Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperdense Basilar Artery Sign on Unenhanced CT Predicts Thrombus and Outcome in Acute Posterior Circulation Stroke

Background and Purpose— In acute stroke patients, the presence of a hyperdense middle cerebral artery sign on unenhanced CT is a specific but insensitive indicator of acute thrombosis. Our purpose was to determine whether the hyperdense basilar artery (HDBA) sign has utility in detecting thrombosis and predicting outcome in patients presenting with signs and symptoms of posterior circulation stroke. Methods— Unenhanced CT scans obtained within 24 hours of symptom onset in 95 patients with suspected posterior circulation stroke were reviewed. Three neuroimagers blinded to clinical outcome and results of the concurrent CT angiography (which served as the reference standard) rated presence of HDBA sign on a 5-point scale for level of certainty (1=definitely absent; 5=definitely present). Receiver operating characteristic curve analysis was performed. Short-term outcome was measured by discharge National Institute of Health Stroke Scale (NIHSS) scores; long-term outcome was measured by 6-month modified Rankin score (dichotomized, poor outcome defined as modified Rankin score >2). The following variables were correlated with short-term and long-term outcome by univariate analysis: HDBA sign, age, sex, time from stroke onset to imaging, admission NIHSS, history of stroke/TIA, atrial fibrillation, coronary artery disease, hypertension, diabetes, hypercholesterolemia, tobacco use, and thrombolysis. Variables showing correlation with P<0.1 were included in multiple regression analysis. Results— Using a level of certainty cutoff score of ≥4 (probable, definite), HDBA sign had 71% sensitivity, 98% specificity, 94% accuracy, 83% positive predictive value, and 95% negative predictive value for basilar artery occlusion. In univariate analysis, factors significantly correlated with discharge NIHSS were: admission NIHSS (P<0.0001; r=0.77), HDBA sign (P=0.01), and diabetes (P=0.02). Factors showing significant correlation or association with poor long-term outcome were age (P=0.02), admission NIHSS (P=0.007), HDBA sign (P=0.02), and history of stroke or TIA (P=0.007). The odds ratio of HDBA sign for predicting poor long-term outcome was 5.3 (95% CI, 1.1-33.3). In multiple regression analysis, the only independent predictors of discharge NIHSS were admission NIHSS (P<0.0001) and HDBA sign (P=0.004). Significant independent predictors of poor long-term outcome were age (P=0.02), admission NIHSS (P=0.008), history of stroke/TIA (P=0.03), and HDBA sign (P=0.05). Conclusion— In patients presenting with a high pretest probability of posterior circulation stroke based on clinical symptoms, the presence of the HDBA sign on unenhanced CT is a strong predictor of basilar artery thrombosis, and both short- and long-term outcome.
A Kind of Parallel Reference Frame Display Control Algorithm

Data superpose will be produced when using parallel reference method to display multidimensional information, it is difficult to discover the connotative relation and model. To a certain extend, the mining of relativity among different data dimensionality is restricted. This paper proposed a kind of control algorithm, using dimensional similarity to compare dimensionality approximation and object layout to reorganize the display sequences of dimensionality, making the relative attribute are displayed in a more reasonable dimensional sequence and improved easiness of information mining.
Solid pseudopapillary tumor of the pancreas

An 18-year-old previously healthy Malay woman was referred by her local doctor to a hospital in Singapore in 1998 for a deep painless lump located in the left hypochondriac region, roughly 5 x 5cm. Initially the patient refused treatment for non-medical reasons. During 2000, the patient became pregnant and gave birth to a healthy baby in early 2001. It was during 2002 when the lump continued to increase in size and cause occasional severe pain that the patient decided to seek treatment. The pain was intermittent over the left upper abdomen. Symptoms such as weight loss, lethargy, altered bowel habits and malaena were absent. There was also no history of abdominal trauma, alcohol abuse or pancreatitis.
Modular Certification of Low-Level Intermediate Representation Programs

Modular certification of low-level intermediate representation (IR) programs is one of the key steps of proof-transforming compilation. The major challenges are the complexity of abstract control stacks and the lack of control flow information due to their flat nature.To tackle these challenges, we present in this paper a novel Hoare-style logic framework for modular certification of p-machine style bytecode as IR programs. This logic can fully support abstract control stacks and unstructured control flows for modular certification of IR programs involving while loops, procedure call/return, recursive procedures, and even nested procedures. It applies Foundational Proof-Carrying Code (FPCC) concepts to IR level. This system is expressive and fully mechanized. We prove its soundness and demonstrate its power by certifying the implementation of some IR programs in the Coq proof assistant. This work not only provides a solid theoretical foundation for reasoning about IR programs, but also makes an important advance toward building proof-transforming compilation environment in which certified IR code with proofs can be compiled to machine checkable proof-carrying low-level assembly code.
Efficient state-space inference of periodic latent force models

Latent force models (LFM) are principled approaches to incorporating solutions to differential equations within non-parametric inference methods. Unfortunately, the development and application of LFMs can be inhibited by their computational cost, especially when closed-form solutions for the LFM are unavailable, as is the case in many real world problems where these latent forces exhibit periodic behaviour. Given this, we develop a new sparse representation of LFMs which considerably improves their computational efficiency, as well as broadening their applicability, in a principled way, to domains with periodic or near periodic latent forces. Our approach uses a linear basis model to approximate one generative model for each periodic force. We assume that the latent forces are generated from Gaussian process priors and develop a linear basis model which fully expresses these priors. We apply our approach to model the thermal dynamics of domestic buildings and show that it is effective at predicting day-ahead temperatures within the homes. We also apply our approach within queueing theory in which quasi-periodic arrival rates are modelled as latent forces. In both cases, we demonstrate that our approach can be implemented efficiently using state-space methods which encode the linear dynamic systems via LFMs. Further, we show that state estimates obtained using periodic latent force models can reduce the root mean squared error to 17% of that from non-periodic models and 27% of the nearest rival approach which is the resonator model (Sarkka et al., 2012; Hartikainen et al., 2012).
Recovering Dynamic Stroke Information of Multi-stroke Handwritten Characters with Complex Patterns

In this paper, we consider the problem of recovering dynamic stroke information from multi-stroke handwritten character images with complex patterns. The characters are assumed to be of a class of characters whose strokes are recursively formulated. By employing the so-called graph theoretic approach, we develop a systematic algorithm for recovering dynamic stroke information from character images in the class of our interest. It is shown that the correctness of algorithm is guaranteed mathematically. Moreover, we show that the time complexity becomes O(n), where n denotes the number of stroke-intersections on characters. Some recovery examples are included.
Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control

This paper studies the use of statistical induction techniques as a basis for automated performance diagnosis and performance management. The goal of the work is to develop and evaluate tools for offline and online analysis of system metrics gathered from instrumentation in Internet server platforms. We use a promising class of probabilistic models (Tree-Augmented Bayesian Networks or TANs) to identify combinations of system-level metrics and threshold values that correlate with high-level performance states--compliance with Service Level Objectives (SLOs) for average-case response time--in a three-tier Web service under a variety of conditions. Experimental results from a testbed show that TAN models involving small subsets of metrics capture patterns of performance behavior in a way that is accurate and yields insights into the causes of observed performance effects. TANs are extremely efficient to represent and evaluate, and they have interpretability properties that make them excellent candidates for automated diagnosis and control. We explore the use of TAN models for offline forensic diagnosis, and in a limited online setting for performance forecasting with stable workloads.
Scalable parallel co-clustering over multiple heterogeneous data types

The bi-clustering, i.e., simultaneously clustering two types of objects based on their correlations, has been studied actively in the last few years, in virtue of its impact on several relevant applications, such as text mining, collaborative filtering, gene expression analysis. In particular, many research efforts were recently spent on extending such a problem towards higher-order scenarios, where more than two data types are to be clustered synergically, according to pairwise inter-type relations. Measuring co-clustering quality as a weighted combination of the distortions over input relations, a number of alternate-optimization methods were developed of late, which scale linearly with the size of data. This result is likely to be inadequate for large scale applications where massive volumes of data are involved, and high performance solutions would be desirable. However, to date, parallel clustering approaches have been investigated deeply only for the case of just one or two inter-related data types. In this paper, we face the more general (high-order) co-clustering problem by proposing a parallel implementation of an effective and state-of-the-art method, by leveraging a parallel computation infrastructure implementing popular Map-Reduce paradigm.
OpenNF

Network functions virtualization (NFV) together with software-defined networking (SDN) has the potential to help operators satisfy tight service level agreements, accurately monitor and manipulate network traffic, and minimize operating expenses. However, in scenarios that require packet processing to be redistributed across a collection of network function (NF) instances, simultaneously achieving all three goals requires a framework that provides efficient, coordinated control of both internal NF state and network forwarding state. To this end, we design a control plane called OpenNF. We use carefully designed APIs and a clever combination of events and forwarding updates to address race conditions, bound overhead, and accommodate a variety of NFs. Our evaluation shows that OpenNF offers efficient state control without compromising flexibility, and requires modest additions to NFs.
Super Compact Low-Temperature Co-Fired Ceramic Bandpass Filters Using the Hybrid Resonator

A novel low-temperature co-fired ceramic (LTCC) resonator is proposed based on the hybrid resonant circuits. The resonator exhibits a very compact size and is also able to introduce a transmission zero in the filter design. A super compact bandpass filter with high performance was designed at the center frequency of 2.45 GHz and fabricated using the proposed LTCC hybrid resonator. This filter has a very compact size of 0.9 mm 0.688 mm 0.652 mm and exhibits a low insertion loss of less than 1.2 dB in the passband. Moreover, the transmission zero that is located at 4.5 GHz efficiently suppresses the spurious responses. In order to improve the stopband suppression even more, the revised resonator is used for the second filter design. Besides occupying an even smaller volume of 0.76 mm 0.748 mm0.67 mm, the second filter exhibits a wide stopband suppression higher than 35 dB from 3.4 to 8 GHz. In addition to the super compact size and wide stopband suppression, insertion losses of less than 2.5 dB were achieved in the passband. A transmission zero at 5.8 GHz was also achieved.
Iterative Tomographic Solution of Integer Least Squares Problems With Applications to MIMO Detection

Least squares (LS) fitting is one of the most fundamental techniques in science and engineering. It is used to estimate parameters from multiple noisy observations. In many problems the parameters are known a priori to be bounded integer valued, or they come from a finite set of values on an arbitrary finite lattice. Integer least squares is also an important problem in multichannel communication systems and GPS applications. In this case finding the closest vector becomes NP-hard problem. In this paper, we propose two novel algorithms, the Tomographic Least Squares Decoder (TLSD), that not only solves the ILS problem, better than other sub-optimal techniques, but can also provide the a posteriori probability distribution for each element in the solution vector and a belief propagation version termed two-dimensional belief propagation (2DBP). Both algorithms are based on reconstruction of the vector from multiple two-dimensional projections. The projections are carefully chosen to provide low computational complexity. We show that the projections are equivalent to the two-dimensional marginals of the soft zero forcing linear decoder. We also provide simulated experiments comparing the algorithm to other sub-optimal algorithms. We end with a discussion of possible extensions to coded systems and combinations with sphere decoding.
Genetic variants modify the effect of age on APOE methylation in the Genetics of Lipid Lowering Drugs and Diet Network study

Although apolipoprotein E (APOE) variants are associated with age‐related diseases, the underlying mechanism is unknown and DNA methylation may be a potential one. With methylation data, measured by the Infinium Human Methylation 450 array, from 993 participants (age ranging from 18 to 87 years) in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, and from Encyclopedia of DNA Elements (ENCODE) consortium, combined with published methylation datasets, we described the methylation pattern of 13 CpG sites within APOE locus, their correlations with gene expression across cell types, and their relationships with age, plasma lipids, and sequence variants. Based on methylation levels and the genetic regions, we categorized the 13 APOE CpG sites into three groups: Group 1 showed hypermethylation (> 50%) and were located in the promoter region, Group 2 exhibited hypomethylation (< 50%) and were located in the first two exons and introns, and Group 3 showed hypermethylation (> 50%) and were located in the exon 4. APOE methylation was negatively correlated with gene expression (minimum r = −0.66, P = 0.004). APOE methylation was significantly associated with age (minimum P = 2.06E‐08) and plasma total cholesterol (minimum P = 3.53E‐03). Finally, APOE methylation patterns differed across APOE ε variants (minimum P = 3.51E‐05) and the promoter variant rs405509 (minimum P = 0.01), which further showed a significant interaction with age (P = 0.03). These findings suggest that methylation may be a potential mechanistic explanation for APOE functions related to aging and call for further molecular mechanistic studies.
High-Throughput, Kingdom-Wide Prediction and Annotation of Bacterial Non-Coding RNAs

Background Diverse bacterial genomes encode numerous small non-coding RNAs (sRNAs) that regulate myriad biological processes. While bioinformatic algorithms have proven effective in identifying sRNA-encoding loci, the lack of tools and infrastructure with which to execute these computationally demanding algorithms has limited their utilization. Genome-wide predictions of sRNA-encoding genes have been conducted in less than 3% of all sequenced bacterial strains, leading to critical gaps in current annotations. The relative paucity of genome-wide sRNA prediction represents a critical gap in current annotations of bacterial genomes and has limited examination of larger issues in sRNA biology, such as sRNA evolution. Methodology/Principal Findings We have developed and deployed SIPHT, a high throughput computational tool that utilizes workflow management and distributed computing to effectively conduct kingdom-wide predictions and annotations of intergenic sRNA-encoding genes. Candidate sRNA-encoding loci are identified based on the presence of putative Rho-independent terminators downstream of conserved intergenic sequences, and each locus is annotated for several features, including conservation in other species, association with one of several transcription factor binding sites and homology to any of over 300 previously identified sRNAs and cis-regulatory RNA elements. Using SIPHT, we conducted searches for putative sRNA-encoding genes in all 932 bacterial replicons in the NCBI database. These searches yielded nearly 60% of previously confirmed sRNAs, hundreds of previously annotated cis-encoded regulatory RNA elements such as riboswitches, and over 45,000 novel candidate intergenic loci. Conclusions/Significance Candidate loci were identified across all branches of the bacterial evolutionary tree, suggesting a central and ubiquitous role for RNA-mediated regulation among bacterial species. Annotation of candidate loci by SIPHT provides clues into the potential biological function of thousands of previously confirmed and candidate regulatory RNAs and affords new insights into the evolution of bacterial riboregulation.
Latent Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

We present a general framework for Bayesian case-based reasoning and prototype classification and clustering — Latent Case Model (LCM). LCM learns the most representative prototype observations of a dataset by performing joint inference on cluster prototypes and features. Simultaneously, LCM pursues sparsity by learning subspaces, the sets of few features that play important roles in characterizing the prototypes. The prototype and subspace representation preserves interpretability in high dimensional data. We validate the approach preserves classification accuracy on standard data sets, and verify through human subject experiments that the output of LCM produces statistically significant improvements in participants’ performance on a task requiring an understanding of clusters within a dataset.
Triple Therapy with Scopolamine, Ondansetron, and Dexamethasone for Prevention of Postoperative Nausea and Vomiting in Moderate to High-Risk Patients Undergoing Craniotomy Under General Anesthesia: A Pilot Study

Introduction Postoperative nausea and vomiting (PONV) is one of the most common complaints from patients and clinicians after a surgical procedure. According to the current Society of Ambulatory Anesthesia Consensus Guidelines, the general incidence of vomiting and nausea is around 30 and 50%, respectively; and up to 80% in high-risk patients. In previous studies, the reported incidence of PONV at 24 h after craniotomy was 43–70%. The transdermal scopolamine (TDS) delivery system contains a 1.5-mg drug reservoir, which is designed to deliver a continuous slow release of scopolamine through intact skin during the first 72 h of patch application. Therefore, we designed this single arm, non-randomized, pilot study to assess the efficacy and safety of triple therapy with scopolamine, ondansetron, and dexamethasone to prevent PONV. Materials and methods In the preoperative area, subjects received an active TDS 1.5 mg that was applied to a hairless patch of skin in the mastoid area approximately 2 h prior to the operation. Immediately after anesthesia induction, all patients received a single 4 mg dose of ondansetron IV and a single 10 mg dose of dexamethasone IV. Patients who experienced nausea and/or vomiting received ondansetron 4 mg IV as the initial rescue medication. Postoperative nausea and vomiting assessments were performed for up to 120 h after surgery. Results A total of 36 subjects were analyzed. The overall incidence of PONV during the first 24 h after neurological surgery was 33% (n = 12). The incidence of nausea and emesis during the first 24 h after surgery was recorded as 33% (n = 12) and 16% (n = 6), respectively. Conclusion Our data showed that this triple therapy regimen may be an efficient alternative regimen for PONV prophylaxis in patients undergoing neurological surgery with general anesthesia. Further studies using regimens affecting different receptor pathways should be performed to better prove the efficacy and safety in the prevention or delay of PONV.
Opus: University of Bath Online Publication Store Ecological Bias in Studies of the Short–term Effects of Air Pollution on Health

Geoinformation. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. This version is made available in accordance with publisher policies. Please cite only the published version using the reference above. Abstract There has been a great deal of research into the short–term effects of air pollution on health with a large number of studies modelling the association between aggregate disease counts and environmental exposures measured at point locations, for example via air pollution monitors. In such cases, the standard approach is to average the observed measurements from the individual monitors and use this in a log-linear health model. Hence such studies are ecological in nature being based on spatially aggregated health and exposure data. Here we investigate the potential for bias in the estimates of the effects on health when estimating the short–term effects of air pollution on health. Such ecological bias may occur if a simple summary measure, such as a daily mean, is not a suitable summary of a spatially variable pollution surface. We assess the performance of commonly used models when confronted with such issues using simulation studies and compare their performance with a model specifically designed to acknowledge the effects of exposure aggregation. In addition to simulation studies, we apply the models to a case study of the short–term effects of particulate matter on respiratory mortality using data from Greater London for the period 2002-2005. We found a significant increased risk of 3% (95% CI 1-5%) associated with the average of the previous three days exposure to particulate matter (per 10 µgm −3 PM 10).