Miracle-dz/UniFAR-FTUs huggingface dataset:
Solution of a multi-dimensional batch crystallization model with fines dissolution using CE / SE method

This article is concerned with the numerical investigation of two-dimensional population balance models for batch crystallization processes with fines dissolution. In batch crystallization, dissolution of smaller unwanted nuclei below some critical size is of vital importance as it improves the quality of the product. The crystals growth rates for both size-independent and size-dependent cases are considered. Moreover, a delay in the recycle pipe is also included in the model. The space-time conservation element and solution element method, originally derived for non reacting flows, is used to solve the resulting model. This scheme has already been applied to a range of PDE's, mainly in the area of Fluid mechanics. The CE/SE method has been successfully applied to disciplines other than it originated from, this distinguishing feature confirms the method's robustness and generality. The numerical results in this article demonstrate the excellent performance of proposed numerical schemes in solving the current physical problem. [Noor S, Qamar S. solution of a multi-dimensional batch crystallization model with fines dissolution using CE/SE method. Life Sci J 2014;11(2s):69-78]. (ISSN:1097-8135). http://www.lifesciencesite. com. 13
Nonparametric Bayesian Clinical Trial Design for Continuous Patient Response

The evolutionary nature of sequential clinical trials encourages the use of the Bayesian paradigm in the design, monitoring and analysis of such trials. Here we focus on the design issue for the case of continuous possibly multivariate patient response. In particular we address the comparison of two treatments or arms. We take a nonparametric perspective for the likelihood speci cation by assuming that the data is drawn from a distribution which arises through Dirichlet process mixing. To address the subjectivity in the prior speci cation we can consider a range of sampling priors from skeptical to enthusiastic to capture our views. Regardless, we use a tting prior which is always vague hence skeptical to allow the data to inform about treatment di erences. Our approach is descriptive. Given a model speci cation a design is characterized by a number of interim evaluations, the group size for each interim look, and a set of stopping criteria which determine our decision at a given look. We then simulate replications of the design and use these replicates to summarize design performance in terms of when the trial was stopped and reason for stopping. We illustrate with two examples, one having univariate response, the other bivariate response.
Recovering Low-Rank and Sparse Matrices via Robust Bilateral Factorization

Recovering low-rank and sparse matrices from partial, incomplete or corrupted observations is an important problem in many areas of science and engineering. In this paper, we propose a scalable robust bilateral factorization (RBF) method to recover both structured matrices from missing and grossly corrupted data such as robust matrix completion (RMC), or incomplete and grossly corrupted measurements such as compressive principal component pursuit (CPCP). With the unified framework, we first present two robust trace norm regularized bilateral factorization models for RMC and CPCP problems, which can achieve an orthogonal dictionary and a robust data representation, simultaneously. Then, we apply the alternating direction method of multipliers to efficiently solve the RMC problems. Finally, we provide the convergence analysis of our algorithm, and extend it to address general CPCP problems. Experimental results verified both the efficiency and effectiveness of our RBF method compared with the state-of-the-art methods.
A case report of dipyridamole stress-induced ST depression progressing to ST-elevation myocardial infarction despite intravenous aminophylline: steal, spasm, or something else?

Abstract Background Dipyridamole stress is commonly used for myocardial perfusion imaging and is generally safe. Myocardial ischaemia can occasionally occur and is classically thought to be due to coronary steal as a result of redistribution of flow away from collateral dependent myocardium. Although ischaemia more commonly presents as electrocardiographic (ECG) ST depression and angina, ST-elevation myocardial infarction may occur as a very rare complication. Case summary We report a case of a patient who developed chest pain and ST depression during dipyridamole infusion. The pain persisted despite intravenous aminophylline with new inferior ST elevation soon after. Coronary angiography showed subtotal right coronary artery occlusion with no collateral supply. The symptoms and ECG changes resolved after percutaneous coronary intervention. Discussion Coronary steal may not fully account for our patient’s presentation given the failure of aminophylline and absent angiographic collaterals. Vasospasm may be triggered by dipyridamole and can directly cause ischaemia or provoke rupture of an unstable plaque. Augmentation of cardiac energetics during vasodilator stress may also play a role.
MOSAIC IMAGE INDEXING BY COLOR-BASED SEGMENTATION AND SKELETON EXTRACTION

This threatened heritage is in danger of falling to pieces. Fortunately, a huge number of photographs have been taken by lovers of this art, by professionals and by experts of Islamic arts. However, these pictures are scattered through a number of books, private collections and libraries, so that it is difficult to collect material for archeological studies. The demand for a fast and accurate access to these images by means of an image retrieval system is very strong. Fully aware of this growing demand, many owners of these valuable photographs are ready to lend them in order to build databases of mosaic color images. In this context, the retrieval of target images that are similar to a given query image has become a challenging task.
Analysis of an evolutionary reinforcement learning method in a multiagent domain

Many multiagent problems comprise subtasks which can be considered as reinforcement learning (RL) problems. In addition to classical temporal difference methods, evolutionary algorithms are among the most promising approaches for such RL problems. The relative performance of these approaches in certain subdomains (e. g. multiagent learning) of the general RL problem remains an open question at this time. In addition to theoretical analysis, benchmarks are one of the most important tools for comparing different RL methods in certain problem domains. A recently proposed multiagent RL benchmark problem is the RoboCup Keepaway benchmark. This benchmark is one of the most challenging multiagent learning problems because its state-space is continuous and high dimensional, and both the sensors and the actuators are noisy. In this paper we analyze the performance of the neuroevolutionary approach called Evolutionary Acquisition of Neural Topologies (EANT) in the Keepaway benchmark, and compare the results obtained using EANT with the results of other algorithms tested on the same benchmark.
PLUMMER : An Almost Linear Approach to Multiple Whole Genome Alignment

Following advances in biotechnology, many new whole genome sequences are becoming available every year. A lot of useful information can be derived from the alignment and comparison of different genomes. However, most of the current research focuses on pairwise genome alignment, and few available applications can efficiently align multiple genomes. In this paper, we present an optimal approach to multiple whole genome alignment. Our algorithm aligns closely related multiple genomes to find their conserved regions. We present two approaches to align whole genomes. In first approach, called Direct Matching (DM), multiple whole genomes are aligned with their DNA sequences. However, because most parts of prokaryotic genomes are encoded regions, we introduce second method, Functional Matching (FM), to especially align multiple prokaryotic genomes. We present graph theoretic argumentation to prove that our approach has almost linear complexity. In addition, analyses of experimental results for both methods are presented. It may be noted that the FM method generates a greater number of and longer conserved regions than the DM method and thus conveys detailed and accurate information about the conservation and inheritance of genomes.
Microglia derived from aging mice exhibit an altered inflammatory profile

Microglia play a critical role in neurodegenerative diseases and in the brain aging process. Yet, little is known about the functional dynamics of microglia during aging. Thus, using young and aging transgenic mice expressing enhanced‐green fluorescent protein (EGFP) under the promoter of the c‐fms gene for macrophage‐colony stimulating factor receptor, we evaluated invivo‐induced inflammatory responses of EGFP‐expressing microglia sorted by flow cytometry. Aging microglia were characterized by the presence of lipofuscin granules, decreased processes complexity, altered granularity, and increased mRNA expression of both pro‐inflammatory (TNFα, IL‐1β, IL‐6) and anti‐inflammatory (IL‐10, TGFβ1) cytokines. Following lipopolysaccharide (LPS) challenge (1 mg/kg, 3 h), aging microglia exhibit increased basal expression of TNFα, IL‐1β, IL‐6, and IL‐10. Yet, the fold‐over‐basal LPS response remained constant across age, implying that the inflammatory machinery in aging microglia is functional and adjusted to the basal state. Gender differences were not overall observed across the treatments (age, LPS). The low but sustained production of pro‐inflammatory cytokines by aging microglia may have a profound impact in the brain aging process. © 2007 Wiley‐Liss, Inc.
Using Regulatory and Epistatic Networks to Extend the Findings of a Genome Scan: Identifying the Gene Drivers of Pigmentation in Merino Sheep

Extending genome wide association analysis by the inclusion of gene expression data may assist in the dissection of complex traits. We examined piebald, a pigmentation phenotype in both human and Merino sheep, by analysing multiple data types using a systems approach. First, a case control analysis of 49,034 ovine SNP was performed which confirmed a multigenic basis for the condition. We combined these results with gene expression data from five tissue types analysed with a skin-specific microarray. Promoter sequence analysis of differentially expressed genes allowed us to reverse-engineer a regulatory network. Likewise, by testing two-loci models derived from all pair-wise comparisons across piebald-associated SNP, we generated an epistatic network. At the intersection of both networks, we identified thirteen genes with insulin-like growth factor binding protein 7 (IGFBP7), platelet-derived growth factor alpha (PDGFRA) and the tetraspanin platelet activator CD9 at the kernel of the intersection. Further, we report a number of differentially expressed genes in regions containing highly associated SNP including ATRN, DOCK7, FGFR1OP, GLI3, SILV and TBX15. The application of network theory facilitated co-analysis of genetic variation with gene expression, recapitulated aspects of the known molecular biology of skin pigmentation and provided insights into the transcription regulation and epistatic interactions involved in piebald Merino sheep.
Automated cell phenotype image classification combining different methods.

In this paper our aim is to study how an ensemble of classifiers can improve the performance of a machine learning technique for cell phenotype image classification. We want to point out some of the advantages that an ensemble of classifiers permits to obtain respect a stand-alone method. Finally, the preliminary results on the 2D-HeLa dataset, obtained by the fusion between a random subspace of Levenberg-Marquardt neural networks and a variant of the AdaBoost, are reported. It is interesting to note that the proposed system obtains an outstanding 97.5% Rank-1 accuracy and a >99% Rank-2 accuracy.
Spectral statistics for unitary transfer matrices of binary graphs

Quantum graphs have recently been introduced as model systems to study the spectral statistics of linear wave problems with chaotic classical limits. It is proposed here to generalize this approach by considering arbitrary, directed graphs with unitary transfer matrices. An exponentially increasing contribution to the form factor is identified when performing a diagonal summation over periodic orbit degeneracy classes. A special class of graphs, so-called binary graphs, is studied in more detail. For these, the conditions for periodic orbit pairs to be correlated (including correlations due to the unitarity of the transfer matrix) can be given explicitly. Using combinatorial techniques it is possible to perform the summation over correlated periodic orbit pair contributions to the form factor for some low-dimensional cases. Gradual convergence towards random matrix results is observed when increasing the number of vertices of the binary graphs.
The security of practical quantum key distribution

Quantum key distribution (QKD) is the first quantum information task to reach the level of mature technology, already fit for commercialization. It aims at the creation of a secret key between authorized partners connected by a quantum channel and a classical authenticated channel. The security of the key can in principle be guaranteed without putting any restriction on an eavesdropper's power. This article provides a concise up-to-date review of QKD, biased toward the practical side. Essential theoretical tools that have been developed to assess the security of the main experimental platforms are presented (discrete-variable, continuous-variable, and distributed-phase-reference protocols).
28 frames later: predicting screen touches from back-of-device grip changes

We demonstrate that front-of-screen targeting on mobile phones can be predicted from back-of-device grip manipulations. Using simple, low-resolution capacitive touch sensors placed around a standard phone, we outline a machine learning approach to modelling the grip modulation and inferring front-of-screen touch targets. We experimentally demonstrate that grip is a remarkably good predictor of touch, and we can predict touch position 200ms before contact with an accuracy of 18mm.
A comprehensive analysis of cancer-driving mutations and genes in kidney cancer

An accumulation of driver mutations is important for cancer formation and progression, and leads to the disruption of genes and signaling pathways. The identification of driver mutations and genes has been the subject of numerous previous studies. The present study was performed to identify cancer-driving mutations and genes in renal cell carcinoma (RCC), prioritizing noncoding variants with a high functional impact, in order to analyze the most informative features. Sorting Intolerant From Tolerant (SIFT), Polymorphism Phenotyping version 2 (Polyphen2) and MutationAssessor were applied to predict deleterious mutations in the coding genome. OncodriveFM and OncodriveCLUST were used to detect potential driver genes and signaling pathways. The functional impact of noncoding variants was evaluated using Combined Annotation Dependent Depletion, FunSeq2 and Genome-Wide Annotation of Variants. Noncoding features were analyzed with respect to their enrichment of high-scoring variants. A total of 1,327 coding mutations in clear cell RCC, 258 in chromophobe RCC and 1,186 in papillary RCC were predicted to be deleterious by all three of MutationAssessor, Polyphen2 and SIFT. In total, 77 genes were positively selected by OncodriveFM and 1 by OncodriveCLUST, 45 of which were recurrently mutated genes. In addition, 10 signaling pathways were recurrently mutated and had a high functional impact bias (FM bias), and 31 novel signaling pathways with high FM bias were identified. Furthermore, noncoding regulatory features and conserved regions contained numerous high-scoring variants, and expression, replication time, GC content and recombination rate were positively correlated with the densities of high-scoring variants. In conclusion, the present study identified a list of cancer-driving genes and signaling pathways, features like regulatory elements, conserved regions, replication time, expression, GC content and recombination rate are major factors that affect the distribution of high-scoring non-coding mutations in kidney cancer.
Contact and conflict between adult children and their parents in immigrant families: is integration problematic for family relationships?

ABSTRACT Previous studies have shown that family ties are relatively strong in most non-western immigrant groups in Europe. This paper focuses on differences within the immigrant population and examines how cultural and social aspects of integration affect the relationships that adult children have with their parents. The study is based on survey data with a systematic oversample of persons aged 15–45 with Moroccan and Turkish origins in the Netherlands. The focus is on the amount of contact and conflict that children have with their parents. Findings show that persons of Moroccan and Turkish origins have more frequent contact, but also somewhat more conflict with their parents compared to people without any migration background. Ordinal logit models show that ties to parents are weaker when immigrant children are more liberal in their values and behaviours and when they have more frequent contact with natives. Educational attainment tends to increase conflict between parents and adult children. It is concluded that cultural and social integration may hurt family relationships, pointing to another but less often recognised obstacle for immigrant integration in the west.
Project ( DRP ) Report : Ontology-based Data Integration and Modeling

Project Summary This report will cover the evolution of four projects centering around the theme of Ontology-based Data Integration and Modeling. Purpose The purpose of this report is to demonstrate that the author shows the promise of a successful researcher and future Ph.D. candidate, one able to decompose large problems into manageable parts, synthesize prior work, make crucial insights, critically analyze results, and effectively share findings with the greater research community. Deliverables As mentioned, this report will cover four different projects on using an ontology-based approach to integrate data in a way that formally preserves semantics between resources. A significant body of database research is devoted to the problem of information mapping and integration, but little progress has been made in that community toward formally addressing semantics. On the other hand, the Semantic Web community derives its strength from logic, representation and reasoning systems. Our work attempts to bridge this gap and balance the power of reasoning systems with the efficiency and scalability of databases. Each project develops incrementally in a bottom-up fashion toward a viable framework for addressing this difficult problem. Three of these have resulted in publications so far, the fourth will be submitted to a leading conference upon final implementation of the algorithms described later in this report. The following publications with bibliographies constitute the bulk of this report: The fourth and final project presented as part of this DRP will develop the idea of an ontology-based approach for data modeling. Based on insights from the above works, we explore a method for automatically transforming relational database schema definitions to ontologies and vice versa. Although there is a significant body of work in model-management (e.g., XML-Schema, RDF, EER and OODB model transformations), we believe that ontology-based data modeling presents some new opportunities for innovation – especially when it comes to bridging axioms. This work is in its early stages. The hope is that ontology-based modeling will be the first step toward our idea of true model-independence in a semantic integration framework. In this report, some background will be presented followed by a discussion of each project in chronological order. At each stage in the development of these works, open questions and new ideas generated from one project lead into the next one, building up to the goal of achieving a deeper understanding of and steps toward a solution for the problem of integrating data in a heterogeneous …
The Relationship between English Language Proficiency and Academic Achievement for English Language Learners

The purpose of this quantitative correlational research study was to test the threshold hypothesis as it applied to the performance of English language learner students on the Oklahoma Core Curriculum Test (OCCT) in third grade reading and the Assessing Comprehension and Communication in English State to State for English language learners assessment, also known as the ACCESS for ELLs. The OCCT assesses student performance on third grade reading content and the ACCESS for ELLs assesses the English language proficiency levels in speaking, listening, reading, and writing. This study specifically proposed to analyze the relationship between the ACCESS for ELLs reading proficiency level and the student performance on the OCCT third grade reading assessment. Additional analysis was proposed to determine if the relationship in student performance on the assessments was influenced by the number of years in which the student had been receiving English language development services. The participants in the study were third grade English language learner students enrolled in a large school district in Oklahoma for the 2014-2015 school year.
Context-based image modelling

In this work we address the task of adapting the model representation of a given image, in the context of a second target image model. We present the BlobEMD framework, in which the images are represented as sets of blobs, and optimal correspondences are found between the representations of the images and are used to adapt the representation of the source image to that of the target image. The context-based model adaptation allows for similarity measures between images that are insensitive to the segmentation process and different levels of details of the representation. We show applications for matching models of heavily dithered images with models of full resolution images, and for content-based image segmentation where the transition from regions to representative silhouettes is shown.
Flavonoid metabolites reduce tumor necrosis factor‐α secretion to a greater extent than their precursor compounds in human THP‐1 monocytes

1 Scope Flavonoids are generally studied in vitro, in isolation, and as unmetabolized precursor structures. However, in the habitual diet, multiple flavonoids are consumed together and found present in the circulation as complex mixtures of metabolites. Using a unique study design, we investigated the potential for singular or additive anti‐inflammatory effects of flavonoid metabolites relative to their precursor structures.2 Methods and results Six flavonoids, 14 flavonoid metabolites, and 29 combinations of flavonoids and their metabolites (0.1–10 μM) were screened for their ability to reduce LPS‐induced tumor necrosis factor‐α (TNF‐α) secretion in THP‐1 monocytes. One micromolar peonidin‐3‐glucoside, cyanidin‐3‐glucoside, and the metabolites isovanillic acid (IVA), IVA‐glucuronide, vanillic acid‐glucuronide, protocatechuic acid‐3‐sulfate, and benzoic acid‐sulfate significantly reduced TNF‐α secretion when in isolation, while there was no effect on TNF‐α mRNA expression. Four combinations of metabolites that included 4‐hydroxybenzoic acid (4HBA) and/or protocatechuic acid also significantly reduced TNF‐α secretion to a greater extent than the precursors or metabolites alone. The effects on LPS‐induced IL‐1β and IL‐10 secretion and mRNA expression were also examined. 4HBA significantly reduced IL‐1β secretion but none of the flavonoids or metabolites significantly modified IL‐10 secretion.3 Conclusion This study provides novel evidence suggesting flavonoid bioactivity results from cumulative or additive effects of circulating metabolites.