediction error of one decreases significantly in the absence of the other variable among the predictor covariates. Almost all existing Granger causal algorithms condition on a large number of variables (all but two variables) to test for effects between a pair of variables. We propose a new structure learning algorithm called MMPC-p inspired by the well known MMHC algorithm for non-time series data. We show that under some assumptions, the algorithm provides false discovery rate control. The algorithm is sound and complete when given access to perfect directed information testing oracles. We also outline a novel tester for the linear Gaussian case. We show through our extensive experiments that the MMPC-p algorithm scales to larger problems and has improved statistical power compared to existing state of the art for large sparse graphs. We also apply our algorithm on a global development dataset and validate our findings with subject matter experts.
Design Guidelines of Low-Density Parity-Check Codes for Magnetic Recording Systems

As one of the most classical data-storage systems, magnetic recording (MR) systems have attracted a significant amount of research attention in the past several decades due to the advantages of low cost and high storage density. Along with the continual increase of areal density of modern MR devices, more severe inter-symbol interference (ISI) and noise appear and thus reliable storage becomes more difficult. To address this challenging problem, turbo detections and error-correction codes (ECCs) have been applied to MR systems so as to significantly improve the overall data-storage reliability. Among all the existing ECCs, low-density parity-check (LDPC) codes are of particular interest because they can offer excellent error performance with relatively low encoding and decoding complexity. This paper presents a comprehensive survey of the latest research advancements in LDPC-code design for MR systems from the perspectives of code construction, decoder design, as well as asymptotic performance-evaluation methodology. More specifically, we summarize the design guidelines of LDPC encoder and decoder over both one-dimensional (OD) ISI and two-dimensional (TD) ISI channels, which are commonly used to characterize MR systems with different areal densities. We also concisely portray the research progress in the design of some LDPC-code variants, such as protograph codes, repeat-accumulate codes, spatially coupled codes, and their non-binary counterparts over the aforementioned ISI channels. In particular, we restrict our attention to the reading process and ignore the written-in errors in MR systems unless otherwise stated. Hopefully, this survey will inspire more research activities in the area of LDPC-coded MR systems.
Cost Optimization of Cloud-RAN Planning and Provisioning for 5G Networks

In this paper, we propose a network planning and provisioning framework that optimizes deployment cost in C-RAN based 5G networks. Our framework is based on a Mixed Integer Quadratically Constrained Programming (MIQCP) model which optimizes ``virtualized'' 5G service chain deployment cost while performing adequate provisioning to address user demand and performance requirements. We use two realistic scenarios to showcase that our framework can be applied to different types of deployments and discuss the computational cost and scalability of our solution.
First comprehensive tool for screening pain in Parkinson's disease: the King's Parkinson's Disease Pain Questionnaire

Pain is highly prevalent in Parkinson's disease (PD), impacting patients’ ability, mood and quality of life. Detecting the presence of pain in its multiple modalities is necessary for adequate personalized management of PD. A 14‐item, PD‐specific, patient‐based questionnaire (the King's Parkinson's Disease Pain Questionnaire, KPPQ) was designed corresponding to the rater‐based KPP Scale (KPPS). The present multicentre study was aimed at testing the validity of this screening tool.
Zeno: Byzantine-suspicious stochastic gradient descent

We propose Zeno, a new robust aggregation rule, for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The key idea is to suspect the workers that are potentially malicious, and use a ranking-based preference mechanism. This allows us to generalize beyond past work--in our case, the number of malicious workers can be arbitrarily large, and we use only the weakest assumption on honest workers~(at least one honest worker). We prove the convergence of SGD under these scenarios. Empirical results show that Zeno outperforms existing approaches under various attacks.
Context-Based Event Trace Reduction in Client-Side JavaScript Applications

Record-replay techniques are developed to facilitate debugging client-side JavaScript application failures. They faithfully record all events that reveal a failure, but record many events irrelevant to the failure. Delta debugging adopts the divide-and-conquer algorithm to generate a minimal event subtrace that still reveals the same failure. However, delta debugging is slow because it may generate lots of syntactically infeasible candidate event subtraces in which some events can trigger syntactical errors (e.g., ReferenceError and TypeError), and thus cannot be replayed as expected. Based on this observation, we propose EvMin, an effective and efficient approach to remove failure-irrelevant events from an event trace. We use the variable usage information (e.g., DOM variable usage) in an event to model the event's context. We require that, each event in an event subtrace has the compatible context with its corresponding one in the original event trace. In this way, we avoid generating syntactically infeasible event subtraces, and dramatically speed up delta debugging. We have implemented EvMin and evaluated it on 10 real-world JavaScript application failures. Our evaluation shows that EvMin generates 72% fewer event subtraces, and takes 84% less time than delta debugging.
Classification of Both Seizure and Non-Seizure Based on EEG Signals Using Hidden Markov Model

In this paper, we propose a novel feature extraction method, a slope of counting wavelet coefficients over various thresholds (SCOT) method based hidden markov model (HMM) for seizure detection. The purpose of the proposed method is to aid in the diagnosis of epilepsy, which requires long-term electroencephalography (EEG) monitoring. The interpretation of long-term EEG monitoring takes a lot of time and requires the assistance of experienced experts. In order to overcome these limitations, it is important to apply the optimized feature extraction algorithm to the seizure detection system. The proposed SCOT method based HMM has a robust detection accuracy, and a short feature extraction time; whereas the existing methods require a large amount of training data and a long feature extraction time for learning the seizure detection model. Experimental result shows that with the proposed method, the average detection accuracies are 96.5% and 98.4% using the HMM in seizure and non-seizure, respectively. In addition, the proposed method has robust detection performance regardless of the given window sizes (0.15, 0.25, 0.5, 1, and 2 seconds) are used.
A review of validated biomarkers obtained through metabolomics

ABSTRACT Introduction: Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.
Machine Reading of Biomedical Data Dictionaries

This article describes an approach for the automated reading of biomedical data dictionaries. Automated reading is the process of extracting element details for each of the data elements from a data dictionary in a document format (such as PDF) to a completely structured representation. A structured representation is essential if the data dictionary metadata are to be used in applications such as data integration and also in evaluating the quality of the associated data. We present an approach and implemented solution for the problem, considering different formats of data dictionaries. We have a particular focus on the most challenging format with a machine-learning classification solution to the problem using conditional random field classifiers. We present an evaluation using several actual data dictionaries, demonstrating the effectiveness of our approach.
QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities

Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities.
Joker at SemEval-2018 Task 12: The Argument Reasoning Comprehension with Neural Attention

This paper describes a classification system that participated in the SemEval-2018 Task 12: The Argument Reasoning Comprehension Task. Briefly the task can be described as that a natural language “argument” is what we have, with reason, claim, and correct and incorrect warrants, and we need to choose the correct warrant. In order to make fully understand of the semantic information of the sentences, we proposed a neural network architecture with attention mechanism to achieve this goal. Besides we try to introduce keywords into the model to improve accuracy. Finally the proposed system achieved 5th place among 22 participating systems
CO emission and export from Asia: an analysis combining complementary satellite measurements (MOPITT, SCIAMACHY and ACE-FTS) with global modeling

This study presents the complementary picture of the pollution outflow provided by several satellite observations of carbon monoxide (CO), based on different observation techniques. This is illustrated by an analysis of the Asian outflow during the spring of 2005, through comparisons with simulations by the LMDz-INCA global chemistry transport model. The CO observations from the MOPITT and SCIAMACHY nadir sounders, which provide vertically integrated information with excellent horizontal sampling, and from the ACE-FTS solar occultation instrument, which has limited spatial coverage but allows the retrieval of vertical profiles, are used. Combining observations from MOPITT (mainly sensitive to the free troposphere) and SCIAMACHY (sensitive to the full column) allows a qualitative evaluation of the boundary layer CO. The model tends to underestimate this residual compared to the observations, suggesting underestimated emissions, especially in eastern Asia. However, a better understanding of the consistency and possible biases between the MOPITT and SCIAMACHY CO is necessary for a quantitative evaluation. Underestimated emissions, and possibly too low lofting and underestimated chemical production in the model, lead to an underestimate of the export to the free troposphere, as highlighted by comparisons with MOPITT and ACE-FTS. Both instruments observe large trans-Pacific transport extending from ~20° N to ~60° N, with high upper tropospheric CO observed by ACE-FTS above the eastern Pacific (with values of up to 300 ppbv around 50° N at 500 hPa and up to ~200 ppbv around 30° N at 300 hPa). The low vertical and horizontal resolutions of the global model do not allow the simulation of the strong enhancements in the observed plumes. However, the transport patterns are well captured, and are mainly attributed to export from eastern Asia, with increasing contributions from South Asia and Indonesia towards the tropics. Additional measurements of C 2 H 2 , C 2 H 6 and HCN by ACE-FTS provide further information on the plume history. C 2 H 2 and C 2 H 6 enhancements are well correlated with the CO plumes, indicating common sources and rapid trans-Pacific transport. HCN observations show that the biomass burning contributes mainly at latitudes lower than ~40° N. This study provides a first step towards a full combination of complementary observations, but also highlights the need for a better evaluation of consistency between the datasets in order to allow precise quantitative analyses.