Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy of mean arterial pressure and blood pressure measurements in predicting pre-eclampsia: systematic review and meta-analysis

Objective To determine the accuracy of using systolic and diastolic blood pressure, mean arterial pressure, and increase of blood pressure to predict pre-eclampsia. Design Systematic review with meta-analysis of data on test accuracy. Data sources Medline, Embase, Cochrane Library, Medion, checking reference lists of included articles and reviews, contact with authors. Review methods Without language restrictions, two reviewers independently selected the articles in which the accuracy of blood pressure measurement during pregnancy was evaluated to predict pre-eclampsia. Data were extracted on study characteristics, quality, and results to construct 2×2 tables. Summary receiver operating characteristic curves and likelihood ratios were generated for the various levels and their thresholds. Results 34 studies, testing 60 599 women (3341 cases of pre-eclampsia), were included. In women at low risk for pre-eclampsia, the areas under the summary receiver operating characteristic curves for blood pressure measurement in the second trimester were 0.68 (95% confidence interval 0.64 to 0.72) for systolic blood pressure, 0.66 (0.59 to 0.72) for diastolic blood pressure, and 0.76 (0.70 to 0.82) for mean arterial pressure. Findings for the first trimester showed a similar pattern. Second trimester mean arterial pressure of 90 mm Hg or more showed a positive likelihood ratio of 3.5 (95% confidence interval 2.0 to 5.0) and a negative likelihood ratio of 0.46 (0.16 to 0.75). In women deemed to be at high risk, a diastolic blood pressure of 75 mm Hg or more at 13 to 20 weeks’ gestation best predicted pre-eclampsia: positive likelihood ratio 2.8 (1.8 to 3.6), negative likelihood ratio 0.39 (0.18 to 0.71). Additional subgroup analyses did not show improved predictive accuracy. Conclusion When blood pressure is measured in the first or second trimester of pregnancy, the mean arterial pressure is a better predictor for pre-eclampsia than systolic blood pressure, diastolic blood pressure, or an increase of blood pressure.
Immunomodulatory effects of human amniotic membrane-derived mesenchymal stem cells

Human amniotic membrane-derived mesenchymal stem cells (hAM-MSCs) are capable of differentiating into several lineages and possess immunomodulatory properties. In this study, we investigated the soluble factor-mediated immunomodulatory effects of hAM-MSCs. Mitogen-induced peripheral blood mononuclear cell (PBMC) proliferation was suppressed by hAM-MSCs in a dose-dependent manner as well as hAM-MSC culture supernatant. Moreover, interferon-gamma and interleukin (IL)-17 production significantly decreased from PBMC, whereas IL-10 from PBMCs and transforming growth factor beta (TGF-β) production from hAM-MSCs significantly increased in co-cultures of hAM-MSCs and PBMCs. Production of several MSC factors, including hepatocyte growth factor (HGF), TGF-β, prostaglandin E2 (PGE2), and indoleamine 2, 3 dioxygenase (IDO), increased significantly in hAM-MSCs co-cultured with PBMCs. These results indicate that the immunomodulatory effects of hAM-MSCs may be associated with soluble factors (TGF-β, HGF, PGE2, and IDO), suggesting that hAM-MSCs may have potential clinical use in regenerative medicine.
Gene expression profiles of human breast cancer progression

Although distinct pathological stages of breast cancer have been described, the molecular differences among these stages are largely unknown. Here, through the combined use of laser capture microdissection and DNA microarrays, we have generated in situ gene expression profiles of the premalignant, preinvasive, and invasive stages of human breast cancer. Our data reveal extensive similarities at the transcriptome level among the distinct stages of progression and suggest that gene expression alterations conferring the potential for invasive growth are already present in the preinvasive stages. In contrast to tumor stage, different tumor grades are associated with distinct gene expression signatures. Furthermore, a subset of genes associated with high tumor grade is quantitatively correlated with the transition from preinvasive to invasive growth.
Revisiting the Markov Chain Model of IEEE 802.11E EDCA and Introducing the Virtual Collision Phenomenon

: IEEE 802.11e speciﬁes the Enhanced Distributed Channel Access (EDCA) for distributed access with trafﬁc differentiation to a Wireless Local Area Network (WLAN). Trafﬁc differentiation led to introducing several trafﬁc queues within each station, thus introducing virtual collision. In this paper, we present a pattern based Markov chain model of EDCA including the virtual collision phenomenon not found in previous work. We also correct the misconceptions found in existing Markov chain models of EDCA (Kong et al., 2004). We thus obtain a more realistic model of IEEE 802.11e EDCA which will allow a more precise performance analysis that may be used for admission control.
Effects of Acute Exposure to Moderate Altitude on Vascular Function, Metabolism and Systemic Inflammation

Background Travel to mountain areas is popular. However, the effects of acute exposure to moderate altitude on the cardiovascular system and metabolism are largely unknown. Objectives To investigate the effects of acute exposure to moderate altitude on vascular function, metabolism and systemic inflammation. Methods In 51 healthy male subjects with a mean (SD) age of 26.9 (9.3) years, oxygen saturation, blood pressure, heart rate, arterial stiffness, lipid profiles, low density lipoprotein (LDL) particle size, insulin resistance (HOMA-index), highly-sensitive C-reactive protein and pro-inflammatory cytokines were measured at 490 m (Zurich) and during two days at 2590 m, (Davos Jakobshorn, Switzerland) in randomized order. The largest differences in outcomes between the two altitudes are reported. Results Mean (SD) oxygen saturation was significantly lower at 2590 m, 91.0 (2.0)%, compared to 490 m, 96.0 (1.0)%, p<0.001. Mean blood pressure (mean difference +4.8 mmHg, p<0.001) and heart rate (mean difference +3.3 bpm, p<0.001) were significantly higher at 2590 m, compared to 490 m, but this was not associated with increased arterial stiffness. At 2590 m, lipid profiles improved (median difference triglycerides −0.14 mmol/l, p = 0.012, HDL +0.08 mmol/l, p<0.001, total cholesterol/HDL-ratio −0.25, p = 0.001), LDL particle size increased (median difference +0.45 nm, p = 0.048) and hsCRP decreased (median difference −0.18 mg/l, p = 0.024) compared to 490 m. No significant change in pro-inflammatory cytokines or insulin resistance was observed upon ascent to 2590 m. Conclusions Short-term stay at moderate altitude is associated with increased blood pressure and heart rate likely due to augmented sympathetic activity. Exposure to moderate altitude improves the lipid profile and systemic inflammation, but seems to have no significant effect on glucose metabolism. Trial Registration ClinicalTrials.gov NCT01130948
Data Processing in Macromolecular Crystallography

Data processing in macromolecular crystallography is the effort by which a user converts a set of raw diffraction data into a list of Bragg reflections with measured intensities. With modern crystallographic hardware the raw data consists of a set of twodimensional detector images, each collected at a particular orientation of the crystal. Data processing may be broken down into four steps: calibration, determination of the unit cell, measurement of the integrated intensities, and merging and scaling the integrated data. Algorithms for each of these steps have been derived and validated, and are implemented in several commercially available software packages. In recent years these packages have become more flexible and easier to use. Software developed for monochromatic laboratory experiments must be modified or even replaced when the user is performing more exotic experiments, particularly Laue diffraction experiments.
On Optimising Local Feature Face Recognition for Mobile Devices !

Face recognition is currently a very active research topic due to the great variety of applications it can offer. Moreover, nowadays it is very common for people to have mobile devices such as smart phones or laptops which have an integrated digital camera. This gives the opportunity to develop face recognition applications for this type of devices. This paper treats the problem of face verification in mobile devices. The problem is discussed and analysed, observing which are the difficulties that are encountered. Some algorithms for face recognition are analysed and optimised so that they are better suited for the resource constrains of mobile devices.
An Adaptive Denoising and Enhancing Algorithm Based on the MAP Rule in the Contourlet Domain for Infrared Image

In order to solve the problem of weakening the details and the edges of image while denoising in the contourlet domain, this paper presents an adaptive denoising algorithm with detail enhancement and applies it to the denoising procedure of infrared image. On the basis of the assumption that the prior distribution of the original image coefficients and the noise’s are both Gaussian in the contourlet domain, this method firstly makes use of the rule of Maximum a Posteriori to compute the shrinkable factor for the contourlet coefficients, then modifies it by taking decomposable scale and directional energy into account. Finally, a denoised and enhanced image can be obtained after the contourlet coefficients, which are shrunk by the modified shrinkable factor, are made by the reverse contourlet transform. The contourlet transform has not the character of translationinvariance, so the cycle spinning method is applied to the whole denoising procedure to overcome the drawback. The experimental results show that the method given by this paper, compared with the general denoising algorithm of wavelet and contourlet, can enhance image details, stretch image contrast and produce a good visual effect though it has a little loss of PSNR(Peak Signal Noise Ratio). The enhancing idea of coefficients in the contourlet domain proposed by this paper can apply to other algorithms based on proportional shrinkage. Keywords-denoising in the contourlet domain; image enhancement; proportional shrinkage; Gaussian distribution; Maximum a Posteriori; cycle spinning
An Approach to Web Query Recommendation System

In this work, the authors address the problem of finding desirable and accurate information on the web by search engines due to two problems of low precis ion and low recall caused by significant and rapid growth in amount of information and the number of users. Base d on the studies performed on query recommendation system using keyword extraction from the search results fo r a query, an algorithm is proposed to suggest alte rnate search topics which are related to the input query provided. Based on this cluster labels were identified and once these labels are distinguished, search results are assigned to t hem using frequency of occurrence of any particular label in the corresponding document. Keywords—web mining, query recommendation system.
Preservation of strong normalisation in named lambda calculi with explicit substitution and garbage collection

In this paper we introduce and study a new-calculus with explicit substitution, xgc, which has two distinguishing features: rst, it retains the use of traditional variable names, specifying terms modulo renaming; this simpliies the reduction system. Second, it includes reduction rules for explicit garbage collection; this simpliies several proofs. We show that xgc is a conservative extension which preserves strong normalisation (PSN) of the untyped-calculus. The result is obtained in a modular way by rst proving it for garbage-free reduction and then extending tòreductions in garbage'. This provides insight into the counterexample to PSN for of Melli es (1995); we exploit the abstract nature of xgc to show how PSN is in connict with any reasonable substitution composition rule (except for trivial composition rules of which we mention one).
A Case-based Reasoning System with Two-dimensional Reduction Techniques for Classification of Leisure Constraints

Chiu et al. recently published an article entitled "Developing a case-based reasoning system of leisure constraints" in volume 10 of the Information Technology Journal. In this article, they applied the case-based reasoning technology to predicting the type of leisure constraints that visitors encounter to assist leisure services providers in negotiation of the constraints. Unfortunately, the inference relies on all instances in the database and features of these instances (or cases). Inaccurate results may be obtained if the instances or features are not representative. A plan of simultaneously selecting representative instances and features through GA optimized training with random combinations of instances or features is proposed and expected to improve the performance of the existing system.
An expectation-maximization algorithm working on data summary

Scalable cluster analysis addresses the problem of processing large data sets with limited resources, e.g., memory and computation time. A data summarization or sampling procedure is an essential step of most scalable algorithms. It forms a compact representation of the data. Based on it, traditional clustering algorithms can process large data sets efficiently. However, there is little work on how to effectively perform cluster analysis on data summaries. From the principle of the general expectation-maximization algorithm, we propose a model-based clustering algorithm to make better use of these data summaries in this paper. The proposed EMACF (Expectation-Maximization Algorithm on Clustering Features) algorithm employs data summary features including weight, mean, and variance explicitly. We prove that EMACF converges to a local maximum likelihood value. The computation time of EMACF is linear with the number of data summaries instead of the number of data items, and thus can be integrated with any efficient data summarization procedure to construct a scalable clustering algorithm.
Design of digital elevation model based on orthorectified satellite stereo images

In this paper, orthorectification of satellite data from Pleiades satellite sensors based on ground control points (GCPs) is performed. A true orthorectification of satellite data is possible with an accurate digital elevation model (DEM). This study was challenging due to weather condition, hilly, and nonuniform terrain at the city Kota Kinabalu in East Malaysia. First, the geometric accuracy of the sensors is required for 3D mapping of the area of interest. In addition, an accurate DEM for better height calculation of vegetation/trees near power poles is required. For these purposes, first we need the true orthorectification of satellite stereo imagery. The measured GCP is compared with the actual GCPs to evaluate the geometric correction in terms of root mean square error. The comparison was made based on rational polynomial Function (RPC) coefficients and GCP measurements. Results show that the Pleiades satellite sensor is feasible for generating the elevation model for small area. This shows that Pleiades sensor can be used for monitoring the vegetation near poles of high transmission power lines based on the satellite stereo images.
Novel Indirect Calorimetry Technology to Analyze Metabolism in Individual Neonatal Rodent Pups

Background The ability to characterize the development of metabolic function in neonatal rodents has been limited due to technological constraints. Low respiratory volumes and flows at rest pose unique problems, making it difficult to reliably measure O2 consumption, CO2 production, respiratory quotient (RQ), and energy expenditure (EE). Our aim was to develop and validate a commercial-grade indirect calorimetry system capable of characterizing the metabolic phenotype of individual neonatal rodents. Methodology/Principal Findings To address this research need, we developed a novel, highly sensitive open-circuit indirect calorimetry system capable of analyzing respiratory gas exchange in a single neonatal rodent pup. Additionally, we derived an equation from known metabolic relationships to estimate inlet flow rates, improving the efficiency of data collection. To validate the neonatal rodent indirect calorimetry system and evaluate the applicability of the derived equation for predicting appropriate flow rates, we conducted a series of experiments evaluating the impact of sex, litter size, time of day (during the light phase), and ambient temperature on neonatal rat metabolic parameters. Data revealed that the only metabolic parameter influenced by litter size is a neonatal rat's RQ, with rat pups reared in a small litter (5 pups) having lower RQ's than rat pups reared in either medium (8 pups) or large (11 pups) litters. Furthermore, data showed that ambient temperature affected all metabolic parameters measured, with colder temperatures being associated with higher CO2 production, higher O2 consumption, and higher energy expenditure. Conclusion/Significance The results of this study demonstrate that the modified Panlab Oxylet system reliably assesses early postnatal metabolism in individual neonatal rodents. This system will be of paramount importance to further our understanding of processes associated with the developmental origins of adult metabolic disease.
Speaker identification in emotional talking environments using both gender and emotion cues

Speaker recognition performance is usually very high in neutral talking environments; however, the performance is significantly degraded in emotional talking environments. This work is devoted to proposing, implementing, and evaluating a new approach to enhance the degraded performance of text-independent speaker identification in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues using Hidden Markov Models (HMMs) as classifiers. This approach has been tested on our collected speech database. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues. The results obtained based on the new proposed approach are close to those obtained in subjective evaluation by human judges.
Optimal H 1 Approximation by Systems of PrescribedOrder using Frequency Response

A method is given for the H 1 approximation of discrete-time systems of arbitrary order, whose frequency responses have derivatives satisfying an a priori bound and are speciied only by corrupted measurements at m frequencies. The approximation is by polynomial systems of prescribed order r ? 1. The method is based on convex optimization. Bounds on the approximation error and its departure from optimality are derived. In certain cases the approximation approaches optimality.
A Machine Description Facility for Compiler Testing

Requirements for a machine description facility for compiler testing are discussed. The compiler testing procedure consists of proving that programs are correctly translated by the compiler at hand. This is achieved by use of a common intermediate representation for both the source and object programs. The intermediate representation for the object program is built by use of a process termed symbolic interpretation. This process interprets a set of procedures which describe the effects of machine language instructions corresponding to the target machine on a suitable computation model in a manner consistent with an execution level definition of the high level language. Some of the important factors which enter into such a definition are discussed. These include architectural constraints posed by the target machine, and a description facility for memory and data types. Once such a definition is formulated, the actual instruction set of the target machine can be described. The highlights and limitations of such a definition facility are discussed in the context of a specific LISP implementation on a PDP-10 computer.
Ohmage

Participatory sensing (PS) is a distributed data collection and analysis approach where individuals, acting alone or in groups, use their personal mobile devices to systematically explore interesting aspects of their lives and communities [Burke et al. 2006]. These mobile devices can be used to capture diverse spatiotemporal data through both intermittent self-report and continuous recording from on-board sensors and applications. Ohmage (http://ohmage.org) is a modular and extensible open-source, mobile to Web PS platform that records, stores, analyzes, and visualizes data from both prompted self-report and continuous data streams. These data streams are authorable and can dynamically be deployed in diverse settings. Feedback from hundreds of behavioral and technology researchers, focus group participants, and end users has been integrated into ohmage through an iterative participatory design process. Ohmage has been used as an enabling platform in more than 20 independent projects in many disciplines. We summarize the PS requirements, challenges and key design objectives learned through our design process, and ohmage system architecture to achieve those objectives. The flexibility, modularity, and extensibility of ohmage in supporting diverse deployment settings are presented through three distinct case studies in education, health, and clinical research.
Learning to Parse Natural Language to a Robot Execution System

The ability to interpret, or ground , natural language commands so they can be executed by a robot is important for enabling untrained users to interact with robots. Logic-based formal representations have been applied very successfully to robots’ world and action models taking perceptual and grounding uncertainty into account; however, these approaches have historically either used formal target representations with severely limited expressiveness or written the formal representations manually rather than learning them. As a consequence, they are not able to parse previously unseen NL commands to more complex robot control systems. In this paper, we investigate how recently developed parser learning techniques can be applied to mapping natural language commands to a formal representation expressive enough for modeling robot control systems. Speciﬁcally, we aim to learn a parser mapping natural language indoor route instructions into a LISP-like control language. We test our approach using a simulator executing RCL programs on sets of natural language route instructions given by non-experts.