Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Metabolic Biomarkers in Drug Toxicity Studies

ABSTRACT Metabolic profiling is a technique that can potentially provide more sensitive and specific biomarkers of toxicity than the current clinical measures benefiting preclinical and clinical drug studies. Both nuclear magnetic resonance (NMR) and mass spectrometry (MS) platforms have been used for metabolic profiling studies of drug toxicity. Not only can both techniques provide novel biomarker(s) of toxicity but the combination of both techniques gives a broader range of metabolites evaluated. Changes in metabolic patterns can provide insight into mechanism(s) of toxicity and help to eliminate a potentially toxic new chemical entity earlier in the developmental process. Metabolic profiling offers numerous advantages in toxicological research and screening as sample collection and preparation are relatively simple. Further, sample throughput, reproducibility, and accuracy are high. The area of drug toxicity of therapeutic compounds has already been impacted by metabolic profiling studies and will continue to be impacted as new, more specific biomarker(s) are found. In order for a biomarker or pattern of biomarkers to be accepted, it must be shown that they originate from the target tissue of interest. Metabolic profiling studies are amenable to any biofluid or tissue sample making it possible to link the changes noted in urine for instance as originating from renal injury. Additionally, the ease of sample collection makes it possible to follow a single animal or subject over time in order to determine whether and when the toxicity resolves itself. This review focuses on the advantages of metabolic profiling for drug toxicity studies.
Quoted staged rewriting: a practical approach to library-defined optimizations

Staging has proved a successful technique for programmatically removing code abstractions, thereby allowing for faster program execution while retaining a high-level interface for the programmer. Unfortunately, techniques based on staging suffer from a number of problems — ranging from practicalities to fundamental limitations — which have prevented their widespread adoption. We introduce Quoted Staged Rewriting (QSR), an approach that uses type-safe, pattern matching-enabled quasiquotes to define optimizations. The approach is “staged” in two ways: first, rewrite rules can execute arbitrary code during pattern matching and code reconstruction, leveraging the power and flexibility of staging; second, library designers can orchestrate the application of successive rewriting phases (stages). The advantages of using quasiquote-based rewriting are that library designers never have to deal directly with the intermediate representation (IR), and that it allows for non-intrusive optimizations — in contrast with staging, it is not necessary to adapt the entire library and user programs to accommodate optimizations. We show how Squid, a Scala macro-based framework, enables QSR and renders library-defined optimizations more practical than ever before: library designers write domain-specific optimizers that users invoke transparently on delimited portions of their code base. As a motivating example we describe an implementation of stream fusion (a well-known deforestation technique) that is both simpler and more powerful than the state of the art, and can readily be used by Scala programmers with no knowledge of metaprogramming.
Part Context Learning for Visual Tracking

Context information is widely used in computer vision for tracking arbitrary objects. Most existing works focus on how to distinguish the tracked object from background or inter-frame object similarity information or key-points supporters as their auxiliary information to assist them in tracking. However, in most cases, how to discover and represent both the intrinsic property inside the object and surrounding information is still an open problem. In this paper, we propose a unified context learning framework that can capture stable structure relations of in-object parts, context parts and the object itself to enhance the tracker’s performance. The proposed Part Context Tracker (PCT) consists of an appearance model, an internal relation model and an context relation model. The appearance model represents the appearances of the object and parts. The internal relation model utilizes the parts inside the object to describe the spatio-temporal structure property directly, while the context relation model takes advantage of the latent intersection between the object and background parts. Then the appearance model, internal relation model and context relation model are embedded in a max-margin structured learning framework. Furthermore, a simple robust update strategy using median filter is utilized, which can deal with appearance change effectively and alleviate the drift problem. Extensive experiments are conducted on various benchmark dataset, and the comparisons with state-of-the-arts demonstrate the effectiveness of our work.
Diving behaviour of whale sharks in relation to a predictable food pulse

We present diving data for four whale sharks in relation to a predictable food pulse (reef fish spawn) and an analysis of the longest continuous fine-resolution diving record for a planktivorous shark. Fine-resolution pressure data from a recovered pop-up archival satellite tag deployed for 206 days on a whale shark were analysed using the fast Fourier Transform method for frequency domain analysis of time-series. The results demonstrated that a free-ranging whale shark displays ultradian, diel and circa-lunar rhythmicity of diving behaviour. Whale sharks dive to over 979.5 m and can tolerate a temperature range of 26.4 °C. The whale sharks made primarily diurnal deep dives and remained in relatively shallow waters at night. Whale shark diving patterns are influenced by a seasonally predictable food source, with shallower dives made during fish spawning periods.
Automated formal synthesis of Wallace Tree multipliers

In this paper, we present a formal synthesis methodology that is capable of performing correct synthesis at almost all levels of abstraction and can be adapted to be used for most of the combinational digital circuits irrespective of their size and complexity. The proposed methodology calls for proving the correctness-preserving characteristic for the transformations that are required in the synthesis of a particular digital circuit in a higher-order-logic theorem prover. These correctness- preserving transformation proofs can then be used to automatically verify the correctness of the corresponding synthesis process within the theorem prover in an automated way. For illustration purposes, we present the construction of an automated formal synthesis tool for Wallace Tree multipliers based on our methodology.
Prognostic value of clotting and fibrinolytic systems in a follow‐up of 165 liver cirrhotic patients

One hundred sixty‐five patients with cirrhosis diagnosed by needle liver biopsy were followed for 2 years to evaluate the relation between clotting factors and survival. Patients with spontaneous bacterial peritonitis, hepatic carcinoma, and cholestatic liver diseases were excluded. Patients were classified as A (n = 34), B (n = 75), or C (n = 56) according to Child‐Pugh criteria. During the follow‐up 45 patients died of liver failure or gastrointestinal hemorrhage. Nonsurvivor patients had significantly higher values of bilirubin and D‐dimer, a marker of fibrinolysis in vivo, lower values of albumin, prothrombin activity, fibrinogen, prekallikrein, factor VII, and a more prolonged activated partial thrombo‐plastin time than survivors. All these variables and Child‐Pugh classification were significantly associated with survival in a univariate analysis. Multivariate analysis (Cox's model) showed that only prekallikrein and factor VII were independently predictors of survival. Ninety‐three percent of patients with prekallikrein values <32% died within 32 months of follow‐up, whereas factor VII <34% identified 93% of patients who died within 10 months of follow‐up. This study suggests that factor VII is an early predictor of survival and may be a useful test to better identify cirrhotic patients who should be candidates for liver transplantation. (HEPATOLOGY 1995 22: 96–100.)
MODEL ORDER SELECTION FOR NON-NEGATIVE MATRIX FACTORIZATIONWITH APPLICATION TO SPEECH ENHANCEMENT

This report deals with the application of non-negative matrixfactorization (NMF) in speech processing. A Bayesian NMFis used to find the optimal number of basis vectors for thespeech signal. The result is validated by performing a speechenhancement task for a set of different number of basis vec-tors. The algorithm performance is measured with the Sourceto Distortion Ratio (SDR) that represents the overall qualityof speech. The results show that for medium input SNRs,60 basis vectors for each speaker are sufficient to model thespeech spectrogram. NMF produced better SDR results thana recently developed version of Spectral Subtraction algo-rithm. The window length was found to have a great effecton the results, but zero padding did not influence the results.
An algorithm for model determination in a layered network with non-uniform hidden layer unit set

On applying a layered neural network to pattern classification problems, simultaneous search for the optimal model and the optimal parameter set can be beneficial, in order to solve the model determination problem. When altering the model during training, it is desirable that the accumulated map will be inherited to the new model for the training process to be continued. In this work, a model selection criterion based on inter-map norms will be introduced, for mappreserving Model Switching applied to networks with non-uniform hidden layer unit sets. Further, a search algorithm for the suitable model applicable to problems requiring various types of discrimination borders, will be introduced.
Parental perceptions of child vulnerability in a community-based sample

The study aimed to assess the prevalence of parental perceptions of a child’s vulnerability (PPCV) in a Dutch community-based sample and its relationship with children’s health and health-related quality of life (HRQoL). Parents completed the Child Vulnerability Scale and a socio-demographic questionnaire. The Pediatric Quality of Life Inventory 4.0 was administered to measure HRQoL. The prevalence of PPCV was assessed in relation to socio-demographic and health-related characteristics. In a three-step multiple hierarchical regression model, the mediational role of PPCV in the association between chronic illness and HRQoL was investigated. Participants were 520 Dutch children aged 5–18 years from nine Dutch schools. In all, 69 (13.3%) had a chronic illness; 1.9% was perceived vulnerable, 3.0% in groups 5–7 and 1.7% in groups 8–12 and 13–18. Younger age of the child, presence of a chronic illness and low HRQoL were associated with PPCV. PPCV partially mediated the negative association between chronic illness and HRQoL. In conclusion, PPCV is associated with adjustment to chronic illness. More research is needed regarding the mechanisms through which PPCV affects HRQoL and to examine whether PPCV can be targeted in parenting interventions.
P2P traffic identification method based on an improvement incremental SVM learning algorithm

How to classify the data sets with vast information amount and large distribution fluctuation, which is always the research hotspot. This paper puts forward an improved SVM incremental learning algorithm by comparing the different incremental learning methods of SVM algorithm. In the algorithm, whether to violate the KTT conditions is regarded as an important basis for incremental data set. And the algorithm will be more efficient on the classification of SVM incremental sets through optimizing and improving itself. Then the paper compares the SVM-based re-training algorithm, the standard SVM incremental learning algorithm and the improved SVM incremental learning algorithm through identifying P2P network traffic. The experimental results show that the improved SVM incremental learning algorithm proposed in this article can save storage space and increase the accuracy of the identification of P2P traffic.
P‐glycoprotein (ABCB1) inhibited network of mitochondrion transport along microtubule and BMP signal‐induced cell shape in chimpanzee left cerebrum by systems‐theoretical analysis

We constructed the significant low‐expression P‐glycoprotein (ABCB1) inhibited transport and signal network in chimpanzee compared with high‐expression (fold change ≥2) the human left cerebrum in GEO data set, by using integration of gene regulatory activated and inhibited network inference method with gene ontology (GO) analysis. Our result showed that ABCB1 transport and signal upstream network RAB2A inhibited ABCB1, and downstream ABCB1‐inhibited SMAD1_2, NCK2, SLC25A46, GDF10, RASGRP1, EGFR, LRPPRC, RASSF2, RASA4, CA2, CBLB, UBR5, SLC25A16, ITGB3BP, DDIT4, PDPN, RAB2A in chimpanzee left cerebrum. We obtained that the different biological processes of ABCB1 inhibited transport and signal network repressed carbon dioxide transport, ER to Golgi vesicle‐mediated transport, folic acid transport, mitochondrion transport along microtubule, water transport, BMP signaling pathway, Ras protein signal transduction, transforming growth factor beta receptor signaling pathway in chimpanzee compared with the inhibited network of the human left cerebrum, as a result of inducing inhibition of mitochondrion transport along microtubule and BMP signal‐induced cell shape in chimpanzee left cerebrum. Our hypothesis was verified by the same and different biological processes of ABCB1 inhibited transport and signal network of chimpanzee compared with the corresponding activated network of chimpanzee and the human left cerebrum, respectively. Copyright © 2012 John Wiley & Sons, Ltd.
Personalized news recommendation based on implicit feedback

This paper presents a personalized news recommendation system that combines effective ways of understanding new articles with novel ways of modelling evolving user interest profiles to deliver relevant news articles to a user. A news article is represented as a taxonomy of hierarchical abstractions that capture different semantic facets of the news story. A users interest profile is modelled as an evolving interest over these facets. Users interest in individual articles is determined using a novel SWL (select-watch-leave) interest modelling framework that leverages on a detailed analysis of his usage history. Initial performance comparisons with state-of-the art personalized ranking approaches[2] are promising.
Sensor for Measuring Strain in Textile

In this paper a stain sensor to measure large strain (80%) in textiles is presented. It consists of a mixture of 50wt-% thermoplastic elastomer (TPE) and 50wt-% carbon black particles and is fiber-shaped with a diameter of 0.315mm. The attachment of the sensor to the textile is realized using a silicone film. This sensor configuration was characterized using a strain tester and measuring the resistance (extension-retraction cycles): It showed a linear resistance response to strain, a small hysteresis, no ageing effects and a small dependance on the strain velocity. The total mean error caused by all these effects was ±5.5% in strain. Washing several times in a conventional washing machine did not influence the sensor properties. The paper finishes by showing an example application where 21 strain sensors were integrated into a catsuit. With this garment, 27 upper body postures could be recognized with an accuracy of 97%.
New CNN based algorithms for the full penetration hole extraction in laser welding processes: Experimental results.

In this paper the results obtained by the use of new CNN based visual algorithms for the control of welding processes are described. The growing number of laser welding applications from automobile production to micro mechanics requires fast systems to create closed loop control for error prevention and correction. Nowadays the image processing frame rates of conventional architectures [1] are not sufficient to control high speed laser welding processes due to the fast fluctuation of the full penetration hole [3]. This paper focuses the attention on new strategies obtained by the use of the Eye-RIS system v1.2 which includes a pixel parallel Cellular Neural Network (CNN) based architecture called Q-Eye [2]. In particular, new algorithms for the full penetration hole detection with frame rates up to 24 kHz will be presented. Finally, the results obtained performing real time control of welding processes by the use of these algorithms will be discussed.
Association between clinical presentations before myocardial infarction and coronary mortality: a prospective population-based study using linked electronic records

Background Ischaemia in different arterial territories before acute myocardial infarction (AMI) may influence post-AMI outcomes. No studies have evaluated prospectively collected information on ischaemia and its effect on short- and long-term coronary mortality. The objective of this study was to compare patients with and without prospectively measured ischaemic presentations before AMI in terms of infarct characteristics and coronary mortality. Methods and results As part of the CALIBER programme, we linked data from primary care, hospital admissions, the national acute coronary syndrome registry and cause-specific mortality to identify patients with first AMI (n = 16,439). We analysed time from AMI to coronary mortality (n = 5283 deaths) using Cox regression (median 2.6 years follow-up), comparing patients with and without recent ischaemic presentations. Patients with ischaemic presentations in the 90 days before AMI experienced lower coronary mortality in the first 7 days after AMI compared with those with no prior ischaemic presentations, after adjusting for age, sex, smoking, diabetes, blood pressure and cardiovascular medications [HR: 0.64 (95% CI: 0.57–0.73) P < 0.001], but subsequent mortality was higher [HR: 1.42 (1.13–1.77) P = 0.001]. Patients with ischaemic presentations closer in time to AMI had the lowest seven day mortality (P-trend = 0.001). Conclusion In the first large prospective study of ischaemic presentations prior to AMI, we have shown that those occurring closest to AMI are associated with lower short-term coronary mortality following AMI, which could represent a natural ischaemic preconditioning effect, observed in a clinical setting. Clinical trials registration Clinicaltrials.gov identifier NCT01604486.
Playing an Action Video Game Reduces Gender Differences in Spatial Cognition

We demonstrate a previously unknown gender difference in the distribution of spatial attention, a basic capacity that supports higher-level spatial cognition. More remarkably, we found that playing an action video game can virtually eliminate this gender difference in spatial attention and simultaneously decrease the gender disparity in mental rotation ability, a higher-level process in spatial cognition. After only 10 hr of training with an action video game, subjects realized substantial gains in both spatial attention and mental rotation, with women benefiting more than men. Control subjects who played a non-action game showed no improvement. Given that superior spatial skills are important in the mathematical and engineering sciences, these findings have practical implications for attracting men and women to these fields.
Relevance feedback using a Bayesian classifier in content-based image retrieval

As an effective solution of the content-based image retrieval problems, relevance feedback has been put on many efforts for the past few years. In this paper, we propose a new relevance feedback approach with progressive leaning capability. It is based on a Bayesian classifier and treats positive and negative feedback examples with different strategies. It can utilitize previous users' feedback information to help the current query. Experimental results show that our algorithm achieves high accuracy and effectiveness on real-world image collections.
Iris tracking and blink detection for human-computer interaction using a low resolution webcam

This paper proposes a technique to develop a non-intrusive interface for human-computer interaction based on visual cues of a human eye, captured using a low resolution USB camera. Such an interface can replace a traditional interface like a mouse thus helping the severely disabled to use the computer just as a normal individual would. It makes use of iris tracking and blink detection for this purpose. Considering fairly constant lighting conditions, a modified Hough Transform is used to track the iris at real time. The Between-The-Eyes feature serves as a stable reference point for gaze direction estimation. When mapped to the computer screen, incremental, directional movement of the mouse pointer position provides precise control over its movement. A method based on the projection function is proposed to detect the user's eye blinks, analyze their patterns and duration, interpreting them as appropriate mouse clicks. The system gives an overall accuracy of 87.4 %.
Feature Selection via Concave Minimization and Support Vector Machines

Computational comparison is made between two feature selection approaches for (cid:12)nding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as few of the n features (dimensions) as possible. In the concave minimization approach [19, 5] a separating plane is generated by minimizing a weighted sum of distances of misclassi(cid:12)ed points to two parallel planes that bound the sets and which determine the separating plane midway between them. Furthermore, the number of dimensions of the space used to determine the plane is minimized. In the support vector machine approach [27, 7, 1, 10, 24, 28], in addition to minimizing the weighted sum of distances of misclassi(cid:12)ed points to the bounding planes, we also maximize the distance between the two bounding planes that generate the separating plane. Computational results show that feature suppression is an indirect consequence of the support vector machine approach when an appropriate norm is used. Numerical tests on 6 public data sets show that classi(cid:12)ers trained by the concave minimization approach and those trained by a support vector machine have comparable 10-fold cross-validation correctness. However, in all data sets tested, the classi(cid:12)ers obtained by the concave minimization approach selected fewer problem features than those trained by a support vector machine.